Simple Classification Model for Diabetes Prediction Using R
David Lowe
April 6, 2018
Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery (http://machinelearningmastery.com/)
Dataset Used: Pima Indians Diabetes Database Data Set ML Model: Classification with numerical attributes Dataset Reference: https://www.kaggle.com/uciml/pima-indians-diabetes-database
For more information on this case study project, please consult Dr. Brownlee’s blog post at https://machinelearningmastery.com/standard-machine-learning-datasets/.
For more information on performance benchmarks, please consult: https://www.kaggle.com/uciml/pima-indians-diabetes-database
INTRODUCTION: The Pima Indians Diabetes Dataset involves predicting the onset of diabetes within 5 years in Pima Indians given medical details. It is a binary (2-class) classification problem. There are 768 observations with 8 input variables and 1 output variable. Missing values are believed to be encoded with zero values.
CONCLUSION: The baseline performance of predicting the class variable achieved an average accuracy of 75.85%. The top accuracy result achieved via Logistic Regression was 77.73% after a series of tuning trials. The ensemble algorithms, in this case, did not yield a better result than the non-ensemble algorithms to justify the additional processing required.
The purpose of this project is to analyze a dataset using various machine learning algorithms and to document the steps using a template. The project aims to touch on the following areas:
- Document a predictive modeling problem end-to-end.
- Explore data transformation options for improving model performance
- Explore non-ensemble and ensemble algorithms for improving model performance
- Explore algorithm tuning techniques for improving model performance
Working through machine learning problems from end-to-end requires a structured modeling approach. Working problems through a project template can encourage you to think about the problem more critically, to challenge your assumptions, and to get good at all parts of a modeling project.
Any predictive modeling machine learning project can be broken down into about 6 common tasks:
- Define Problem
- Summarize Data
- Prepare Data
- Evaluate Algorithms
- Improve Accuracy or Results
- Finalize Model and Present Results
1. Prepare Problem
1.a) Load libraries
library(mlbench)
library(caret)## Warning: package 'caret' was built under R version 3.4.4## Loading required package: lattice## Loading required package: ggplot2library(corrplot)## corrplot 0.84 loaded1.b) Load dataset
seedNum <- 888
data(PimaIndiansDiabetes)
dataset <- PimaIndiansDiabetes
# Rename the class column to a standard label
colnames(dataset)[9] <- "classVar"1.c) Split-out validation dataset
Normally, we would create a training (variable name “dataset”) and a validation (variable name “validation”) dataset. Because this dataset is relatively small, we will opt to test the algorithms with the full set of data and not splitting.
# Not applicable for this iteration of the project.2. Summarize Data
To gain a better understanding of the data that we have on-hand, we will leverage a number of descriptive statistics and data visualization techniques. The plan is to use the results to consider new questions, review assumptions, and validate hypotheses that we can investigate later with specialized models.
2.a) Descriptive statistics
2.a.i) Dimensions of the dataset.
dim(dataset)## [1] 768 92.a.ii) Types of the attributes.
sapply(dataset, class)## pregnant glucose pressure triceps insulin mass pedigree
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
## age classVar
## "numeric" "factor"2.a.iii) Peek at the data itself.
head(dataset)## pregnant glucose pressure triceps insulin mass pedigree age classVar
## 1 6 148 72 35 0 33.6 0.627 50 pos
## 2 1 85 66 29 0 26.6 0.351 31 neg
## 3 8 183 64 0 0 23.3 0.672 32 pos
## 4 1 89 66 23 94 28.1 0.167 21 neg
## 5 0 137 40 35 168 43.1 2.288 33 pos
## 6 5 116 74 0 0 25.6 0.201 30 neg2.a.iv) Summarize the levels of the class attribute.
x <- dataset[,1:8]
y <- dataset[,9]
cbind(freq=table(y), percentage=prop.table(table(y))*100)## freq percentage
## neg 500 65.10417
## pos 268 34.895832.a.v) Summarize correlations between input variables.
cor(x)## pregnant glucose pressure triceps insulin
## pregnant 1.00000000 0.12945867 0.14128198 -0.08167177 -0.07353461
## glucose 0.12945867 1.00000000 0.15258959 0.05732789 0.33135711
## pressure 0.14128198 0.15258959 1.00000000 0.20737054 0.08893338
## triceps -0.08167177 0.05732789 0.20737054 1.00000000 0.43678257
## insulin -0.07353461 0.33135711 0.08893338 0.43678257 1.00000000
## mass 0.01768309 0.22107107 0.28180529 0.39257320 0.19785906
## pedigree -0.03352267 0.13733730 0.04126495 0.18392757 0.18507093
## age 0.54434123 0.26351432 0.23952795 -0.11397026 -0.04216295
## mass pedigree age
## pregnant 0.01768309 -0.03352267 0.54434123
## glucose 0.22107107 0.13733730 0.26351432
## pressure 0.28180529 0.04126495 0.23952795
## triceps 0.39257320 0.18392757 -0.11397026
## insulin 0.19785906 0.18507093 -0.04216295
## mass 1.00000000 0.14064695 0.03624187
## pedigree 0.14064695 1.00000000 0.03356131
## age 0.03624187 0.03356131 1.000000002.a.vi) Statistical summary of all attributes.
summary(dataset)## pregnant glucose pressure triceps
## Min. : 0.000 Min. : 0.0 Min. : 0.00 Min. : 0.00
## 1st Qu.: 1.000 1st Qu.: 99.0 1st Qu.: 62.00 1st Qu.: 0.00
## Median : 3.000 Median :117.0 Median : 72.00 Median :23.00
## Mean : 3.845 Mean :120.9 Mean : 69.11 Mean :20.54
## 3rd Qu.: 6.000 3rd Qu.:140.2 3rd Qu.: 80.00 3rd Qu.:32.00
## Max. :17.000 Max. :199.0 Max. :122.00 Max. :99.00
## insulin mass pedigree age
## Min. : 0.0 Min. : 0.00 Min. :0.0780 Min. :21.00
## 1st Qu.: 0.0 1st Qu.:27.30 1st Qu.:0.2437 1st Qu.:24.00
## Median : 30.5 Median :32.00 Median :0.3725 Median :29.00
## Mean : 79.8 Mean :31.99 Mean :0.4719 Mean :33.24
## 3rd Qu.:127.2 3rd Qu.:36.60 3rd Qu.:0.6262 3rd Qu.:41.00
## Max. :846.0 Max. :67.10 Max. :2.4200 Max. :81.00
## classVar
## neg:500
## pos:268
##
##
##
## 2.b) Data visualizations
2.b.i) Univariate plots to better understand each attribute.
# boxplots for each attribute
par(mfrow=c(2,4))
for(i in 1:8) {
boxplot(dataset[,i], main=names(dataset)[i])
}
# histograms each attribute
par(mfrow=c(2,4))
for(i in 1:8) {
hist(dataset[,i], main=names(dataset)[i])
}
# density plot for each attribute
par(mfrow=c(2,4))
for(i in 1:8) {
plot(density(dataset[,i]), main=names(dataset)[i])
}
2.b.ii) Multivariate plots to better understand the relationships between attributes
# scatterplot matrix colored by class
pairs(classVar~., data=dataset, col=dataset$classVar)
# box and whisker plots for each attribute by class
scales <- list(x=list(relation="free"), y=list(relation="free"))
featurePlot(x=x, y=y, plot="box", scales=scales)
# density plots for each attribute by class value
featurePlot(x=x, y=y, plot="density", scales=scales)
# correlation plot
correlations <- cor(x)
corrplot(correlations, method="circle")
3. Prepare Data
Some dataset may require additional preparation activities that will best exposes the structure of the problem and the relationships between the input attributes and the output variable. Some data-prep tasks might include:
- Cleaning data by removing duplicates, marking missing values and even imputing missing values.
- Feature selection where redundant features may be removed.
- Data transforms where attributes are scaled or redistributed in order to best expose the structure of the problem later to learning algorithms.
3.a) Data Cleaning
# Not applicable for this iteration of the project.
# Mark missing values
# invalid <- 0
# dataset$pressure[dataset$pressure==invalid] <- NA
# dataset$mass[dataset$mass==invalid] <- NA
# dataset$glucose[dataset$glucose==invalid] <- NA
# # Impute missing values
# dataset$pressure <- with(dataset, impute(pressure, mean))
# dataset$mass <- with(dataset, impute(mass, mean))
# dataset$glucose <- with(dataset, impute(glucose, mean))3.b) Feature Selection
# Not applicable for this iteration of the project.3.c) Data Transforms
# Not applicable for this iteration of the project.4. Evaluate Algorithms
After the data-prep, we next work on finding a workable model by evaluating a subset of machine learning algorithms that are good at exploiting the structure of the dataset. The typical evaluation tasks include:
- Defining test options such as cross validation and the evaluation metric to use.
- Spot checking a suite of linear and nonlinear machine learning algorithms.
- Comparing the estimated accuracy of algorithms.
For this project, we will evaluate four non-ensemble and three ensemble algorithms:
Non-Ensemble Algorithms: Logistic Regression, Linear Discriminant Analysis, Decision Trees (CART), Naive Bayes, k-Nearest Neighbors, and Support Vector Machine
Ensemble Algorithms: Bagged CART, Random Forest, Adaboost, Stochastic Gradient Boosting, and Neural Networks
The random number seed is reset before each run to ensure that the evaluation of each algorithm is performed using the same data splits. It ensures the results are directly comparable.
4.a) Test options and evaluation metric
# Run algorithms using 10-fold cross validation
control <- trainControl(method="repeatedcv", number=10, repeats=3)
metricTarget <- "Accuracy"4.b) Generate Models using Non-Ensemble Algorithms
# Linear Regression (Regression)
# Algorithm not applicable for this project
#set.seed(seedNum)
#fit.lm <- train(classVar~., data=dataset, method="lm", metric=metricTarget, trControl=control)# Logistic Regression (Regression/Classification)
set.seed(seedNum)
fit.glm <- train(classVar~., data=dataset, method="glm", metric=metricTarget, trControl=control)# Linear/Quadratic Discriminant Analysis (Regression/Classification)
set.seed(seedNum)
fit.lda <- train(classVar~., data=dataset, method="lda", metric=metricTarget, trControl=control)# Decision Tree - CART (Regression/Classification)
set.seed(seedNum)
fit.cart <- train(classVar~., data=dataset, method="rpart", metric=metricTarget, trControl=control)# Naive Bayes (Classification)
set.seed(seedNum)
fit.nb <- train(classVar~., data=dataset, method="nb", metric=metricTarget, trControl=control)## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
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## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77# k-Nearest Neighbors (Regression/Classification)
set.seed(seedNum)
fit.knn <- train(classVar~., data=dataset, method="knn", metric=metricTarget, trControl=control)# Support Vector Machine (Classification)
set.seed(seedNum)
fit.svm <- train(classVar~., data=dataset, method="svmRadial", metric=metricTarget, trControl=control)4.C) Generate Models using Ensemble Algorithms
In this section, we will explore the use and tuning of ensemble algorithms to see whether we can improve the results.
# Bagged CART (Regression/Classification)
set.seed(seedNum)
fit.bagcart <- train(classVar~., data=dataset, method="treebag", metric=metricTarget, trControl=control)# Random Forest (Regression/Classification)
set.seed(seedNum)
fit.rf <- train(classVar~., data=dataset, method="rf", metric=metricTarget, trControl=control)# AdaBoost (Classification)
set.seed(seedNum)
fit.ada <- train(classVar~., data=dataset, method="adaboost", metric=metricTarget, trControl=control)# Stochastic Gradient Boosting (Regression/Classification)
set.seed(seedNum)
fit.gbm <- train(classVar~., data=dataset, method="gbm", metric=metricTarget, trControl=control)## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2581 nan 0.1000 0.0172
## 2 1.2321 nan 0.1000 0.0124
## 3 1.2023 nan 0.1000 0.0127
## 4 1.1823 nan 0.1000 0.0065
## 5 1.1643 nan 0.1000 0.0073
## 6 1.1462 nan 0.1000 0.0086
## 7 1.1306 nan 0.1000 0.0065
## 8 1.1186 nan 0.1000 0.0048
## 9 1.1019 nan 0.1000 0.0071
## 10 1.0876 nan 0.1000 0.0056
## 20 0.9970 nan 0.1000 0.0020
## 40 0.9149 nan 0.1000 0.0001
## 60 0.8730 nan 0.1000 -0.0007
## 80 0.8509 nan 0.1000 -0.0013
## 100 0.8340 nan 0.1000 -0.0004
## 120 0.8213 nan 0.1000 -0.0010
## 140 0.8106 nan 0.1000 -0.0007
## 150 0.8061 nan 0.1000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2494 nan 0.1000 0.0213
## 2 1.2109 nan 0.1000 0.0177
## 3 1.1751 nan 0.1000 0.0159
## 4 1.1486 nan 0.1000 0.0076
## 5 1.1177 nan 0.1000 0.0116
## 6 1.0928 nan 0.1000 0.0090
## 7 1.0722 nan 0.1000 0.0059
## 8 1.0557 nan 0.1000 0.0061
## 9 1.0408 nan 0.1000 0.0057
## 10 1.0272 nan 0.1000 0.0044
## 20 0.9292 nan 0.1000 0.0002
## 40 0.8541 nan 0.1000 -0.0006
## 60 0.8189 nan 0.1000 -0.0023
## 80 0.7855 nan 0.1000 -0.0009
## 100 0.7579 nan 0.1000 -0.0030
## 120 0.7368 nan 0.1000 -0.0012
## 140 0.7188 nan 0.1000 -0.0008
## 150 0.7122 nan 0.1000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2420 nan 0.1000 0.0225
## 2 1.1925 nan 0.1000 0.0198
## 3 1.1573 nan 0.1000 0.0170
## 4 1.1247 nan 0.1000 0.0132
## 5 1.0941 nan 0.1000 0.0133
## 6 1.0679 nan 0.1000 0.0083
## 7 1.0437 nan 0.1000 0.0087
## 8 1.0244 nan 0.1000 0.0093
## 9 1.0066 nan 0.1000 0.0054
## 10 0.9889 nan 0.1000 0.0052
## 20 0.8968 nan 0.1000 -0.0009
## 40 0.8101 nan 0.1000 -0.0019
## 60 0.7625 nan 0.1000 -0.0019
## 80 0.7237 nan 0.1000 -0.0022
## 100 0.6885 nan 0.1000 -0.0015
## 120 0.6635 nan 0.1000 -0.0021
## 140 0.6313 nan 0.1000 -0.0020
## 150 0.6169 nan 0.1000 -0.0015
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2579 nan 0.1000 0.0168
## 2 1.2242 nan 0.1000 0.0145
## 3 1.1989 nan 0.1000 0.0110
## 4 1.1813 nan 0.1000 0.0080
## 5 1.1628 nan 0.1000 0.0068
## 6 1.1420 nan 0.1000 0.0076
## 7 1.1233 nan 0.1000 0.0092
## 8 1.1107 nan 0.1000 0.0057
## 9 1.0988 nan 0.1000 0.0053
## 10 1.0865 nan 0.1000 0.0049
## 20 0.9945 nan 0.1000 0.0025
## 40 0.9182 nan 0.1000 -0.0009
## 60 0.8761 nan 0.1000 0.0001
## 80 0.8543 nan 0.1000 0.0001
## 100 0.8391 nan 0.1000 -0.0014
## 120 0.8277 nan 0.1000 -0.0007
## 140 0.8155 nan 0.1000 -0.0011
## 150 0.8121 nan 0.1000 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2386 nan 0.1000 0.0193
## 2 1.1956 nan 0.1000 0.0165
## 3 1.1644 nan 0.1000 0.0156
## 4 1.1370 nan 0.1000 0.0122
## 5 1.1127 nan 0.1000 0.0113
## 6 1.0927 nan 0.1000 0.0082
## 7 1.0749 nan 0.1000 0.0080
## 8 1.0603 nan 0.1000 0.0048
## 9 1.0428 nan 0.1000 0.0059
## 10 1.0292 nan 0.1000 0.0049
## 20 0.9292 nan 0.1000 0.0032
## 40 0.8524 nan 0.1000 -0.0011
## 60 0.8075 nan 0.1000 -0.0008
## 80 0.7828 nan 0.1000 -0.0013
## 100 0.7599 nan 0.1000 -0.0003
## 120 0.7398 nan 0.1000 -0.0003
## 140 0.7217 nan 0.1000 -0.0015
## 150 0.7112 nan 0.1000 -0.0015
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2416 nan 0.1000 0.0253
## 2 1.1932 nan 0.1000 0.0202
## 3 1.1527 nan 0.1000 0.0141
## 4 1.1236 nan 0.1000 0.0098
## 5 1.0938 nan 0.1000 0.0132
## 6 1.0687 nan 0.1000 0.0087
## 7 1.0474 nan 0.1000 0.0070
## 8 1.0269 nan 0.1000 0.0081
## 9 1.0082 nan 0.1000 0.0048
## 10 0.9946 nan 0.1000 0.0029
## 20 0.8936 nan 0.1000 0.0017
## 40 0.8087 nan 0.1000 0.0004
## 60 0.7596 nan 0.1000 -0.0010
## 80 0.7219 nan 0.1000 -0.0023
## 100 0.6832 nan 0.1000 -0.0009
## 120 0.6545 nan 0.1000 -0.0014
## 140 0.6284 nan 0.1000 -0.0023
## 150 0.6152 nan 0.1000 -0.0013
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2601 nan 0.1000 0.0137
## 2 1.2322 nan 0.1000 0.0134
## 3 1.2090 nan 0.1000 0.0104
## 4 1.1854 nan 0.1000 0.0075
## 5 1.1684 nan 0.1000 0.0060
## 6 1.1500 nan 0.1000 0.0084
## 7 1.1371 nan 0.1000 0.0053
## 8 1.1206 nan 0.1000 0.0065
## 9 1.1109 nan 0.1000 0.0026
## 10 1.0981 nan 0.1000 0.0060
## 20 1.0015 nan 0.1000 0.0018
## 40 0.9197 nan 0.1000 0.0001
## 60 0.8822 nan 0.1000 0.0002
## 80 0.8573 nan 0.1000 -0.0007
## 100 0.8409 nan 0.1000 -0.0009
## 120 0.8239 nan 0.1000 -0.0009
## 140 0.8121 nan 0.1000 -0.0011
## 150 0.8085 nan 0.1000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2469 nan 0.1000 0.0194
## 2 1.2043 nan 0.1000 0.0193
## 3 1.1739 nan 0.1000 0.0138
## 4 1.1437 nan 0.1000 0.0124
## 5 1.1256 nan 0.1000 0.0082
## 6 1.1026 nan 0.1000 0.0082
## 7 1.0814 nan 0.1000 0.0076
## 8 1.0619 nan 0.1000 0.0101
## 9 1.0453 nan 0.1000 0.0039
## 10 1.0260 nan 0.1000 0.0046
## 20 0.9262 nan 0.1000 0.0024
## 40 0.8472 nan 0.1000 -0.0013
## 60 0.8031 nan 0.1000 -0.0008
## 80 0.7761 nan 0.1000 -0.0016
## 100 0.7520 nan 0.1000 -0.0007
## 120 0.7294 nan 0.1000 -0.0003
## 140 0.7078 nan 0.1000 -0.0006
## 150 0.6989 nan 0.1000 -0.0015
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2408 nan 0.1000 0.0207
## 2 1.1956 nan 0.1000 0.0212
## 3 1.1551 nan 0.1000 0.0169
## 4 1.1240 nan 0.1000 0.0127
## 5 1.0981 nan 0.1000 0.0103
## 6 1.0745 nan 0.1000 0.0085
## 7 1.0551 nan 0.1000 0.0055
## 8 1.0379 nan 0.1000 0.0056
## 9 1.0222 nan 0.1000 0.0057
## 10 1.0026 nan 0.1000 0.0065
## 20 0.8970 nan 0.1000 0.0018
## 40 0.8073 nan 0.1000 -0.0026
## 60 0.7525 nan 0.1000 -0.0014
## 80 0.7119 nan 0.1000 0.0002
## 100 0.6749 nan 0.1000 -0.0023
## 120 0.6440 nan 0.1000 -0.0013
## 140 0.6180 nan 0.1000 -0.0029
## 150 0.6040 nan 0.1000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2559 nan 0.1000 0.0176
## 2 1.2250 nan 0.1000 0.0147
## 3 1.1979 nan 0.1000 0.0113
## 4 1.1779 nan 0.1000 0.0093
## 5 1.1603 nan 0.1000 0.0079
## 6 1.1424 nan 0.1000 0.0058
## 7 1.1290 nan 0.1000 0.0045
## 8 1.1159 nan 0.1000 0.0052
## 9 1.0995 nan 0.1000 0.0057
## 10 1.0889 nan 0.1000 0.0046
## 20 1.0022 nan 0.1000 0.0025
## 40 0.9286 nan 0.1000 0.0003
## 60 0.8892 nan 0.1000 -0.0011
## 80 0.8660 nan 0.1000 0.0002
## 100 0.8523 nan 0.1000 -0.0002
## 120 0.8394 nan 0.1000 -0.0004
## 140 0.8291 nan 0.1000 -0.0022
## 150 0.8250 nan 0.1000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2433 nan 0.1000 0.0212
## 2 1.2033 nan 0.1000 0.0174
## 3 1.1677 nan 0.1000 0.0163
## 4 1.1430 nan 0.1000 0.0093
## 5 1.1221 nan 0.1000 0.0066
## 6 1.0996 nan 0.1000 0.0098
## 7 1.0826 nan 0.1000 0.0055
## 8 1.0667 nan 0.1000 0.0062
## 9 1.0521 nan 0.1000 0.0042
## 10 1.0338 nan 0.1000 0.0067
## 20 0.9392 nan 0.1000 0.0012
## 40 0.8617 nan 0.1000 -0.0002
## 60 0.8210 nan 0.1000 -0.0011
## 80 0.7911 nan 0.1000 -0.0005
## 100 0.7649 nan 0.1000 -0.0018
## 120 0.7444 nan 0.1000 -0.0009
## 140 0.7204 nan 0.1000 -0.0019
## 150 0.7098 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2384 nan 0.1000 0.0217
## 2 1.1937 nan 0.1000 0.0194
## 3 1.1542 nan 0.1000 0.0177
## 4 1.1259 nan 0.1000 0.0117
## 5 1.0963 nan 0.1000 0.0103
## 6 1.0706 nan 0.1000 0.0101
## 7 1.0456 nan 0.1000 0.0086
## 8 1.0264 nan 0.1000 0.0067
## 9 1.0088 nan 0.1000 0.0064
## 10 0.9948 nan 0.1000 0.0045
## 20 0.9041 nan 0.1000 0.0019
## 40 0.8190 nan 0.1000 -0.0022
## 60 0.7735 nan 0.1000 -0.0019
## 80 0.7375 nan 0.1000 -0.0022
## 100 0.7050 nan 0.1000 -0.0024
## 120 0.6681 nan 0.1000 -0.0020
## 140 0.6429 nan 0.1000 -0.0013
## 150 0.6270 nan 0.1000 -0.0020
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2551 nan 0.1000 0.0152
## 2 1.2249 nan 0.1000 0.0138
## 3 1.2001 nan 0.1000 0.0119
## 4 1.1800 nan 0.1000 0.0081
## 5 1.1639 nan 0.1000 0.0067
## 6 1.1468 nan 0.1000 0.0067
## 7 1.1333 nan 0.1000 0.0065
## 8 1.1226 nan 0.1000 0.0047
## 9 1.1092 nan 0.1000 0.0046
## 10 1.0969 nan 0.1000 0.0045
## 20 1.0135 nan 0.1000 0.0008
## 40 0.9291 nan 0.1000 0.0008
## 60 0.8909 nan 0.1000 0.0005
## 80 0.8681 nan 0.1000 0.0005
## 100 0.8506 nan 0.1000 -0.0014
## 120 0.8382 nan 0.1000 -0.0012
## 140 0.8258 nan 0.1000 -0.0011
## 150 0.8210 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2561 nan 0.1000 0.0185
## 2 1.2167 nan 0.1000 0.0170
## 3 1.1805 nan 0.1000 0.0165
## 4 1.1498 nan 0.1000 0.0115
## 5 1.1265 nan 0.1000 0.0106
## 6 1.1051 nan 0.1000 0.0081
## 7 1.0864 nan 0.1000 0.0075
## 8 1.0683 nan 0.1000 0.0049
## 9 1.0549 nan 0.1000 0.0045
## 10 1.0419 nan 0.1000 0.0044
## 20 0.9490 nan 0.1000 0.0018
## 40 0.8657 nan 0.1000 -0.0016
## 60 0.8249 nan 0.1000 -0.0013
## 80 0.7936 nan 0.1000 -0.0014
## 100 0.7733 nan 0.1000 -0.0016
## 120 0.7505 nan 0.1000 -0.0015
## 140 0.7330 nan 0.1000 -0.0017
## 150 0.7210 nan 0.1000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2389 nan 0.1000 0.0243
## 2 1.1936 nan 0.1000 0.0207
## 3 1.1550 nan 0.1000 0.0175
## 4 1.1238 nan 0.1000 0.0108
## 5 1.0955 nan 0.1000 0.0105
## 6 1.0711 nan 0.1000 0.0090
## 7 1.0490 nan 0.1000 0.0090
## 8 1.0260 nan 0.1000 0.0073
## 9 1.0059 nan 0.1000 0.0050
## 10 0.9939 nan 0.1000 0.0019
## 20 0.9000 nan 0.1000 0.0016
## 40 0.8123 nan 0.1000 -0.0028
## 60 0.7626 nan 0.1000 -0.0023
## 80 0.7265 nan 0.1000 -0.0015
## 100 0.6929 nan 0.1000 -0.0028
## 120 0.6581 nan 0.1000 -0.0012
## 140 0.6303 nan 0.1000 -0.0020
## 150 0.6160 nan 0.1000 -0.0020
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2634 nan 0.1000 0.0170
## 2 1.2345 nan 0.1000 0.0131
## 3 1.2115 nan 0.1000 0.0117
## 4 1.1927 nan 0.1000 0.0072
## 5 1.1727 nan 0.1000 0.0096
## 6 1.1538 nan 0.1000 0.0070
## 7 1.1383 nan 0.1000 0.0070
## 8 1.1265 nan 0.1000 0.0042
## 9 1.1110 nan 0.1000 0.0063
## 10 1.0998 nan 0.1000 0.0041
## 20 1.0079 nan 0.1000 0.0005
## 40 0.9254 nan 0.1000 0.0001
## 60 0.8810 nan 0.1000 0.0001
## 80 0.8599 nan 0.1000 -0.0022
## 100 0.8433 nan 0.1000 -0.0004
## 120 0.8310 nan 0.1000 0.0000
## 140 0.8205 nan 0.1000 -0.0006
## 150 0.8179 nan 0.1000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2500 nan 0.1000 0.0201
## 2 1.2185 nan 0.1000 0.0139
## 3 1.1866 nan 0.1000 0.0140
## 4 1.1586 nan 0.1000 0.0120
## 5 1.1331 nan 0.1000 0.0095
## 6 1.1120 nan 0.1000 0.0084
## 7 1.0904 nan 0.1000 0.0070
## 8 1.0702 nan 0.1000 0.0077
## 9 1.0566 nan 0.1000 0.0049
## 10 1.0406 nan 0.1000 0.0051
## 20 0.9421 nan 0.1000 0.0005
## 40 0.8561 nan 0.1000 0.0009
## 60 0.8131 nan 0.1000 -0.0012
## 80 0.7848 nan 0.1000 -0.0018
## 100 0.7580 nan 0.1000 -0.0017
## 120 0.7336 nan 0.1000 -0.0016
## 140 0.7155 nan 0.1000 -0.0010
## 150 0.7070 nan 0.1000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2441 nan 0.1000 0.0240
## 2 1.2010 nan 0.1000 0.0193
## 3 1.1659 nan 0.1000 0.0167
## 4 1.1303 nan 0.1000 0.0160
## 5 1.1027 nan 0.1000 0.0112
## 6 1.0767 nan 0.1000 0.0087
## 7 1.0537 nan 0.1000 0.0090
## 8 1.0353 nan 0.1000 0.0058
## 9 1.0171 nan 0.1000 0.0082
## 10 1.0000 nan 0.1000 0.0065
## 20 0.8987 nan 0.1000 0.0021
## 40 0.8102 nan 0.1000 -0.0024
## 60 0.7616 nan 0.1000 -0.0009
## 80 0.7268 nan 0.1000 -0.0020
## 100 0.6938 nan 0.1000 -0.0016
## 120 0.6655 nan 0.1000 -0.0025
## 140 0.6379 nan 0.1000 -0.0019
## 150 0.6216 nan 0.1000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2555 nan 0.1000 0.0180
## 2 1.2247 nan 0.1000 0.0108
## 3 1.1976 nan 0.1000 0.0116
## 4 1.1733 nan 0.1000 0.0072
## 5 1.1552 nan 0.1000 0.0063
## 6 1.1382 nan 0.1000 0.0077
## 7 1.1233 nan 0.1000 0.0067
## 8 1.1085 nan 0.1000 0.0060
## 9 1.0967 nan 0.1000 0.0037
## 10 1.0806 nan 0.1000 0.0055
## 20 0.9917 nan 0.1000 0.0018
## 40 0.9115 nan 0.1000 0.0004
## 60 0.8794 nan 0.1000 -0.0001
## 80 0.8567 nan 0.1000 -0.0006
## 100 0.8403 nan 0.1000 -0.0011
## 120 0.8279 nan 0.1000 -0.0017
## 140 0.8185 nan 0.1000 -0.0008
## 150 0.8146 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2420 nan 0.1000 0.0236
## 2 1.2064 nan 0.1000 0.0153
## 3 1.1727 nan 0.1000 0.0117
## 4 1.1459 nan 0.1000 0.0124
## 5 1.1231 nan 0.1000 0.0096
## 6 1.0999 nan 0.1000 0.0099
## 7 1.0802 nan 0.1000 0.0088
## 8 1.0629 nan 0.1000 0.0062
## 9 1.0440 nan 0.1000 0.0072
## 10 1.0295 nan 0.1000 0.0063
## 20 0.9278 nan 0.1000 0.0009
## 40 0.8547 nan 0.1000 -0.0008
## 60 0.8140 nan 0.1000 -0.0004
## 80 0.7926 nan 0.1000 -0.0022
## 100 0.7676 nan 0.1000 -0.0031
## 120 0.7493 nan 0.1000 -0.0005
## 140 0.7276 nan 0.1000 -0.0002
## 150 0.7181 nan 0.1000 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2438 nan 0.1000 0.0237
## 2 1.1944 nan 0.1000 0.0218
## 3 1.1596 nan 0.1000 0.0124
## 4 1.1211 nan 0.1000 0.0122
## 5 1.0940 nan 0.1000 0.0102
## 6 1.0658 nan 0.1000 0.0111
## 7 1.0435 nan 0.1000 0.0104
## 8 1.0255 nan 0.1000 0.0070
## 9 1.0089 nan 0.1000 0.0079
## 10 0.9943 nan 0.1000 0.0041
## 20 0.8931 nan 0.1000 0.0006
## 40 0.8093 nan 0.1000 -0.0012
## 60 0.7626 nan 0.1000 -0.0031
## 80 0.7294 nan 0.1000 -0.0024
## 100 0.6934 nan 0.1000 -0.0017
## 120 0.6592 nan 0.1000 -0.0022
## 140 0.6309 nan 0.1000 -0.0039
## 150 0.6183 nan 0.1000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2569 nan 0.1000 0.0158
## 2 1.2317 nan 0.1000 0.0134
## 3 1.2111 nan 0.1000 0.0073
## 4 1.1865 nan 0.1000 0.0104
## 5 1.1659 nan 0.1000 0.0097
## 6 1.1478 nan 0.1000 0.0061
## 7 1.1321 nan 0.1000 0.0059
## 8 1.1211 nan 0.1000 0.0050
## 9 1.1078 nan 0.1000 0.0051
## 10 1.0929 nan 0.1000 0.0059
## 20 1.0067 nan 0.1000 0.0012
## 40 0.9297 nan 0.1000 -0.0002
## 60 0.8877 nan 0.1000 0.0000
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## 140 0.8167 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2409 nan 0.1000 0.0176
## 2 1.2049 nan 0.1000 0.0147
## 3 1.1712 nan 0.1000 0.0134
## 4 1.1424 nan 0.1000 0.0112
## 5 1.1180 nan 0.1000 0.0095
## 6 1.0933 nan 0.1000 0.0091
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## 9 1.0464 nan 0.1000 0.0044
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## 120 0.7251 nan 0.1000 -0.0010
## 140 0.7055 nan 0.1000 -0.0014
## 150 0.6965 nan 0.1000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2404 nan 0.1000 0.0235
## 2 1.1976 nan 0.1000 0.0203
## 3 1.1589 nan 0.1000 0.0126
## 4 1.1249 nan 0.1000 0.0150
## 5 1.0990 nan 0.1000 0.0100
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## 120 0.6410 nan 0.1000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2653 nan 0.1000 0.0108
## 2 1.2316 nan 0.1000 0.0151
## 3 1.2073 nan 0.1000 0.0111
## 4 1.1864 nan 0.1000 0.0111
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## 6 1.1514 nan 0.1000 0.0077
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## 10 1.0900 nan 0.1000 0.0065
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## 40 0.9155 nan 0.1000 -0.0002
## 60 0.8728 nan 0.1000 0.0004
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## 120 0.8164 nan 0.1000 -0.0017
## 140 0.8060 nan 0.1000 -0.0011
## 150 0.8045 nan 0.1000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2523 nan 0.1000 0.0166
## 2 1.2136 nan 0.1000 0.0174
## 3 1.1763 nan 0.1000 0.0152
## 4 1.1432 nan 0.1000 0.0126
## 5 1.1203 nan 0.1000 0.0093
## 6 1.1002 nan 0.1000 0.0094
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## 120 0.7383 nan 0.1000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2423 nan 0.1000 0.0255
## 2 1.1935 nan 0.1000 0.0215
## 3 1.1508 nan 0.1000 0.0177
## 4 1.1191 nan 0.1000 0.0116
## 5 1.0929 nan 0.1000 0.0103
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## 7 1.0426 nan 0.1000 0.0097
## 8 1.0199 nan 0.1000 0.0087
## 9 1.0013 nan 0.1000 0.0069
## 10 0.9856 nan 0.1000 0.0063
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## 40 0.8035 nan 0.1000 -0.0022
## 60 0.7508 nan 0.1000 -0.0018
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2474 nan 0.1000 0.0147
## 2 1.2275 nan 0.1000 0.0084
## 3 1.2027 nan 0.1000 0.0131
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## 6 1.1440 nan 0.1000 0.0066
## 7 1.1294 nan 0.1000 0.0058
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2539 nan 0.1000 0.0188
## 2 1.2144 nan 0.1000 0.0172
## 3 1.1814 nan 0.1000 0.0175
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## 6 1.0993 nan 0.1000 0.0101
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## 10 1.0348 nan 0.1000 0.0039
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2373 nan 0.1000 0.0226
## 2 1.1921 nan 0.1000 0.0208
## 3 1.1573 nan 0.1000 0.0170
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## 10 1.0007 nan 0.1000 0.0032
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2567 nan 0.1000 0.0146
## 2 1.2293 nan 0.1000 0.0144
## 3 1.2078 nan 0.1000 0.0086
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## 6 1.1553 nan 0.1000 0.0061
## 7 1.1394 nan 0.1000 0.0059
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2459 nan 0.1000 0.0194
## 2 1.2126 nan 0.1000 0.0128
## 3 1.1854 nan 0.1000 0.0116
## 4 1.1565 nan 0.1000 0.0123
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2410 nan 0.1000 0.0251
## 2 1.2028 nan 0.1000 0.0172
## 3 1.1645 nan 0.1000 0.0144
## 4 1.1362 nan 0.1000 0.0074
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## 9 1.0178 nan 0.1000 0.0034
## 10 1.0027 nan 0.1000 0.0064
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2565 nan 0.1000 0.0149
## 2 1.2253 nan 0.1000 0.0129
## 3 1.2032 nan 0.1000 0.0102
## 4 1.1819 nan 0.1000 0.0094
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## 10 1.0953 nan 0.1000 0.0036
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2528 nan 0.1000 0.0197
## 2 1.2170 nan 0.1000 0.0142
## 3 1.1822 nan 0.1000 0.0159
## 4 1.1568 nan 0.1000 0.0117
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2435 nan 0.1000 0.0227
## 2 1.1991 nan 0.1000 0.0200
## 3 1.1706 nan 0.1000 0.0088
## 4 1.1358 nan 0.1000 0.0126
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2565 nan 0.1000 0.0161
## 2 1.2298 nan 0.1000 0.0124
## 3 1.2074 nan 0.1000 0.0112
## 4 1.1851 nan 0.1000 0.0092
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2496 nan 0.1000 0.0224
## 2 1.2118 nan 0.1000 0.0168
## 3 1.1751 nan 0.1000 0.0140
## 4 1.1450 nan 0.1000 0.0138
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2340 nan 0.1000 0.0268
## 2 1.1925 nan 0.1000 0.0168
## 3 1.1535 nan 0.1000 0.0174
## 4 1.1195 nan 0.1000 0.0153
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2569 nan 0.1000 0.0179
## 2 1.2314 nan 0.1000 0.0138
## 3 1.2076 nan 0.1000 0.0099
## 4 1.1868 nan 0.1000 0.0090
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2547 nan 0.1000 0.0167
## 2 1.2235 nan 0.1000 0.0149
## 3 1.1922 nan 0.1000 0.0158
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2482 nan 0.1000 0.0185
## 2 1.2034 nan 0.1000 0.0211
## 3 1.1621 nan 0.1000 0.0193
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2647 nan 0.1000 0.0143
## 2 1.2356 nan 0.1000 0.0125
## 3 1.2122 nan 0.1000 0.0094
## 4 1.1882 nan 0.1000 0.0091
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2524 nan 0.1000 0.0172
## 2 1.2117 nan 0.1000 0.0162
## 3 1.1800 nan 0.1000 0.0122
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2421 nan 0.1000 0.0198
## 2 1.1957 nan 0.1000 0.0187
## 3 1.1566 nan 0.1000 0.0138
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2598 nan 0.1000 0.0136
## 2 1.2263 nan 0.1000 0.0146
## 3 1.2062 nan 0.1000 0.0068
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2489 nan 0.1000 0.0170
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## 3 1.1766 nan 0.1000 0.0168
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2438 nan 0.1000 0.0153
## 2 1.2031 nan 0.1000 0.0182
## 3 1.1625 nan 0.1000 0.0192
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## 8 1.0309 nan 0.1000 0.0036
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2540 nan 0.1000 0.0178
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## 3 1.1942 nan 0.1000 0.0131
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2460 nan 0.1000 0.0183
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2381 nan 0.1000 0.0237
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2568 nan 0.1000 0.0157
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2490 nan 0.1000 0.0203
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2449 nan 0.1000 0.0234
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2588 nan 0.1000 0.0177
## 2 1.2248 nan 0.1000 0.0136
## 3 1.1999 nan 0.1000 0.0122
## 4 1.1797 nan 0.1000 0.0093
## 5 1.1613 nan 0.1000 0.0074
## 6 1.1470 nan 0.1000 0.0056
## 7 1.1313 nan 0.1000 0.0063
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## 9 1.1018 nan 0.1000 0.0048
## 10 1.0876 nan 0.1000 0.0063
## 20 0.9940 nan 0.1000 0.0033
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## 80 0.8398 nan 0.1000 -0.0006
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## 120 0.8144 nan 0.1000 -0.0016
## 140 0.8048 nan 0.1000 -0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2441 nan 0.1000 0.0214
## 2 1.2002 nan 0.1000 0.0190
## 3 1.1639 nan 0.1000 0.0124
## 4 1.1319 nan 0.1000 0.0110
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## 9 1.0347 nan 0.1000 0.0064
## 10 1.0200 nan 0.1000 0.0021
## 20 0.9260 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2459 nan 0.1000 0.0227
## 2 1.2002 nan 0.1000 0.0185
## 3 1.1601 nan 0.1000 0.0167
## 4 1.1257 nan 0.1000 0.0126
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## 6 1.0755 nan 0.1000 0.0084
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2604 nan 0.1000 0.0161
## 2 1.2289 nan 0.1000 0.0128
## 3 1.2009 nan 0.1000 0.0134
## 4 1.1795 nan 0.1000 0.0063
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## 6 1.1401 nan 0.1000 0.0080
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2428 nan 0.1000 0.0222
## 2 1.2043 nan 0.1000 0.0181
## 3 1.1663 nan 0.1000 0.0149
## 4 1.1338 nan 0.1000 0.0132
## 5 1.1120 nan 0.1000 0.0093
## 6 1.0884 nan 0.1000 0.0100
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## 60 0.7947 nan 0.1000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2347 nan 0.1000 0.0264
## 2 1.1913 nan 0.1000 0.0193
## 3 1.1514 nan 0.1000 0.0187
## 4 1.1191 nan 0.1000 0.0104
## 5 1.0881 nan 0.1000 0.0108
## 6 1.0644 nan 0.1000 0.0103
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## 9 1.0007 nan 0.1000 0.0042
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2554 nan 0.1000 0.0175
## 2 1.2230 nan 0.1000 0.0092
## 3 1.2007 nan 0.1000 0.0105
## 4 1.1803 nan 0.1000 0.0091
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## 7 1.1269 nan 0.1000 0.0065
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2499 nan 0.1000 0.0165
## 2 1.2134 nan 0.1000 0.0170
## 3 1.1822 nan 0.1000 0.0146
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2427 nan 0.1000 0.0203
## 2 1.1931 nan 0.1000 0.0191
## 3 1.1512 nan 0.1000 0.0165
## 4 1.1238 nan 0.1000 0.0118
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## 40 0.8082 nan 0.1000 -0.0014
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## 120 0.6546 nan 0.1000 -0.0033
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2609 nan 0.1000 0.0160
## 2 1.2360 nan 0.1000 0.0138
## 3 1.2128 nan 0.1000 0.0094
## 4 1.1879 nan 0.1000 0.0113
## 5 1.1665 nan 0.1000 0.0088
## 6 1.1517 nan 0.1000 0.0059
## 7 1.1345 nan 0.1000 0.0059
## 8 1.1206 nan 0.1000 0.0036
## 9 1.1039 nan 0.1000 0.0062
## 10 1.0921 nan 0.1000 0.0038
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## 40 0.9243 nan 0.1000 0.0001
## 60 0.8870 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2473 nan 0.1000 0.0217
## 2 1.2092 nan 0.1000 0.0163
## 3 1.1816 nan 0.1000 0.0139
## 4 1.1541 nan 0.1000 0.0127
## 5 1.1286 nan 0.1000 0.0119
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2354 nan 0.1000 0.0233
## 2 1.1950 nan 0.1000 0.0182
## 3 1.1602 nan 0.1000 0.0141
## 4 1.1309 nan 0.1000 0.0113
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2584 nan 0.1000 0.0145
## 2 1.2297 nan 0.1000 0.0141
## 3 1.2071 nan 0.1000 0.0096
## 4 1.1879 nan 0.1000 0.0078
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2497 nan 0.1000 0.0176
## 2 1.2109 nan 0.1000 0.0157
## 3 1.1813 nan 0.1000 0.0132
## 4 1.1551 nan 0.1000 0.0129
## 5 1.1346 nan 0.1000 0.0090
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2352 nan 0.1000 0.0274
## 2 1.1929 nan 0.1000 0.0180
## 3 1.1543 nan 0.1000 0.0181
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2632 nan 0.1000 0.0153
## 2 1.2291 nan 0.1000 0.0138
## 3 1.2039 nan 0.1000 0.0102
## 4 1.1797 nan 0.1000 0.0098
## 5 1.1606 nan 0.1000 0.0065
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2442 nan 0.1000 0.0178
## 2 1.2036 nan 0.1000 0.0159
## 3 1.1688 nan 0.1000 0.0143
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2453 nan 0.1000 0.0218
## 2 1.2026 nan 0.1000 0.0183
## 3 1.1589 nan 0.1000 0.0187
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2577 nan 0.1000 0.0166
## 2 1.2251 nan 0.1000 0.0146
## 3 1.1989 nan 0.1000 0.0121
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2487 nan 0.1000 0.0222
## 2 1.2127 nan 0.1000 0.0168
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2355 nan 0.1000 0.0262
## 2 1.1927 nan 0.1000 0.0184
## 3 1.1544 nan 0.1000 0.0163
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2541 nan 0.1000 0.0150
## 2 1.2267 nan 0.1000 0.0133
## 3 1.2056 nan 0.1000 0.0100
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2483 nan 0.1000 0.0229
## 2 1.2047 nan 0.1000 0.0205
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2428 nan 0.1000 0.0226
## 2 1.2011 nan 0.1000 0.0184
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2543 nan 0.1000 0.0165
## 2 1.2200 nan 0.1000 0.0147
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2466 nan 0.1000 0.0182
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2465 nan 0.1000 0.0202
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## 150 0.6012 nan 0.1000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2524 nan 0.1000 0.0152
## 2 1.2269 nan 0.1000 0.0120
## 3 1.2038 nan 0.1000 0.0123
## 4 1.1816 nan 0.1000 0.0104
## 5 1.1655 nan 0.1000 0.0078
## 6 1.1475 nan 0.1000 0.0071
## 7 1.1301 nan 0.1000 0.0065
## 8 1.1169 nan 0.1000 0.0040
## 9 1.1045 nan 0.1000 0.0048
## 10 1.0940 nan 0.1000 0.0032
## 20 1.0107 nan 0.1000 -0.0016
## 40 0.9237 nan 0.1000 0.0004
## 60 0.8881 nan 0.1000 -0.0010
## 80 0.8638 nan 0.1000 -0.0006
## 100 0.8479 nan 0.1000 -0.0007
## 120 0.8382 nan 0.1000 -0.0010
## 140 0.8282 nan 0.1000 -0.0005
## 150 0.8218 nan 0.1000 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2498 nan 0.1000 0.0200
## 2 1.2133 nan 0.1000 0.0170
## 3 1.1781 nan 0.1000 0.0152
## 4 1.1579 nan 0.1000 0.0078
## 5 1.1336 nan 0.1000 0.0105
## 6 1.1101 nan 0.1000 0.0090
## 7 1.0874 nan 0.1000 0.0103
## 8 1.0699 nan 0.1000 0.0078
## 9 1.0569 nan 0.1000 0.0048
## 10 1.0407 nan 0.1000 0.0067
## 20 0.9364 nan 0.1000 0.0011
## 40 0.8583 nan 0.1000 -0.0024
## 60 0.8166 nan 0.1000 -0.0021
## 80 0.7959 nan 0.1000 -0.0014
## 100 0.7593 nan 0.1000 -0.0014
## 120 0.7349 nan 0.1000 -0.0009
## 140 0.7151 nan 0.1000 -0.0004
## 150 0.7058 nan 0.1000 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2459 nan 0.1000 0.0213
## 2 1.1948 nan 0.1000 0.0226
## 3 1.1580 nan 0.1000 0.0173
## 4 1.1297 nan 0.1000 0.0131
## 5 1.1038 nan 0.1000 0.0112
## 6 1.0832 nan 0.1000 0.0089
## 7 1.0627 nan 0.1000 0.0070
## 8 1.0443 nan 0.1000 0.0067
## 9 1.0210 nan 0.1000 0.0094
## 10 1.0037 nan 0.1000 0.0064
## 20 0.8941 nan 0.1000 0.0013
## 40 0.8006 nan 0.1000 -0.0007
## 60 0.7469 nan 0.1000 -0.0012
## 80 0.7090 nan 0.1000 -0.0018
## 100 0.6785 nan 0.1000 -0.0020
## 120 0.6506 nan 0.1000 -0.0020
## 140 0.6248 nan 0.1000 -0.0014
## 150 0.6128 nan 0.1000 -0.0021
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2555 nan 0.1000 0.0161
## 2 1.2264 nan 0.1000 0.0134
## 3 1.2041 nan 0.1000 0.0071
## 4 1.1804 nan 0.1000 0.0094
## 5 1.1614 nan 0.1000 0.0085
## 6 1.1426 nan 0.1000 0.0078
## 7 1.1248 nan 0.1000 0.0065
## 8 1.1095 nan 0.1000 0.0062
## 9 1.0957 nan 0.1000 0.0063
## 10 1.0845 nan 0.1000 0.0038
## 20 0.9973 nan 0.1000 0.0017
## 40 0.9239 nan 0.1000 0.0012
## 60 0.8870 nan 0.1000 -0.0001
## 80 0.8572 nan 0.1000 -0.0006
## 100 0.8423 nan 0.1000 -0.0011
## 120 0.8274 nan 0.1000 -0.0007
## 140 0.8190 nan 0.1000 -0.0016
## 150 0.8157 nan 0.1000 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2521 nan 0.1000 0.0197
## 2 1.2117 nan 0.1000 0.0163
## 3 1.1741 nan 0.1000 0.0135
## 4 1.1453 nan 0.1000 0.0080
## 5 1.1181 nan 0.1000 0.0127
## 6 1.0946 nan 0.1000 0.0098
## 7 1.0755 nan 0.1000 0.0077
## 8 1.0574 nan 0.1000 0.0077
## 9 1.0429 nan 0.1000 0.0057
## 10 1.0278 nan 0.1000 0.0047
## 20 0.9322 nan 0.1000 0.0015
## 40 0.8564 nan 0.1000 -0.0004
## 60 0.8128 nan 0.1000 -0.0008
## 80 0.7847 nan 0.1000 -0.0014
## 100 0.7648 nan 0.1000 -0.0021
## 120 0.7441 nan 0.1000 -0.0017
## 140 0.7235 nan 0.1000 -0.0014
## 150 0.7158 nan 0.1000 -0.0019
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2425 nan 0.1000 0.0246
## 2 1.1979 nan 0.1000 0.0231
## 3 1.1614 nan 0.1000 0.0156
## 4 1.1285 nan 0.1000 0.0158
## 5 1.1010 nan 0.1000 0.0128
## 6 1.0776 nan 0.1000 0.0076
## 7 1.0510 nan 0.1000 0.0119
## 8 1.0329 nan 0.1000 0.0050
## 9 1.0141 nan 0.1000 0.0064
## 10 0.9963 nan 0.1000 0.0072
## 20 0.8984 nan 0.1000 0.0014
## 40 0.8148 nan 0.1000 -0.0011
## 60 0.7619 nan 0.1000 -0.0009
## 80 0.7237 nan 0.1000 -0.0019
## 100 0.6905 nan 0.1000 -0.0008
## 120 0.6609 nan 0.1000 -0.0025
## 140 0.6288 nan 0.1000 -0.0012
## 150 0.6177 nan 0.1000 -0.0025
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2584 nan 0.1000 0.0179
## 2 1.2286 nan 0.1000 0.0141
## 3 1.2005 nan 0.1000 0.0123
## 4 1.1801 nan 0.1000 0.0103
## 5 1.1590 nan 0.1000 0.0075
## 6 1.1439 nan 0.1000 0.0069
## 7 1.1286 nan 0.1000 0.0067
## 8 1.1151 nan 0.1000 0.0061
## 9 1.1025 nan 0.1000 0.0051
## 10 1.0876 nan 0.1000 0.0058
## 20 0.9961 nan 0.1000 0.0027
## 40 0.9150 nan 0.1000 -0.0002
## 60 0.8759 nan 0.1000 0.0002
## 80 0.8486 nan 0.1000 -0.0002
## 100 0.8321 nan 0.1000 -0.0004
## 120 0.8209 nan 0.1000 -0.0013
## 140 0.8102 nan 0.1000 -0.0011
## 150 0.8017 nan 0.1000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2485 nan 0.1000 0.0178
## 2 1.2154 nan 0.1000 0.0147
## 3 1.1811 nan 0.1000 0.0140
## 4 1.1545 nan 0.1000 0.0101
## 5 1.1215 nan 0.1000 0.0138
## 6 1.0986 nan 0.1000 0.0089
## 7 1.0845 nan 0.1000 0.0048
## 8 1.0706 nan 0.1000 0.0041
## 9 1.0513 nan 0.1000 0.0075
## 10 1.0367 nan 0.1000 0.0052
## 20 0.9394 nan 0.1000 0.0012
## 40 0.8575 nan 0.1000 -0.0012
## 60 0.8079 nan 0.1000 -0.0007
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## 100 0.7515 nan 0.1000 -0.0013
## 120 0.7268 nan 0.1000 -0.0008
## 140 0.7082 nan 0.1000 -0.0005
## 150 0.6973 nan 0.1000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2413 nan 0.1000 0.0218
## 2 1.1977 nan 0.1000 0.0181
## 3 1.1531 nan 0.1000 0.0178
## 4 1.1249 nan 0.1000 0.0086
## 5 1.0966 nan 0.1000 0.0117
## 6 1.0743 nan 0.1000 0.0070
## 7 1.0525 nan 0.1000 0.0096
## 8 1.0348 nan 0.1000 0.0067
## 9 1.0146 nan 0.1000 0.0056
## 10 0.9970 nan 0.1000 0.0048
## 20 0.8945 nan 0.1000 0.0002
## 40 0.8058 nan 0.1000 -0.0008
## 60 0.7520 nan 0.1000 -0.0023
## 80 0.7180 nan 0.1000 -0.0020
## 100 0.6813 nan 0.1000 -0.0021
## 120 0.6508 nan 0.1000 -0.0019
## 140 0.6255 nan 0.1000 -0.0030
## 150 0.6129 nan 0.1000 -0.0015
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2590 nan 0.1000 0.0159
## 2 1.2306 nan 0.1000 0.0143
## 3 1.2059 nan 0.1000 0.0124
## 4 1.1854 nan 0.1000 0.0096
## 5 1.1666 nan 0.1000 0.0083
## 6 1.1503 nan 0.1000 0.0067
## 7 1.1330 nan 0.1000 0.0068
## 8 1.1167 nan 0.1000 0.0063
## 9 1.1023 nan 0.1000 0.0052
## 10 1.0897 nan 0.1000 0.0046
## 20 1.0097 nan 0.1000 0.0012
## 40 0.9248 nan 0.1000 0.0009
## 50 0.9059 nan 0.1000 -0.0003# Neural Network (Regression/Classification)
set.seed(seedNum)
fit.nnet <- train(classVar~., data=dataset, method="nnet", metric=metricTarget, trControl=control)## # weights: 11
## initial value 450.372635
## iter 10 value 429.578924
## iter 20 value 419.197576
## iter 30 value 414.246101
## iter 40 value 410.558579
## iter 50 value 410.503009
## iter 60 value 410.502560
## final value 410.502221
## converged
## # weights: 31
## initial value 696.076489
## iter 10 value 443.195613
## iter 20 value 429.180927
## iter 30 value 414.389302
## iter 40 value 402.897431
## iter 50 value 336.932200
## iter 60 value 329.881527
## iter 70 value 320.392704
## iter 80 value 319.107903
## final value 319.001506
## converged
## # weights: 51
## initial value 537.788813
## iter 10 value 431.174234
## iter 20 value 420.707494
## iter 30 value 410.046855
## iter 40 value 401.101865
## iter 50 value 391.799571
## iter 60 value 378.901790
## iter 70 value 370.738479
## iter 80 value 367.838536
## iter 90 value 366.041697
## iter 100 value 366.016847
## final value 366.016847
## stopped after 100 iterations
## # weights: 11
## initial value 474.874962
## iter 10 value 415.416545
## iter 20 value 405.109574
## iter 30 value 397.787977
## iter 40 value 361.688742
## iter 50 value 335.359452
## iter 60 value 327.973078
## iter 70 value 327.948475
## final value 327.945863
## converged
## # weights: 31
## initial value 580.188871
## iter 10 value 444.541557
## iter 20 value 442.999851
## iter 30 value 436.793082
## iter 40 value 423.894469
## iter 50 value 417.083658
## iter 60 value 411.870446
## iter 70 value 407.633013
## iter 80 value 387.685927
## iter 90 value 375.488335
## iter 100 value 334.051607
## final value 334.051607
## stopped after 100 iterations
## # weights: 51
## initial value 462.975170
## iter 10 value 437.386889
## iter 20 value 420.423014
## iter 30 value 406.224649
## iter 40 value 397.516148
## iter 50 value 390.374211
## iter 60 value 385.480793
## iter 70 value 381.477597
## iter 80 value 378.167628
## iter 90 value 369.813808
## iter 100 value 366.018396
## final value 366.018396
## stopped after 100 iterations
## # weights: 11
## initial value 463.515306
## iter 10 value 427.242179
## iter 20 value 421.396228
## iter 30 value 420.564806
## iter 40 value 420.486550
## iter 50 value 420.337944
## iter 60 value 420.335729
## iter 70 value 420.334329
## iter 70 value 420.334328
## iter 70 value 420.334328
## final value 420.334328
## converged
## # weights: 31
## initial value 658.846597
## final value 447.912497
## converged
## # weights: 51
## initial value 573.491987
## iter 10 value 428.910980
## iter 20 value 410.350760
## iter 30 value 398.221498
## iter 40 value 394.876130
## iter 50 value 393.677236
## iter 60 value 392.612204
## iter 70 value 391.366739
## iter 80 value 388.793965
## iter 90 value 388.704387
## iter 100 value 388.693578
## final value 388.693578
## stopped after 100 iterations
## # weights: 11
## initial value 448.979188
## iter 10 value 437.765827
## iter 20 value 429.446108
## iter 30 value 415.432297
## iter 40 value 413.889404
## iter 50 value 410.911600
## iter 60 value 369.210002
## iter 70 value 350.945359
## iter 80 value 343.550709
## iter 90 value 343.386826
## iter 90 value 343.386824
## iter 90 value 343.386824
## final value 343.386824
## converged
## # weights: 31
## initial value 498.349294
## iter 10 value 435.078555
## iter 20 value 431.168435
## iter 30 value 428.038860
## iter 40 value 426.558850
## iter 50 value 426.208760
## iter 60 value 425.986498
## iter 70 value 425.980704
## iter 80 value 425.978223
## iter 90 value 425.970695
## iter 100 value 425.968029
## final value 425.968029
## stopped after 100 iterations
## # weights: 51
## initial value 537.086533
## iter 10 value 421.653374
## iter 20 value 402.413294
## iter 30 value 393.458974
## iter 40 value 383.526757
## iter 50 value 378.930981
## iter 60 value 372.088746
## iter 70 value 366.821913
## iter 80 value 356.882600
## iter 90 value 340.643090
## iter 100 value 322.527060
## final value 322.527060
## stopped after 100 iterations
## # weights: 11
## initial value 585.591995
## iter 10 value 426.836790
## iter 20 value 404.594005
## iter 30 value 402.682350
## iter 40 value 401.184122
## iter 50 value 400.555135
## iter 60 value 400.383352
## final value 400.383327
## converged
## # weights: 31
## initial value 439.805407
## iter 10 value 432.128374
## iter 20 value 418.377441
## iter 30 value 400.942525
## iter 40 value 395.571036
## iter 50 value 394.297539
## iter 60 value 392.093827
## iter 70 value 386.984343
## iter 80 value 377.278217
## iter 90 value 355.391423
## iter 100 value 327.494825
## final value 327.494825
## stopped after 100 iterations
## # weights: 51
## initial value 448.281594
## iter 10 value 426.919165
## iter 20 value 414.972421
## iter 30 value 407.380738
## iter 40 value 399.813945
## iter 50 value 390.371223
## iter 60 value 372.010344
## iter 70 value 365.845718
## iter 80 value 351.341629
## iter 90 value 332.498372
## iter 100 value 315.599847
## final value 315.599847
## stopped after 100 iterations
## # weights: 11
## initial value 447.203247
## iter 10 value 414.258513
## iter 20 value 406.682718
## iter 30 value 400.183083
## iter 40 value 399.993728
## iter 50 value 399.922427
## iter 60 value 399.898810
## iter 70 value 399.890088
## iter 80 value 399.873932
## iter 90 value 399.870820
## iter 100 value 399.868554
## final value 399.868554
## stopped after 100 iterations
## # weights: 31
## initial value 451.381609
## iter 10 value 427.470101
## iter 20 value 424.901879
## iter 30 value 423.901323
## iter 40 value 423.894170
## iter 50 value 423.890581
## iter 60 value 423.883169
## iter 70 value 423.881276
## final value 423.881099
## converged
## # weights: 51
## initial value 469.961864
## iter 10 value 434.063588
## iter 20 value 419.529918
## iter 30 value 413.202631
## iter 40 value 399.640868
## iter 50 value 386.061239
## iter 60 value 380.043701
## iter 70 value 374.773466
## iter 80 value 371.969350
## iter 90 value 369.062747
## iter 100 value 368.340243
## final value 368.340243
## stopped after 100 iterations
## # weights: 11
## initial value 494.598261
## iter 10 value 435.809815
## iter 20 value 434.549735
## iter 30 value 434.366636
## iter 40 value 433.587320
## iter 50 value 433.121333
## final value 433.074344
## converged
## # weights: 31
## initial value 450.159384
## iter 10 value 441.658749
## iter 20 value 416.926771
## iter 30 value 403.681945
## iter 40 value 399.864349
## iter 50 value 396.231265
## iter 60 value 395.685216
## final value 395.684507
## converged
## # weights: 51
## initial value 468.407545
## iter 10 value 414.317319
## iter 20 value 395.713545
## iter 30 value 390.341958
## iter 40 value 385.856942
## iter 50 value 384.376778
## iter 60 value 384.067556
## iter 70 value 384.065825
## iter 70 value 384.065822
## iter 70 value 384.065822
## final value 384.065822
## converged
## # weights: 11
## initial value 447.753343
## iter 10 value 446.034426
## iter 20 value 442.992093
## iter 30 value 423.019945
## iter 40 value 408.503328
## iter 50 value 398.240708
## iter 60 value 380.965700
## iter 70 value 339.563956
## iter 80 value 331.431908
## iter 90 value 331.141279
## final value 331.141263
## converged
## # weights: 31
## initial value 531.088624
## iter 10 value 431.369996
## iter 20 value 423.362819
## iter 30 value 417.888596
## iter 40 value 401.624298
## iter 50 value 390.291827
## iter 60 value 388.421720
## iter 70 value 386.421099
## iter 80 value 372.731222
## iter 90 value 337.165314
## iter 100 value 330.467932
## final value 330.467932
## stopped after 100 iterations
## # weights: 51
## initial value 443.879888
## iter 10 value 419.566234
## iter 20 value 399.948393
## iter 30 value 391.476302
## iter 40 value 383.070671
## iter 50 value 376.679949
## iter 60 value 370.352223
## iter 70 value 368.091476
## iter 80 value 342.508058
## iter 90 value 332.563880
## iter 100 value 324.383281
## final value 324.383281
## stopped after 100 iterations
## # weights: 11
## initial value 455.298125
## final value 446.857343
## converged
## # weights: 31
## initial value 447.839525
## iter 10 value 434.737668
## iter 20 value 430.958443
## iter 30 value 429.501840
## iter 40 value 427.995451
## iter 50 value 427.432051
## iter 60 value 427.199123
## iter 70 value 427.193574
## iter 80 value 427.191469
## final value 427.190852
## converged
## # weights: 51
## initial value 667.567011
## iter 10 value 435.535166
## iter 20 value 433.778838
## iter 30 value 432.719716
## iter 40 value 432.101323
## iter 50 value 431.742465
## iter 60 value 431.737903
## final value 431.737462
## converged
## # weights: 11
## initial value 516.219146
## iter 10 value 442.960851
## iter 20 value 442.959269
## final value 442.958963
## converged
## # weights: 31
## initial value 602.249111
## iter 10 value 425.510539
## iter 20 value 416.290554
## iter 30 value 406.274502
## iter 40 value 403.292301
## iter 50 value 401.709112
## iter 60 value 400.591596
## iter 70 value 400.226452
## iter 80 value 400.115268
## iter 90 value 399.988481
## iter 100 value 399.961042
## final value 399.961042
## stopped after 100 iterations
## # weights: 51
## initial value 496.104416
## iter 10 value 439.667637
## iter 20 value 416.154832
## iter 30 value 406.333181
## iter 40 value 402.467088
## iter 50 value 400.723577
## iter 60 value 398.295640
## iter 70 value 398.218493
## final value 398.217724
## converged
## # weights: 11
## initial value 446.997311
## iter 10 value 446.887800
## iter 20 value 443.109264
## iter 30 value 437.552946
## iter 40 value 435.034675
## iter 50 value 430.905534
## iter 60 value 411.928219
## iter 70 value 381.458489
## iter 80 value 354.938856
## iter 90 value 335.384839
## iter 100 value 334.366971
## final value 334.366971
## stopped after 100 iterations
## # weights: 31
## initial value 487.876753
## iter 10 value 439.727113
## iter 20 value 428.345227
## iter 30 value 411.216634
## iter 40 value 405.374688
## iter 50 value 399.889927
## iter 60 value 396.886603
## iter 70 value 394.477907
## iter 80 value 393.784061
## iter 90 value 393.726217
## final value 393.726118
## converged
## # weights: 51
## initial value 517.601986
## iter 10 value 442.971227
## iter 20 value 439.963412
## iter 30 value 412.164683
## iter 40 value 406.301323
## iter 50 value 399.871393
## iter 60 value 390.530467
## iter 70 value 379.167085
## iter 80 value 377.471698
## iter 90 value 376.667439
## iter 100 value 375.881324
## final value 375.881324
## stopped after 100 iterations
## # weights: 11
## initial value 574.510563
## final value 446.858115
## converged
## # weights: 31
## initial value 521.579695
## iter 10 value 437.440255
## iter 20 value 412.311742
## iter 30 value 403.612857
## iter 40 value 400.992339
## iter 50 value 397.404764
## iter 60 value 395.680363
## iter 70 value 395.311713
## iter 80 value 395.249012
## iter 90 value 395.217560
## final value 395.217031
## converged
## # weights: 51
## initial value 460.970942
## iter 10 value 435.565552
## iter 20 value 428.016327
## iter 30 value 413.224057
## iter 40 value 408.538772
## iter 50 value 404.035598
## iter 60 value 395.069801
## iter 70 value 390.899146
## iter 80 value 389.612943
## iter 90 value 389.492884
## iter 100 value 389.432736
## final value 389.432736
## stopped after 100 iterations
## # weights: 11
## initial value 573.459127
## final value 446.857145
## converged
## # weights: 31
## initial value 562.043614
## iter 10 value 422.040399
## iter 20 value 412.175022
## iter 30 value 409.331329
## iter 40 value 407.610617
## iter 50 value 406.396207
## iter 60 value 406.379489
## final value 406.379472
## converged
## # weights: 51
## initial value 599.415134
## iter 10 value 441.233896
## iter 20 value 419.062354
## iter 30 value 406.780740
## iter 40 value 399.786308
## iter 50 value 394.193878
## iter 60 value 390.723181
## iter 70 value 389.481212
## iter 80 value 389.474536
## final value 389.474473
## converged
## # weights: 11
## initial value 628.820103
## iter 10 value 446.951468
## iter 20 value 446.878787
## final value 446.878778
## converged
## # weights: 31
## initial value 627.569241
## iter 10 value 427.858508
## iter 20 value 408.760674
## iter 30 value 399.465074
## iter 40 value 395.961811
## iter 50 value 393.776756
## iter 60 value 390.449336
## iter 70 value 382.246387
## iter 80 value 375.435109
## iter 90 value 359.512909
## iter 100 value 328.579717
## final value 328.579717
## stopped after 100 iterations
## # weights: 51
## initial value 503.099976
## iter 10 value 411.359690
## iter 20 value 404.859410
## iter 30 value 400.587735
## iter 40 value 393.534764
## iter 50 value 384.282179
## iter 60 value 368.396374
## iter 70 value 338.023054
## iter 80 value 312.376747
## iter 90 value 304.198908
## iter 100 value 302.404460
## final value 302.404460
## stopped after 100 iterations
## # weights: 11
## initial value 484.911087
## final value 446.858531
## converged
## # weights: 31
## initial value 580.045213
## iter 10 value 434.357777
## iter 20 value 415.414485
## iter 30 value 411.998427
## iter 40 value 410.692307
## iter 50 value 409.796708
## iter 60 value 409.075959
## iter 70 value 409.032930
## iter 80 value 409.030804
## iter 90 value 409.029647
## iter 100 value 409.028020
## final value 409.028020
## stopped after 100 iterations
## # weights: 51
## initial value 467.553820
## iter 10 value 442.274440
## iter 20 value 437.850698
## iter 30 value 436.185381
## iter 40 value 433.976041
## iter 50 value 433.443633
## iter 60 value 432.218293
## iter 70 value 431.343197
## iter 80 value 431.340975
## iter 90 value 431.338288
## iter 100 value 431.336909
## final value 431.336909
## stopped after 100 iterations
## # weights: 11
## initial value 466.715338
## final value 447.909142
## converged
## # weights: 31
## initial value 475.874861
## iter 10 value 444.625012
## iter 20 value 440.280929
## iter 30 value 438.265583
## iter 40 value 436.025869
## iter 50 value 435.360734
## iter 60 value 433.330680
## iter 70 value 431.084045
## iter 80 value 429.879467
## iter 90 value 429.747182
## iter 100 value 429.623943
## final value 429.623943
## stopped after 100 iterations
## # weights: 51
## initial value 618.964361
## iter 10 value 436.433937
## iter 20 value 424.909625
## iter 30 value 420.866527
## iter 40 value 418.382272
## iter 50 value 415.736292
## iter 60 value 415.653663
## iter 70 value 413.461849
## iter 80 value 407.501043
## iter 90 value 406.455312
## iter 100 value 405.622613
## final value 405.622613
## stopped after 100 iterations
## # weights: 11
## initial value 451.094048
## iter 10 value 428.156354
## iter 20 value 416.470969
## iter 30 value 407.188620
## iter 40 value 398.098204
## iter 50 value 348.170564
## iter 60 value 333.184841
## iter 70 value 332.394212
## final value 332.198881
## converged
## # weights: 31
## initial value 451.414322
## iter 10 value 442.219230
## iter 20 value 426.477692
## iter 30 value 407.586832
## iter 40 value 402.590325
## iter 50 value 400.107791
## iter 60 value 395.124633
## iter 70 value 388.758356
## iter 80 value 343.792132
## iter 90 value 333.782685
## iter 100 value 331.590921
## final value 331.590921
## stopped after 100 iterations
## # weights: 51
## initial value 444.894176
## iter 10 value 425.367969
## iter 20 value 412.309724
## iter 30 value 406.361828
## iter 40 value 402.723077
## iter 50 value 397.406150
## iter 60 value 386.832364
## iter 70 value 383.534991
## iter 80 value 380.359955
## iter 90 value 374.391166
## iter 100 value 365.576474
## final value 365.576474
## stopped after 100 iterations
## # weights: 11
## initial value 563.537255
## final value 447.910003
## converged
## # weights: 31
## initial value 478.993611
## iter 10 value 440.365930
## iter 20 value 434.208869
## iter 30 value 418.127351
## iter 40 value 415.567926
## iter 50 value 413.774435
## iter 60 value 412.070590
## iter 70 value 411.148621
## iter 80 value 411.059204
## iter 90 value 411.042442
## iter 100 value 411.038740
## final value 411.038740
## stopped after 100 iterations
## # weights: 51
## initial value 641.121767
## iter 10 value 443.976979
## iter 20 value 422.231761
## iter 30 value 412.548307
## iter 40 value 402.609872
## iter 50 value 398.914578
## iter 60 value 393.589141
## iter 70 value 384.785050
## iter 80 value 384.389989
## iter 90 value 384.350036
## iter 100 value 384.297192
## final value 384.297192
## stopped after 100 iterations
## # weights: 11
## initial value 480.977029
## iter 10 value 422.158768
## iter 20 value 411.700831
## iter 30 value 409.893623
## iter 40 value 409.208196
## iter 50 value 408.658306
## iter 60 value 408.461882
## iter 70 value 408.449684
## iter 80 value 408.411639
## iter 90 value 408.410972
## final value 408.410490
## converged
## # weights: 31
## initial value 471.643815
## iter 10 value 419.246383
## iter 20 value 413.664633
## iter 30 value 406.611070
## iter 40 value 403.678381
## iter 50 value 403.635321
## iter 60 value 403.626169
## final value 403.625853
## converged
## # weights: 51
## initial value 590.839477
## iter 10 value 418.399131
## iter 20 value 408.510350
## iter 30 value 406.089106
## iter 40 value 404.824287
## iter 50 value 403.916131
## iter 60 value 400.265298
## iter 70 value 396.390769
## iter 80 value 396.241560
## final value 396.240534
## converged
## # weights: 11
## initial value 491.898400
## iter 10 value 446.741942
## iter 20 value 445.826471
## final value 445.814964
## converged
## # weights: 31
## initial value 453.734002
## iter 10 value 437.201686
## iter 20 value 421.541254
## iter 30 value 417.023127
## iter 40 value 410.345982
## iter 50 value 407.601877
## iter 60 value 406.679385
## iter 70 value 404.179806
## iter 80 value 391.382839
## iter 90 value 380.258011
## iter 100 value 355.698204
## final value 355.698204
## stopped after 100 iterations
## # weights: 51
## initial value 464.835480
## iter 10 value 424.909164
## iter 20 value 414.440831
## iter 30 value 406.282613
## iter 40 value 402.644779
## iter 50 value 398.427970
## iter 60 value 383.128857
## iter 70 value 360.279460
## iter 80 value 337.213390
## iter 90 value 329.036685
## iter 100 value 323.602435
## final value 323.602435
## stopped after 100 iterations
## # weights: 11
## initial value 458.419062
## iter 10 value 446.601035
## iter 20 value 443.441334
## iter 30 value 442.725666
## iter 40 value 442.260621
## iter 50 value 441.989520
## iter 60 value 441.824816
## iter 70 value 441.655783
## iter 80 value 441.648377
## iter 90 value 441.645542
## final value 441.645479
## converged
## # weights: 31
## initial value 460.905022
## iter 10 value 437.742374
## iter 20 value 432.808548
## iter 30 value 412.256028
## iter 40 value 398.125115
## iter 50 value 386.913269
## iter 60 value 383.247254
## iter 70 value 367.713080
## iter 80 value 322.420582
## iter 90 value 318.937740
## iter 100 value 317.902314
## final value 317.902314
## stopped after 100 iterations
## # weights: 51
## initial value 467.660589
## iter 10 value 424.732207
## iter 20 value 411.427191
## iter 30 value 406.807679
## iter 40 value 404.838230
## iter 50 value 404.319675
## iter 60 value 401.944785
## iter 70 value 400.053851
## iter 80 value 398.865895
## iter 90 value 398.266756
## iter 100 value 397.153081
## final value 397.153081
## stopped after 100 iterations
## # weights: 11
## initial value 577.378613
## final value 446.857145
## converged
## # weights: 31
## initial value 452.518258
## iter 10 value 431.318236
## iter 20 value 427.612105
## iter 30 value 426.204235
## iter 40 value 422.484227
## iter 50 value 422.348338
## final value 422.348118
## converged
## # weights: 51
## initial value 535.664098
## iter 10 value 444.243462
## iter 20 value 443.756305
## iter 30 value 443.390042
## iter 40 value 442.518831
## iter 50 value 442.306483
## final value 442.306140
## converged
## # weights: 11
## initial value 486.822174
## iter 10 value 446.733562
## iter 20 value 441.989445
## iter 30 value 427.132391
## iter 40 value 404.529964
## iter 50 value 395.429034
## iter 60 value 374.811852
## iter 70 value 335.081283
## iter 80 value 332.016353
## iter 90 value 330.869186
## final value 330.869181
## converged
## # weights: 31
## initial value 599.433720
## iter 10 value 447.584507
## iter 20 value 439.503502
## iter 30 value 436.296544
## iter 40 value 434.342483
## iter 50 value 434.342298
## iter 50 value 434.342298
## final value 434.342298
## converged
## # weights: 51
## initial value 589.646506
## iter 10 value 429.588136
## iter 20 value 420.325906
## iter 30 value 412.968766
## iter 40 value 405.560083
## iter 50 value 400.559442
## iter 60 value 394.583266
## iter 70 value 392.214778
## iter 80 value 386.997609
## iter 90 value 385.111631
## iter 100 value 376.804453
## final value 376.804453
## stopped after 100 iterations
## # weights: 11
## initial value 460.802737
## final value 446.857272
## converged
## # weights: 31
## initial value 676.335086
## iter 10 value 422.081752
## iter 20 value 410.034575
## iter 30 value 405.040400
## iter 40 value 402.155451
## iter 50 value 402.019489
## iter 60 value 402.012538
## iter 70 value 401.986179
## iter 80 value 401.985538
## iter 90 value 401.983176
## iter 100 value 401.979751
## final value 401.979751
## stopped after 100 iterations
## # weights: 51
## initial value 1128.109346
## iter 10 value 439.007265
## iter 20 value 419.401882
## iter 30 value 413.258485
## iter 40 value 408.782058
## iter 50 value 405.276633
## iter 60 value 402.799846
## iter 70 value 402.454540
## iter 80 value 402.419298
## iter 90 value 402.377765
## iter 100 value 401.892924
## final value 401.892924
## stopped after 100 iterations
## # weights: 11
## initial value 446.935475
## final value 446.857145
## converged
## # weights: 31
## initial value 434.140888
## iter 10 value 417.216631
## iter 20 value 409.956980
## iter 30 value 409.254094
## iter 40 value 408.478141
## iter 50 value 407.052731
## iter 60 value 406.929047
## iter 70 value 406.886876
## iter 80 value 406.855495
## final value 406.855434
## converged
## # weights: 51
## initial value 602.689488
## iter 10 value 419.588582
## iter 20 value 409.503167
## iter 30 value 403.707869
## iter 40 value 399.591862
## iter 50 value 396.514058
## iter 60 value 394.781865
## iter 70 value 394.706363
## final value 394.705851
## converged
## # weights: 11
## initial value 547.841667
## iter 10 value 441.205730
## iter 20 value 439.278433
## final value 439.276420
## converged
## # weights: 31
## initial value 556.142418
## iter 10 value 422.750331
## iter 20 value 409.828797
## iter 30 value 399.881550
## iter 40 value 395.900841
## iter 50 value 392.334954
## iter 60 value 382.142442
## iter 70 value 377.966241
## iter 80 value 372.805171
## iter 90 value 369.625732
## iter 100 value 359.860231
## final value 359.860231
## stopped after 100 iterations
## # weights: 51
## initial value 495.824258
## iter 10 value 441.408643
## iter 20 value 423.360694
## iter 30 value 414.116456
## iter 40 value 412.225079
## iter 50 value 405.246032
## iter 60 value 376.439055
## iter 70 value 325.562005
## iter 80 value 314.801554
## iter 90 value 311.426185
## iter 100 value 308.867863
## final value 308.867863
## stopped after 100 iterations
## # weights: 11
## initial value 445.676552
## iter 10 value 411.115078
## iter 20 value 401.400128
## iter 30 value 391.710119
## iter 40 value 368.711207
## iter 50 value 329.011353
## iter 60 value 321.740793
## iter 70 value 321.484384
## final value 321.278080
## converged
## # weights: 31
## initial value 520.768003
## iter 10 value 421.265364
## iter 20 value 406.850439
## iter 30 value 399.365753
## iter 40 value 397.261059
## iter 50 value 395.125755
## iter 60 value 387.925303
## iter 70 value 359.516378
## iter 80 value 346.891810
## iter 90 value 332.891899
## iter 100 value 323.235427
## final value 323.235427
## stopped after 100 iterations
## # weights: 51
## initial value 493.010393
## iter 10 value 415.532988
## iter 20 value 398.882486
## iter 30 value 395.973785
## iter 40 value 391.016035
## iter 50 value 385.795669
## iter 60 value 385.491131
## iter 70 value 385.317056
## iter 80 value 385.231805
## iter 90 value 385.196919
## iter 100 value 385.182324
## final value 385.182324
## stopped after 100 iterations
## # weights: 11
## initial value 564.807554
## final value 446.857145
## converged
## # weights: 31
## initial value 476.185417
## iter 10 value 412.433410
## iter 20 value 404.930123
## iter 30 value 376.253348
## iter 40 value 357.253756
## iter 50 value 349.381325
## iter 60 value 347.361900
## iter 70 value 342.378703
## iter 80 value 341.943964
## final value 341.943351
## converged
## # weights: 51
## initial value 509.785840
## iter 10 value 424.870080
## iter 20 value 413.039749
## iter 30 value 408.303668
## iter 40 value 404.609016
## iter 50 value 403.795122
## iter 60 value 403.699079
## iter 70 value 403.695538
## iter 80 value 403.692680
## iter 90 value 403.312871
## iter 100 value 403.262287
## final value 403.262287
## stopped after 100 iterations
## # weights: 11
## initial value 446.882049
## iter 10 value 424.995511
## iter 20 value 407.678846
## iter 30 value 403.833350
## iter 40 value 403.578361
## iter 40 value 403.578360
## iter 40 value 403.578357
## final value 403.578357
## converged
## # weights: 31
## initial value 449.425858
## iter 10 value 418.393600
## iter 20 value 416.810251
## iter 30 value 414.854074
## iter 40 value 408.598103
## iter 50 value 401.782214
## iter 60 value 397.489480
## iter 70 value 393.062545
## iter 80 value 389.499404
## iter 90 value 381.114157
## iter 100 value 377.326674
## final value 377.326674
## stopped after 100 iterations
## # weights: 51
## initial value 469.933455
## iter 10 value 439.689603
## iter 20 value 433.661086
## iter 30 value 418.862152
## iter 40 value 408.532863
## iter 50 value 402.148132
## iter 60 value 392.684538
## iter 70 value 385.776039
## iter 80 value 380.484468
## iter 90 value 375.482847
## iter 100 value 367.415260
## final value 367.415260
## stopped after 100 iterations
## # weights: 11
## initial value 647.216779
## final value 446.857388
## converged
## # weights: 31
## initial value 445.815400
## iter 10 value 439.364268
## iter 20 value 434.646836
## iter 30 value 432.625018
## iter 40 value 432.497451
## iter 50 value 432.382076
## iter 60 value 432.217708
## iter 70 value 432.204927
## iter 80 value 432.191835
## iter 90 value 432.010760
## iter 100 value 430.899317
## final value 430.899317
## stopped after 100 iterations
## # weights: 51
## initial value 462.374574
## iter 10 value 420.361284
## iter 20 value 411.253131
## iter 30 value 403.658619
## iter 40 value 398.120713
## iter 50 value 397.405879
## iter 60 value 396.493996
## iter 70 value 394.414129
## iter 80 value 394.320330
## iter 90 value 391.787695
## iter 100 value 351.595278
## final value 351.595278
## stopped after 100 iterations
## # weights: 11
## initial value 449.134723
## iter 10 value 431.862376
## iter 20 value 429.390531
## iter 30 value 429.017720
## iter 40 value 428.876044
## iter 50 value 428.849601
## final value 428.849533
## converged
## # weights: 31
## initial value 588.754557
## final value 446.857060
## converged
## # weights: 51
## initial value 498.696566
## iter 10 value 432.907258
## iter 20 value 428.763288
## iter 30 value 427.914253
## iter 40 value 427.910138
## final value 427.910131
## converged
## # weights: 11
## initial value 448.167677
## iter 10 value 437.407236
## iter 20 value 433.631897
## final value 433.593718
## converged
## # weights: 31
## initial value 615.302890
## iter 10 value 440.458180
## iter 20 value 436.298607
## iter 30 value 430.897582
## iter 40 value 417.870314
## iter 50 value 407.461300
## iter 60 value 399.104432
## iter 70 value 396.551350
## iter 80 value 395.199345
## iter 90 value 394.893502
## iter 100 value 394.098010
## final value 394.098010
## stopped after 100 iterations
## # weights: 51
## initial value 456.289150
## iter 10 value 433.406423
## iter 20 value 425.493744
## iter 30 value 420.083649
## iter 40 value 401.115955
## iter 50 value 390.725497
## iter 60 value 380.011160
## iter 70 value 374.719049
## iter 80 value 371.544131
## iter 90 value 370.525471
## iter 100 value 370.361162
## final value 370.361162
## stopped after 100 iterations
## # weights: 11
## initial value 635.646402
## iter 10 value 437.998303
## iter 20 value 433.521047
## iter 30 value 428.799503
## iter 40 value 426.811104
## iter 50 value 426.437028
## iter 60 value 426.433276
## iter 70 value 426.428989
## final value 426.427596
## converged
## # weights: 31
## initial value 526.150599
## iter 10 value 437.202210
## iter 20 value 432.819014
## iter 30 value 431.129597
## iter 40 value 430.794408
## iter 50 value 430.453486
## iter 60 value 430.358371
## iter 70 value 430.352096
## iter 80 value 430.298884
## iter 90 value 430.244518
## iter 100 value 430.240774
## final value 430.240774
## stopped after 100 iterations
## # weights: 51
## initial value 454.294193
## iter 10 value 434.517110
## iter 20 value 413.822121
## iter 30 value 402.596515
## iter 40 value 398.848942
## iter 50 value 397.261061
## iter 60 value 397.143457
## iter 70 value 397.068482
## iter 80 value 397.061520
## iter 90 value 397.058893
## iter 100 value 396.304866
## final value 396.304866
## stopped after 100 iterations
## # weights: 11
## initial value 461.121062
## final value 447.909139
## converged
## # weights: 31
## initial value 456.299140
## iter 10 value 432.033162
## iter 20 value 420.508779
## iter 30 value 402.281876
## iter 40 value 399.371852
## iter 50 value 389.296112
## iter 60 value 341.302316
## iter 70 value 325.486146
## iter 80 value 324.607917
## iter 90 value 324.577432
## iter 100 value 324.565841
## final value 324.565841
## stopped after 100 iterations
## # weights: 51
## initial value 538.277493
## iter 10 value 424.480466
## iter 20 value 420.504335
## iter 30 value 418.045963
## iter 40 value 414.347871
## iter 50 value 413.929046
## final value 413.928070
## converged
## # weights: 11
## initial value 457.071252
## iter 10 value 440.372320
## iter 20 value 434.615589
## iter 30 value 407.431388
## iter 40 value 393.836491
## iter 50 value 380.677542
## iter 60 value 336.350914
## iter 70 value 332.899716
## iter 80 value 332.434386
## iter 80 value 332.434386
## iter 80 value 332.434386
## final value 332.434386
## converged
## # weights: 31
## initial value 505.881688
## iter 10 value 444.727309
## iter 20 value 433.318124
## iter 30 value 427.925773
## iter 40 value 425.951028
## iter 50 value 424.912831
## iter 60 value 423.041916
## iter 70 value 410.143905
## iter 80 value 402.637775
## iter 90 value 398.544203
## iter 100 value 395.554427
## final value 395.554427
## stopped after 100 iterations
## # weights: 51
## initial value 460.552216
## iter 10 value 440.553654
## iter 20 value 415.864433
## iter 30 value 409.819683
## iter 40 value 407.345656
## iter 50 value 401.230720
## iter 60 value 399.030123
## iter 70 value 396.226967
## iter 80 value 393.616381
## iter 90 value 386.817756
## iter 100 value 381.486720
## final value 381.486720
## stopped after 100 iterations
## # weights: 11
## initial value 552.839437
## final value 447.912460
## converged
## # weights: 31
## initial value 525.984989
## iter 10 value 447.906713
## iter 20 value 447.770171
## iter 30 value 444.944533
## iter 40 value 442.730420
## iter 50 value 441.219689
## iter 60 value 440.244919
## iter 70 value 440.218397
## iter 80 value 440.212735
## iter 90 value 440.146670
## iter 100 value 440.011450
## final value 440.011450
## stopped after 100 iterations
## # weights: 51
## initial value 455.287782
## iter 10 value 430.578157
## iter 20 value 409.307625
## iter 30 value 380.885706
## iter 40 value 340.506126
## iter 50 value 332.614107
## iter 60 value 332.395192
## iter 70 value 331.618810
## iter 80 value 328.617279
## iter 90 value 323.366714
## iter 100 value 318.104271
## final value 318.104271
## stopped after 100 iterations
## # weights: 11
## initial value 489.960666
## final value 446.857145
## converged
## # weights: 31
## initial value 553.910903
## iter 10 value 427.773129
## iter 20 value 413.715243
## iter 30 value 405.659487
## iter 40 value 397.137770
## iter 50 value 390.446917
## iter 60 value 383.319967
## iter 70 value 379.682795
## iter 80 value 360.266931
## iter 90 value 324.596439
## iter 100 value 318.226521
## final value 318.226521
## stopped after 100 iterations
## # weights: 51
## initial value 546.371646
## iter 10 value 437.483436
## iter 20 value 427.224728
## iter 30 value 419.966214
## iter 40 value 416.039244
## iter 50 value 414.677370
## iter 60 value 412.034555
## iter 70 value 409.602025
## iter 80 value 409.588600
## final value 409.588580
## converged
## # weights: 11
## initial value 514.990097
## iter 10 value 446.757920
## iter 20 value 442.561765
## iter 30 value 434.193877
## iter 40 value 421.581274
## iter 50 value 407.765903
## iter 60 value 398.981998
## iter 70 value 378.372570
## iter 80 value 336.242478
## iter 90 value 334.279033
## iter 100 value 334.276434
## final value 334.276434
## stopped after 100 iterations
## # weights: 31
## initial value 454.806271
## iter 10 value 444.379615
## iter 20 value 414.345997
## iter 30 value 401.457757
## iter 40 value 390.121688
## iter 50 value 349.916301
## iter 60 value 332.814104
## iter 70 value 329.835615
## iter 80 value 327.366844
## iter 90 value 326.520265
## iter 100 value 326.397399
## final value 326.397399
## stopped after 100 iterations
## # weights: 51
## initial value 507.126408
## iter 10 value 433.238977
## iter 20 value 424.662740
## iter 30 value 419.718494
## iter 40 value 417.309994
## iter 50 value 399.789324
## iter 60 value 369.791384
## iter 70 value 358.909154
## iter 80 value 330.797105
## iter 90 value 327.282684
## iter 100 value 325.210773
## final value 325.210773
## stopped after 100 iterations
## # weights: 11
## initial value 527.491263
## final value 446.857427
## converged
## # weights: 31
## initial value 447.973661
## iter 10 value 417.386725
## iter 20 value 397.775358
## iter 30 value 371.328055
## iter 40 value 343.327511
## iter 50 value 338.428305
## iter 60 value 336.642849
## iter 70 value 332.558265
## iter 80 value 326.647081
## iter 90 value 325.493327
## iter 100 value 325.489592
## final value 325.489592
## stopped after 100 iterations
## # weights: 51
## initial value 452.358955
## iter 10 value 411.695971
## iter 20 value 402.276221
## iter 30 value 400.552309
## iter 40 value 398.453088
## iter 50 value 393.245498
## iter 60 value 392.288695
## iter 70 value 391.417210
## iter 80 value 379.793311
## iter 90 value 368.384773
## iter 100 value 364.775931
## final value 364.775931
## stopped after 100 iterations
## # weights: 11
## initial value 456.209236
## iter 10 value 443.021271
## iter 20 value 437.490142
## iter 30 value 432.877753
## iter 40 value 432.007813
## iter 50 value 428.256693
## iter 60 value 424.323969
## iter 70 value 414.060254
## iter 80 value 406.954386
## iter 90 value 403.602743
## iter 100 value 403.386588
## final value 403.386588
## stopped after 100 iterations
## # weights: 31
## initial value 486.338409
## iter 10 value 439.533469
## iter 20 value 430.219693
## iter 30 value 427.451944
## iter 40 value 425.150778
## iter 50 value 424.376376
## iter 60 value 423.468036
## iter 70 value 421.439934
## iter 80 value 421.087006
## iter 90 value 421.001957
## iter 100 value 420.993581
## final value 420.993581
## stopped after 100 iterations
## # weights: 51
## initial value 543.767135
## iter 10 value 434.695108
## iter 20 value 427.222854
## iter 30 value 418.701552
## iter 40 value 411.934396
## iter 50 value 403.244647
## iter 60 value 399.896960
## iter 70 value 398.884436
## iter 80 value 398.682980
## iter 90 value 398.283540
## iter 100 value 398.050536
## final value 398.050536
## stopped after 100 iterations
## # weights: 11
## initial value 445.620060
## iter 10 value 440.539898
## iter 20 value 439.621541
## final value 439.619191
## converged
## # weights: 31
## initial value 486.576370
## iter 10 value 437.353567
## iter 20 value 428.241429
## iter 30 value 425.806029
## iter 40 value 425.618887
## iter 50 value 425.601008
## iter 60 value 425.565412
## iter 70 value 425.561212
## iter 80 value 423.469466
## iter 90 value 411.118013
## iter 100 value 402.888850
## final value 402.888850
## stopped after 100 iterations
## # weights: 51
## initial value 461.143479
## iter 10 value 430.868810
## iter 20 value 420.017951
## iter 30 value 407.071822
## iter 40 value 401.674029
## iter 50 value 395.702821
## iter 60 value 386.430152
## iter 70 value 373.086043
## iter 80 value 364.638418
## iter 90 value 352.761469
## iter 100 value 345.570275
## final value 345.570275
## stopped after 100 iterations
## # weights: 11
## initial value 446.908544
## final value 446.857365
## converged
## # weights: 31
## initial value 462.730993
## iter 10 value 446.187014
## iter 20 value 435.433213
## iter 30 value 420.841472
## iter 40 value 413.354382
## iter 50 value 411.198418
## iter 60 value 410.897011
## iter 70 value 410.321368
## iter 80 value 409.858476
## iter 90 value 409.292027
## iter 100 value 409.273661
## final value 409.273661
## stopped after 100 iterations
## # weights: 51
## initial value 449.961050
## iter 10 value 431.813390
## iter 20 value 414.487333
## iter 30 value 406.908323
## iter 40 value 403.981187
## iter 50 value 400.872070
## iter 60 value 398.032174
## iter 70 value 390.495166
## iter 80 value 388.984924
## iter 90 value 387.962914
## iter 100 value 386.717743
## final value 386.717743
## stopped after 100 iterations
## # weights: 11
## initial value 477.564533
## iter 10 value 422.220104
## iter 20 value 413.233349
## iter 30 value 410.833133
## iter 40 value 409.888699
## iter 50 value 409.206653
## iter 60 value 396.054622
## iter 70 value 378.246075
## iter 80 value 350.206814
## iter 90 value 342.480698
## iter 100 value 342.322511
## final value 342.322511
## stopped after 100 iterations
## # weights: 31
## initial value 568.478439
## iter 10 value 434.968124
## iter 20 value 421.396046
## iter 30 value 417.013054
## iter 40 value 415.836157
## iter 50 value 415.009031
## iter 60 value 414.408188
## iter 70 value 414.050181
## iter 80 value 414.046683
## iter 80 value 414.046680
## iter 80 value 414.046680
## final value 414.046680
## converged
## # weights: 51
## initial value 455.345919
## iter 10 value 432.938911
## iter 20 value 415.016338
## iter 30 value 404.421711
## iter 40 value 400.193604
## iter 50 value 394.655432
## iter 60 value 388.246708
## iter 70 value 385.224408
## iter 80 value 384.397633
## iter 90 value 384.392126
## iter 90 value 384.392123
## iter 90 value 384.392123
## final value 384.392123
## converged
## # weights: 11
## initial value 499.035460
## iter 10 value 446.878799
## final value 446.878791
## converged
## # weights: 31
## initial value 447.494397
## iter 10 value 428.836419
## iter 20 value 419.681901
## iter 30 value 416.233531
## iter 40 value 413.170960
## iter 50 value 402.772720
## iter 60 value 392.924812
## iter 70 value 381.319947
## iter 80 value 350.461964
## iter 90 value 339.292856
## iter 100 value 324.115391
## final value 324.115391
## stopped after 100 iterations
## # weights: 51
## initial value 450.304258
## iter 10 value 425.197305
## iter 20 value 409.754161
## iter 30 value 403.474012
## iter 40 value 399.134387
## iter 50 value 394.675617
## iter 60 value 389.870707
## iter 70 value 384.274732
## iter 80 value 378.826063
## iter 90 value 375.760494
## iter 100 value 372.107635
## final value 372.107635
## stopped after 100 iterations
## # weights: 11
## initial value 459.102093
## final value 446.857374
## converged
## # weights: 31
## initial value 475.248953
## iter 10 value 444.199220
## iter 20 value 402.479471
## iter 30 value 396.740859
## iter 40 value 391.374141
## iter 50 value 388.934559
## iter 60 value 388.871138
## iter 70 value 388.862847
## iter 80 value 388.855867
## iter 90 value 388.817980
## iter 100 value 388.813221
## final value 388.813221
## stopped after 100 iterations
## # weights: 51
## initial value 439.904213
## iter 10 value 422.370429
## iter 20 value 417.993745
## iter 30 value 415.354166
## iter 40 value 415.177741
## iter 50 value 415.131812
## iter 60 value 415.121128
## final value 415.120511
## converged
## # weights: 11
## initial value 454.001973
## iter 10 value 435.215934
## iter 20 value 414.878044
## iter 30 value 404.202174
## iter 40 value 386.390966
## iter 50 value 338.196924
## iter 60 value 327.143398
## iter 70 value 324.156589
## iter 80 value 322.394431
## iter 90 value 322.353439
## final value 322.326416
## converged
## # weights: 31
## initial value 481.496343
## iter 10 value 438.239253
## iter 20 value 417.587424
## iter 30 value 406.099842
## iter 40 value 398.541862
## iter 50 value 395.993818
## iter 60 value 395.780127
## final value 395.779559
## converged
## # weights: 51
## initial value 528.698625
## iter 10 value 436.825011
## iter 20 value 427.877535
## iter 30 value 404.346203
## iter 40 value 394.548274
## iter 50 value 390.589152
## iter 60 value 388.094114
## iter 70 value 387.135054
## iter 80 value 386.554688
## iter 90 value 386.475814
## final value 386.475689
## converged
## # weights: 11
## initial value 457.874091
## iter 10 value 427.450246
## iter 20 value 413.795650
## iter 30 value 407.926119
## iter 40 value 404.843392
## iter 50 value 404.396915
## final value 404.396665
## converged
## # weights: 31
## initial value 573.997701
## iter 10 value 445.501282
## iter 20 value 421.036846
## iter 30 value 403.161105
## iter 40 value 395.182678
## iter 50 value 392.555403
## iter 60 value 386.712153
## iter 70 value 383.053923
## iter 80 value 377.850907
## iter 90 value 346.022278
## iter 100 value 327.588493
## final value 327.588493
## stopped after 100 iterations
## # weights: 51
## initial value 466.729650
## iter 10 value 425.501082
## iter 20 value 406.590343
## iter 30 value 401.299329
## iter 40 value 397.187789
## iter 50 value 395.817748
## iter 60 value 394.628952
## iter 70 value 392.839763
## iter 80 value 391.995960
## iter 90 value 391.107315
## iter 100 value 390.638459
## final value 390.638459
## stopped after 100 iterations
## # weights: 11
## initial value 543.737899
## iter 10 value 447.962752
## iter 20 value 444.057941
## iter 30 value 439.095700
## iter 40 value 437.954811
## iter 50 value 437.862117
## iter 60 value 437.857691
## final value 437.856981
## converged
## # weights: 31
## initial value 608.872532
## iter 10 value 415.268149
## iter 20 value 407.651745
## iter 30 value 406.143680
## iter 40 value 405.937460
## iter 50 value 405.567536
## iter 60 value 405.251293
## iter 70 value 404.674855
## iter 80 value 404.614900
## iter 90 value 404.502993
## iter 100 value 404.268035
## final value 404.268035
## stopped after 100 iterations
## # weights: 51
## initial value 453.476155
## iter 10 value 422.780021
## iter 20 value 400.492700
## iter 30 value 395.756509
## iter 40 value 391.339081
## iter 50 value 388.608145
## iter 60 value 385.302579
## iter 70 value 384.729260
## iter 80 value 384.695060
## iter 90 value 384.661706
## iter 100 value 384.559362
## final value 384.559362
## stopped after 100 iterations
## # weights: 11
## initial value 476.585804
## final value 446.857145
## converged
## # weights: 31
## initial value 454.826080
## iter 10 value 420.787641
## iter 20 value 416.658503
## iter 30 value 414.713045
## iter 40 value 413.869125
## iter 50 value 413.864435
## final value 413.864422
## converged
## # weights: 51
## initial value 678.294219
## iter 10 value 442.727977
## iter 20 value 418.599343
## iter 30 value 409.108523
## iter 40 value 403.751315
## iter 50 value 403.236725
## iter 60 value 402.612372
## iter 70 value 402.549307
## iter 80 value 402.241762
## iter 90 value 402.202243
## iter 100 value 402.193590
## final value 402.193590
## stopped after 100 iterations
## # weights: 11
## initial value 467.286492
## iter 10 value 446.881557
## final value 446.878833
## converged
## # weights: 31
## initial value 464.262648
## iter 10 value 436.223022
## iter 20 value 434.446471
## iter 30 value 428.795401
## iter 40 value 415.295783
## iter 50 value 409.948132
## iter 60 value 404.545421
## iter 70 value 399.942890
## iter 80 value 393.155392
## iter 90 value 388.295170
## iter 100 value 386.229701
## final value 386.229701
## stopped after 100 iterations
## # weights: 51
## initial value 702.289551
## iter 10 value 438.332221
## iter 20 value 423.690886
## iter 30 value 408.244299
## iter 40 value 393.372658
## iter 50 value 384.072820
## iter 60 value 348.608982
## iter 70 value 318.397230
## iter 80 value 312.754607
## iter 90 value 311.297388
## iter 100 value 309.790371
## final value 309.790371
## stopped after 100 iterations
## # weights: 11
## initial value 562.266369
## final value 446.857291
## converged
## # weights: 31
## initial value 490.948957
## iter 10 value 446.464027
## iter 20 value 444.451956
## iter 30 value 442.461973
## iter 40 value 439.083171
## iter 50 value 437.813643
## iter 60 value 437.054313
## iter 70 value 430.384779
## iter 80 value 398.797638
## iter 90 value 395.170619
## iter 100 value 390.234709
## final value 390.234709
## stopped after 100 iterations
## # weights: 51
## initial value 465.090636
## iter 10 value 442.827393
## iter 20 value 435.809124
## iter 30 value 433.094306
## iter 40 value 429.681159
## iter 50 value 425.903123
## iter 60 value 425.117011
## iter 70 value 424.110733
## iter 80 value 422.210979
## iter 90 value 421.812351
## iter 100 value 421.669531
## final value 421.669531
## stopped after 100 iterations
## # weights: 11
## initial value 452.552357
## final value 446.857145
## converged
## # weights: 31
## initial value 478.657695
## iter 10 value 420.769903
## iter 20 value 400.083878
## iter 30 value 395.475051
## iter 40 value 392.955818
## iter 50 value 388.167647
## iter 60 value 387.723334
## final value 387.720810
## converged
## # weights: 51
## initial value 450.917360
## iter 10 value 433.832703
## iter 20 value 427.983661
## iter 30 value 424.609560
## iter 40 value 423.371479
## iter 50 value 422.384319
## iter 60 value 418.197114
## iter 70 value 415.515471
## iter 80 value 411.698403
## iter 90 value 410.535368
## iter 100 value 408.819631
## final value 408.819631
## stopped after 100 iterations
## # weights: 11
## initial value 452.636095
## iter 10 value 438.005324
## iter 20 value 417.105688
## iter 30 value 408.604363
## iter 40 value 394.272432
## iter 50 value 374.943210
## iter 60 value 333.107829
## iter 70 value 331.751832
## final value 331.482149
## converged
## # weights: 31
## initial value 465.977865
## iter 10 value 439.582253
## iter 20 value 422.167819
## iter 30 value 412.038368
## iter 40 value 402.196694
## iter 50 value 395.158049
## iter 60 value 392.987193
## iter 70 value 363.185338
## iter 80 value 337.614268
## iter 90 value 329.864700
## iter 100 value 328.193624
## final value 328.193624
## stopped after 100 iterations
## # weights: 51
## initial value 450.847468
## iter 10 value 437.470938
## iter 20 value 405.766112
## iter 30 value 397.447081
## iter 40 value 395.372675
## iter 50 value 394.940452
## iter 60 value 392.839184
## iter 70 value 389.622424
## iter 80 value 388.698056
## iter 90 value 382.876808
## iter 100 value 374.045692
## final value 374.045692
## stopped after 100 iterations
## # weights: 11
## initial value 487.265955
## iter 10 value 422.962941
## iter 20 value 407.581332
## iter 30 value 404.260895
## iter 40 value 403.157148
## iter 50 value 403.151120
## iter 60 value 403.142613
## final value 403.142539
## converged
## # weights: 31
## initial value 465.394310
## iter 10 value 436.500560
## iter 20 value 431.857781
## iter 30 value 428.249183
## iter 40 value 425.290470
## iter 50 value 422.956365
## iter 60 value 420.118862
## iter 70 value 415.352161
## iter 80 value 415.174873
## iter 90 value 415.099669
## iter 100 value 414.743994
## final value 414.743994
## stopped after 100 iterations
## # weights: 51
## initial value 496.847822
## iter 10 value 435.016819
## iter 20 value 408.833239
## iter 30 value 401.837636
## iter 40 value 395.682089
## iter 50 value 395.024185
## iter 60 value 395.001111
## iter 70 value 394.979899
## iter 80 value 394.916009
## iter 90 value 394.636086
## iter 100 value 394.565549
## final value 394.565549
## stopped after 100 iterations
## # weights: 11
## initial value 464.688032
## final value 446.857145
## converged
## # weights: 31
## initial value 456.154042
## iter 10 value 445.223229
## final value 445.187095
## converged
## # weights: 51
## initial value 440.047189
## iter 10 value 417.790854
## iter 20 value 403.783173
## iter 30 value 391.933791
## iter 40 value 390.266271
## iter 50 value 389.761695
## iter 60 value 388.343123
## iter 70 value 387.304659
## iter 80 value 386.492129
## iter 90 value 386.386183
## iter 100 value 385.923804
## final value 385.923804
## stopped after 100 iterations
## # weights: 11
## initial value 606.391007
## iter 10 value 447.389173
## iter 20 value 446.878869
## final value 446.878838
## converged
## # weights: 31
## initial value 516.593165
## iter 10 value 437.038390
## iter 20 value 416.562842
## iter 30 value 403.743680
## iter 40 value 387.435176
## iter 50 value 344.505984
## iter 60 value 328.980478
## iter 70 value 326.257450
## iter 80 value 323.934960
## iter 90 value 322.938464
## iter 100 value 320.622679
## final value 320.622679
## stopped after 100 iterations
## # weights: 51
## initial value 454.006140
## iter 10 value 416.576239
## iter 20 value 402.830097
## iter 30 value 393.258849
## iter 40 value 389.847486
## iter 50 value 386.710987
## iter 60 value 383.547971
## iter 70 value 383.287889
## iter 80 value 383.268783
## iter 90 value 383.262485
## iter 100 value 383.183043
## final value 383.183043
## stopped after 100 iterations
## # weights: 11
## initial value 484.208447
## final value 446.857573
## converged
## # weights: 31
## initial value 444.535324
## iter 10 value 435.806020
## iter 20 value 435.144289
## iter 30 value 433.124155
## iter 40 value 432.562273
## iter 50 value 432.533178
## iter 60 value 432.517172
## iter 70 value 431.118366
## iter 80 value 430.750022
## iter 90 value 430.042147
## iter 100 value 430.036024
## final value 430.036024
## stopped after 100 iterations
## # weights: 51
## initial value 462.972259
## iter 10 value 444.262829
## iter 20 value 429.778941
## iter 30 value 424.418539
## iter 40 value 421.807859
## iter 50 value 419.694458
## iter 60 value 415.291456
## iter 70 value 412.542552
## iter 80 value 411.586407
## iter 90 value 411.440140
## iter 100 value 410.919483
## final value 410.919483
## stopped after 100 iterations
## # weights: 11
## initial value 456.048011
## final value 446.857145
## converged
## # weights: 31
## initial value 569.448624
## iter 10 value 417.145096
## iter 20 value 412.965376
## iter 30 value 408.629019
## iter 40 value 408.186694
## iter 50 value 408.157705
## iter 60 value 408.155384
## iter 70 value 408.154844
## iter 80 value 406.180445
## iter 90 value 405.621870
## iter 100 value 405.150588
## final value 405.150588
## stopped after 100 iterations
## # weights: 51
## initial value 474.002013
## iter 10 value 434.370773
## iter 20 value 431.256838
## iter 30 value 429.014622
## iter 40 value 428.131674
## iter 50 value 427.718457
## iter 60 value 427.668682
## iter 70 value 427.554374
## iter 80 value 427.221019
## iter 90 value 426.911298
## iter 100 value 426.113541
## final value 426.113541
## stopped after 100 iterations
## # weights: 11
## initial value 485.935997
## iter 10 value 443.989412
## iter 20 value 441.963057
## iter 30 value 440.338875
## iter 40 value 438.291399
## iter 50 value 438.068452
## iter 50 value 438.068450
## iter 50 value 438.068450
## final value 438.068450
## converged
## # weights: 31
## initial value 538.693546
## iter 10 value 446.627045
## iter 20 value 435.497348
## iter 30 value 431.211185
## iter 40 value 430.587129
## iter 50 value 429.757381
## iter 60 value 425.690021
## iter 70 value 422.700883
## iter 80 value 407.405296
## iter 90 value 404.478244
## iter 100 value 398.068451
## final value 398.068451
## stopped after 100 iterations
## # weights: 51
## initial value 564.126672
## iter 10 value 440.015939
## iter 20 value 405.365786
## iter 30 value 387.896634
## iter 40 value 378.971796
## iter 50 value 369.805080
## iter 60 value 343.773686
## iter 70 value 320.125625
## iter 80 value 305.738138
## iter 90 value 302.688825
## iter 100 value 301.497193
## final value 301.497193
## stopped after 100 iterations
## # weights: 11
## initial value 495.701664
## final value 446.857488
## converged
## # weights: 31
## initial value 532.140028
## iter 10 value 424.507691
## iter 20 value 412.845049
## iter 30 value 404.910643
## iter 40 value 400.371159
## iter 50 value 396.415167
## iter 60 value 394.852574
## iter 70 value 394.470643
## iter 80 value 394.332870
## iter 90 value 394.249695
## iter 100 value 394.229520
## final value 394.229520
## stopped after 100 iterations
## # weights: 51
## initial value 448.264892
## iter 10 value 421.819460
## iter 20 value 411.713082
## iter 30 value 401.684783
## iter 40 value 391.848673
## iter 50 value 390.620440
## iter 60 value 387.721876
## iter 70 value 370.797968
## iter 80 value 348.709407
## iter 90 value 324.603791
## iter 100 value 312.439402
## final value 312.439402
## stopped after 100 iterations
## # weights: 11
## initial value 459.774512
## final value 446.857145
## converged
## # weights: 31
## initial value 541.474120
## final value 446.857145
## converged
## # weights: 51
## initial value 477.724411
## iter 10 value 441.149485
## iter 20 value 436.286332
## iter 30 value 432.769200
## iter 40 value 429.447487
## iter 50 value 427.944807
## iter 60 value 426.031184
## iter 70 value 424.310482
## iter 80 value 424.276779
## final value 424.276659
## converged
## # weights: 11
## initial value 498.161014
## iter 10 value 447.052721
## iter 20 value 446.868841
## iter 30 value 443.034077
## iter 40 value 425.495237
## iter 50 value 423.978115
## iter 60 value 415.703746
## iter 70 value 403.082553
## iter 80 value 391.279447
## iter 90 value 381.952827
## iter 100 value 333.469356
## final value 333.469356
## stopped after 100 iterations
## # weights: 31
## initial value 539.231081
## iter 10 value 437.313557
## iter 20 value 426.218396
## iter 30 value 397.909834
## iter 40 value 393.521978
## iter 50 value 392.574144
## iter 60 value 389.507300
## iter 70 value 345.887224
## iter 80 value 332.649076
## iter 90 value 326.613663
## iter 100 value 324.542229
## final value 324.542229
## stopped after 100 iterations
## # weights: 51
## initial value 511.104946
## iter 10 value 422.325352
## iter 20 value 414.689376
## iter 30 value 411.112942
## iter 40 value 407.988673
## iter 50 value 406.025467
## iter 60 value 402.553710
## iter 70 value 398.934556
## iter 80 value 381.780125
## iter 90 value 341.454855
## iter 100 value 330.793378
## final value 330.793378
## stopped after 100 iterations
## # weights: 11
## initial value 658.358101
## final value 446.858469
## converged
## # weights: 31
## initial value 591.475497
## final value 446.858968
## converged
## # weights: 51
## initial value 459.271369
## iter 10 value 440.347235
## iter 20 value 437.701104
## iter 30 value 434.688333
## iter 40 value 434.188170
## iter 50 value 434.046179
## iter 60 value 433.748635
## iter 70 value 433.547757
## iter 80 value 433.281058
## iter 90 value 433.032289
## iter 100 value 432.977753
## final value 432.977753
## stopped after 100 iterations
## # weights: 11
## initial value 554.722872
## iter 10 value 440.277230
## iter 20 value 436.645780
## iter 30 value 401.113768
## iter 40 value 397.987989
## iter 50 value 397.033569
## iter 60 value 395.621067
## iter 70 value 395.518415
## iter 80 value 395.367664
## iter 90 value 395.359969
## iter 100 value 395.314813
## final value 395.314813
## stopped after 100 iterations
## # weights: 31
## initial value 496.012530
## final value 446.857145
## converged
## # weights: 51
## initial value 673.033702
## iter 10 value 425.747675
## iter 20 value 412.674634
## iter 30 value 407.521331
## iter 40 value 403.961267
## iter 50 value 402.943203
## iter 60 value 401.439247
## iter 70 value 401.301053
## iter 80 value 401.298206
## final value 401.298163
## converged
## # weights: 11
## initial value 469.775956
## iter 10 value 440.539094
## iter 20 value 439.697681
## iter 30 value 439.324320
## iter 40 value 438.963140
## iter 50 value 438.959474
## final value 438.959193
## converged
## # weights: 31
## initial value 447.385333
## iter 10 value 441.014097
## iter 20 value 431.550136
## iter 30 value 411.971332
## iter 40 value 402.362157
## iter 50 value 394.881706
## iter 60 value 390.139954
## iter 70 value 388.759127
## iter 80 value 388.557606
## iter 90 value 388.545150
## final value 388.545126
## converged
## # weights: 51
## initial value 465.038649
## iter 10 value 423.415906
## iter 20 value 409.255400
## iter 30 value 404.365760
## iter 40 value 400.582426
## iter 50 value 388.562783
## iter 60 value 348.913317
## iter 70 value 329.752398
## iter 80 value 323.922120
## iter 90 value 322.513936
## iter 100 value 321.174934
## final value 321.174934
## stopped after 100 iterations
## # weights: 11
## initial value 624.373844
## final value 446.858026
## converged
## # weights: 31
## initial value 535.326939
## iter 10 value 429.302122
## iter 20 value 410.434806
## iter 30 value 398.636549
## iter 40 value 359.014970
## iter 50 value 335.578869
## iter 60 value 326.908527
## iter 70 value 325.347763
## iter 80 value 325.309123
## final value 325.309085
## converged
## # weights: 51
## initial value 442.903145
## iter 10 value 432.436052
## iter 20 value 416.040213
## iter 30 value 404.442381
## iter 40 value 401.766597
## iter 50 value 400.656876
## iter 60 value 398.752306
## iter 70 value 398.378101
## iter 80 value 398.347556
## iter 90 value 398.190127
## iter 100 value 398.036393
## final value 398.036393
## stopped after 100 iterations
## # weights: 11
## initial value 459.112139
## final value 447.881630
## converged
## # weights: 31
## initial value 449.872601
## iter 10 value 415.397249
## iter 20 value 402.248836
## iter 30 value 396.558350
## iter 40 value 393.076709
## iter 50 value 388.391024
## iter 60 value 385.865132
## iter 70 value 385.442540
## iter 80 value 385.414093
## iter 90 value 385.389347
## iter 100 value 385.347873
## final value 385.347873
## stopped after 100 iterations
## # weights: 51
## initial value 1134.992217
## iter 10 value 442.007313
## iter 20 value 420.561882
## iter 30 value 399.604544
## iter 40 value 357.465722
## iter 50 value 337.141793
## iter 60 value 331.855382
## iter 70 value 321.832228
## iter 80 value 315.505043
## iter 90 value 314.781943
## iter 100 value 314.377739
## final value 314.377739
## stopped after 100 iterations
## # weights: 11
## initial value 475.491955
## iter 10 value 446.569596
## iter 20 value 436.304723
## iter 30 value 434.569168
## iter 40 value 433.901855
## final value 433.823217
## converged
## # weights: 31
## initial value 555.249569
## iter 10 value 444.097792
## iter 20 value 438.426083
## iter 30 value 434.550408
## iter 40 value 422.173462
## iter 50 value 415.027984
## iter 60 value 407.612235
## iter 70 value 402.878635
## iter 80 value 386.034851
## iter 90 value 341.654512
## iter 100 value 330.427787
## final value 330.427787
## stopped after 100 iterations
## # weights: 51
## initial value 643.193026
## iter 10 value 443.575942
## iter 20 value 434.767336
## iter 30 value 433.586693
## iter 40 value 431.953388
## iter 50 value 425.116927
## iter 60 value 399.699384
## iter 70 value 395.796295
## iter 80 value 393.977090
## iter 90 value 386.495732
## iter 100 value 378.717112
## final value 378.717112
## stopped after 100 iterations
## # weights: 11
## initial value 540.235612
## final value 447.909585
## converged
## # weights: 31
## initial value 550.692098
## iter 10 value 443.166609
## iter 20 value 421.340234
## iter 30 value 416.638191
## iter 40 value 406.838168
## iter 50 value 402.781649
## iter 60 value 401.411389
## iter 70 value 400.175004
## iter 80 value 399.246279
## iter 90 value 398.965430
## iter 100 value 398.854116
## final value 398.854116
## stopped after 100 iterations
## # weights: 51
## initial value 641.882022
## iter 10 value 445.052280
## iter 20 value 426.088445
## iter 30 value 420.298855
## iter 40 value 416.673170
## iter 50 value 408.900289
## iter 60 value 398.777614
## iter 70 value 390.749681
## iter 80 value 379.686043
## iter 90 value 363.768080
## iter 100 value 329.111540
## final value 329.111540
## stopped after 100 iterations
## # weights: 11
## initial value 446.177754
## iter 10 value 444.377753
## iter 20 value 444.354596
## iter 30 value 443.954092
## iter 40 value 442.627054
## iter 50 value 442.310084
## iter 60 value 442.304845
## iter 70 value 442.303736
## final value 442.303724
## converged
## # weights: 31
## initial value 494.552656
## iter 10 value 446.828719
## final value 446.828624
## converged
## # weights: 51
## initial value 555.841066
## iter 10 value 436.696639
## iter 20 value 428.164958
## iter 30 value 411.000627
## iter 40 value 396.395187
## iter 50 value 390.638263
## iter 60 value 384.969145
## iter 70 value 370.619867
## iter 80 value 348.941147
## iter 90 value 313.551261
## iter 100 value 306.646970
## final value 306.646970
## stopped after 100 iterations
## # weights: 11
## initial value 535.076216
## iter 10 value 446.366497
## iter 20 value 416.682302
## iter 30 value 408.652574
## iter 40 value 405.488625
## iter 50 value 404.460551
## iter 60 value 404.091122
## final value 404.090770
## converged
## # weights: 31
## initial value 501.763220
## iter 10 value 446.178992
## iter 20 value 420.705166
## iter 30 value 416.504611
## iter 40 value 413.612710
## iter 50 value 406.598226
## iter 60 value 403.031670
## iter 70 value 402.571185
## iter 80 value 401.801427
## iter 90 value 398.835359
## iter 100 value 396.123323
## final value 396.123323
## stopped after 100 iterations
## # weights: 51
## initial value 449.542920
## iter 10 value 436.442649
## iter 20 value 408.622732
## iter 30 value 396.444474
## iter 40 value 390.124517
## iter 50 value 382.046442
## iter 60 value 372.562920
## iter 70 value 354.133001
## iter 80 value 324.453356
## iter 90 value 319.810100
## iter 100 value 317.394809
## final value 317.394809
## stopped after 100 iterations
## # weights: 11
## initial value 532.324761
## final value 446.857374
## converged
## # weights: 31
## initial value 448.759268
## iter 10 value 426.013282
## iter 20 value 404.653299
## iter 30 value 401.710218
## iter 40 value 400.086657
## iter 50 value 399.526248
## iter 60 value 399.486017
## iter 70 value 399.483906
## iter 80 value 399.480581
## iter 90 value 399.471281
## iter 100 value 399.103754
## final value 399.103754
## stopped after 100 iterations
## # weights: 51
## initial value 487.777340
## iter 10 value 441.831011
## iter 20 value 434.676480
## iter 30 value 421.388513
## iter 40 value 413.091438
## iter 50 value 410.692558
## iter 60 value 408.400131
## iter 70 value 408.289758
## iter 80 value 408.228912
## iter 90 value 408.198774
## iter 100 value 408.071933
## final value 408.071933
## stopped after 100 iterations
## # weights: 11
## initial value 519.792668
## final value 446.828630
## converged
## # weights: 31
## initial value 682.648859
## iter 10 value 416.805654
## iter 20 value 402.768701
## iter 30 value 395.892374
## iter 40 value 393.316849
## iter 50 value 388.623001
## iter 60 value 387.778952
## final value 387.778674
## converged
## # weights: 51
## initial value 566.596641
## iter 10 value 439.574605
## iter 20 value 429.964534
## iter 30 value 412.393988
## iter 40 value 405.305310
## iter 50 value 403.705389
## iter 60 value 402.686638
## iter 70 value 402.131417
## iter 80 value 401.738095
## iter 90 value 401.734376
## final value 401.734358
## converged
## # weights: 11
## initial value 473.790457
## iter 10 value 421.648944
## iter 20 value 412.036381
## iter 30 value 408.931913
## iter 40 value 404.550877
## iter 50 value 397.590095
## iter 60 value 381.229070
## iter 70 value 326.710269
## iter 80 value 326.268094
## final value 326.266097
## converged
## # weights: 31
## initial value 517.408829
## iter 10 value 447.342837
## iter 20 value 441.527879
## iter 30 value 429.551885
## iter 40 value 425.952341
## iter 50 value 421.728292
## iter 60 value 410.970175
## iter 70 value 407.733773
## iter 80 value 402.994430
## iter 90 value 402.461254
## iter 100 value 402.345737
## final value 402.345737
## stopped after 100 iterations
## # weights: 51
## initial value 643.265461
## iter 10 value 440.147524
## iter 20 value 425.211798
## iter 30 value 410.009959
## iter 40 value 404.897665
## iter 50 value 389.516955
## iter 60 value 382.614755
## iter 70 value 379.522445
## iter 80 value 376.156444
## iter 90 value 373.954590
## iter 100 value 372.080673
## final value 372.080673
## stopped after 100 iterations
## # weights: 11
## initial value 549.192609
## final value 446.857598
## converged
## # weights: 31
## initial value 614.406217
## final value 446.864450
## converged
## # weights: 51
## initial value 690.229509
## iter 10 value 416.571314
## iter 20 value 405.424306
## iter 30 value 395.132715
## iter 40 value 389.815547
## iter 50 value 387.764854
## iter 60 value 386.357945
## iter 70 value 385.047906
## iter 80 value 383.802090
## iter 90 value 383.425483
## iter 100 value 382.904115
## final value 382.904115
## stopped after 100 iterations
## # weights: 11
## initial value 499.997227
## final value 446.857145
## converged
## # weights: 31
## initial value 449.119388
## iter 10 value 419.665474
## iter 20 value 411.523717
## iter 30 value 403.132887
## iter 40 value 396.090106
## iter 50 value 382.677891
## iter 60 value 363.423878
## iter 70 value 324.969009
## iter 80 value 321.616448
## iter 90 value 320.942113
## iter 100 value 319.652457
## final value 319.652457
## stopped after 100 iterations
## # weights: 51
## initial value 623.254589
## iter 10 value 427.873871
## iter 20 value 414.373592
## iter 30 value 401.848699
## iter 40 value 396.608841
## iter 50 value 392.647074
## iter 60 value 389.496007
## iter 70 value 389.290480
## iter 80 value 389.289790
## final value 389.289771
## converged
## # weights: 11
## initial value 557.995031
## iter 10 value 446.575462
## iter 20 value 433.480346
## iter 30 value 430.999365
## iter 40 value 411.445026
## iter 50 value 399.899309
## iter 60 value 368.112096
## iter 70 value 340.080099
## iter 80 value 335.152424
## final value 335.079898
## converged
## # weights: 31
## initial value 453.693178
## iter 10 value 444.485259
## iter 20 value 426.259967
## iter 30 value 409.529476
## iter 40 value 399.327401
## iter 50 value 397.346932
## iter 60 value 395.060197
## iter 70 value 363.757500
## iter 80 value 329.341975
## iter 90 value 318.454557
## iter 100 value 317.499581
## final value 317.499581
## stopped after 100 iterations
## # weights: 51
## initial value 496.217999
## iter 10 value 435.519889
## iter 20 value 411.807971
## iter 30 value 408.468738
## iter 40 value 405.028830
## iter 50 value 402.851197
## iter 60 value 402.237180
## iter 70 value 402.143375
## iter 80 value 401.635282
## iter 90 value 399.953388
## iter 100 value 398.777024
## final value 398.777024
## stopped after 100 iterations
## # weights: 11
## initial value 458.814442
## final value 446.857350
## converged
## # weights: 31
## initial value 485.025617
## iter 10 value 439.623086
## iter 20 value 433.476855
## iter 30 value 427.382463
## iter 40 value 425.834579
## iter 50 value 425.222716
## iter 60 value 425.106969
## iter 70 value 425.078756
## iter 80 value 425.055083
## iter 90 value 425.050509
## iter 100 value 424.982952
## final value 424.982952
## stopped after 100 iterations
## # weights: 51
## initial value 558.333909
## iter 10 value 439.478371
## iter 20 value 423.670805
## iter 30 value 418.333372
## iter 40 value 403.373555
## iter 50 value 400.095846
## iter 60 value 396.936655
## iter 70 value 393.536622
## iter 80 value 393.437357
## iter 90 value 393.297667
## iter 100 value 393.207083
## final value 393.207083
## stopped after 100 iterations
## # weights: 11
## initial value 668.497215
## final value 447.909139
## converged
## # weights: 31
## initial value 504.051381
## iter 10 value 438.598650
## iter 20 value 434.770575
## iter 30 value 434.331064
## iter 40 value 434.009767
## iter 50 value 433.974774
## final value 433.974739
## converged
## # weights: 51
## initial value 448.530411
## iter 10 value 430.624767
## iter 20 value 417.774101
## iter 30 value 410.037937
## iter 40 value 404.731737
## iter 50 value 399.936761
## iter 60 value 396.772073
## iter 70 value 393.854854
## iter 80 value 393.486851
## iter 90 value 393.478751
## final value 393.478734
## converged
## # weights: 11
## initial value 494.385269
## iter 10 value 447.898019
## iter 20 value 445.194668
## iter 30 value 444.088098
## iter 40 value 444.039987
## iter 50 value 442.834287
## iter 60 value 442.580912
## final value 442.580752
## converged
## # weights: 31
## initial value 447.728395
## iter 10 value 429.464227
## iter 20 value 405.731184
## iter 30 value 397.148132
## iter 40 value 392.773137
## iter 50 value 377.715696
## iter 60 value 368.022723
## iter 70 value 344.009591
## iter 80 value 315.445346
## iter 90 value 312.435730
## iter 100 value 311.680550
## final value 311.680550
## stopped after 100 iterations
## # weights: 51
## initial value 533.341945
## iter 10 value 427.609824
## iter 20 value 409.949270
## iter 30 value 403.550186
## iter 40 value 375.870917
## iter 50 value 366.651114
## iter 60 value 331.066296
## iter 70 value 313.698214
## iter 80 value 310.368930
## iter 90 value 309.822650
## iter 100 value 307.384866
## final value 307.384866
## stopped after 100 iterations
## # weights: 11
## initial value 485.019636
## final value 447.909311
## converged
## # weights: 31
## initial value 482.721885
## iter 10 value 447.047106
## iter 20 value 441.875805
## iter 30 value 440.110016
## iter 40 value 439.768580
## iter 50 value 432.268364
## iter 60 value 421.203276
## iter 70 value 415.953294
## iter 80 value 413.343089
## iter 90 value 408.187414
## iter 100 value 407.349250
## final value 407.349250
## stopped after 100 iterations
## # weights: 51
## initial value 451.271378
## iter 10 value 432.947398
## iter 20 value 411.707825
## iter 30 value 394.761513
## iter 40 value 389.429602
## iter 50 value 387.706599
## iter 60 value 387.279822
## iter 70 value 385.825526
## iter 80 value 385.078597
## iter 90 value 384.867761
## iter 100 value 384.796752
## final value 384.796752
## stopped after 100 iterations
## # weights: 11
## initial value 447.778291
## iter 10 value 444.552264
## iter 20 value 442.154272
## iter 30 value 441.140925
## final value 441.135680
## converged
## # weights: 31
## initial value 611.595052
## iter 10 value 427.787335
## iter 20 value 415.980203
## iter 30 value 410.125315
## iter 40 value 391.814859
## iter 50 value 385.503558
## iter 60 value 373.015366
## iter 70 value 362.670904
## iter 80 value 358.678559
## iter 90 value 357.916213
## iter 100 value 357.776894
## final value 357.776894
## stopped after 100 iterations
## # weights: 51
## initial value 481.312445
## iter 10 value 444.150631
## iter 20 value 440.403352
## iter 30 value 438.491325
## iter 40 value 437.685862
## iter 50 value 437.226028
## iter 60 value 436.950359
## iter 70 value 436.645257
## iter 80 value 436.393454
## iter 90 value 435.968519
## iter 100 value 435.857409
## final value 435.857409
## stopped after 100 iterations
## # weights: 11
## initial value 498.128497
## iter 10 value 442.814517
## iter 20 value 437.964725
## iter 30 value 433.532729
## iter 40 value 432.989723
## final value 432.989713
## converged
## # weights: 31
## initial value 441.990558
## iter 10 value 422.815713
## iter 20 value 405.440432
## iter 30 value 393.531535
## iter 40 value 387.230316
## iter 50 value 374.587012
## iter 60 value 353.396278
## iter 70 value 330.170804
## iter 80 value 328.354962
## iter 90 value 325.807510
## iter 100 value 323.936610
## final value 323.936610
## stopped after 100 iterations
## # weights: 51
## initial value 637.426285
## iter 10 value 430.942383
## iter 20 value 425.336334
## iter 30 value 414.278217
## iter 40 value 411.424333
## iter 50 value 409.461584
## iter 60 value 405.464875
## iter 70 value 395.163759
## iter 80 value 381.535163
## iter 90 value 375.165126
## iter 100 value 367.266267
## final value 367.266267
## stopped after 100 iterations
## # weights: 11
## initial value 475.562573
## iter 10 value 446.514433
## iter 20 value 444.767762
## final value 444.757250
## converged
## # weights: 31
## initial value 447.440546
## iter 10 value 445.738040
## iter 20 value 443.751230
## iter 30 value 439.446842
## iter 40 value 439.211934
## final value 439.211375
## converged
## # weights: 51
## initial value 525.230335
## iter 10 value 434.319083
## iter 20 value 420.572677
## iter 30 value 408.238123
## iter 40 value 405.226038
## iter 50 value 401.145910
## iter 60 value 399.510124
## iter 70 value 396.280817
## iter 80 value 368.171826
## iter 90 value 322.145643
## iter 100 value 313.255541
## final value 313.255541
## stopped after 100 iterations
## # weights: 11
## initial value 585.825649
## final value 446.857145
## converged
## # weights: 31
## initial value 539.942063
## final value 446.857145
## converged
## # weights: 51
## initial value 571.576315
## iter 10 value 432.969547
## iter 20 value 428.426183
## iter 30 value 426.771632
## iter 40 value 426.280610
## iter 50 value 425.332046
## iter 60 value 424.660923
## iter 70 value 424.324536
## iter 80 value 424.041340
## iter 90 value 423.884275
## iter 100 value 423.716604
## final value 423.716604
## stopped after 100 iterations
## # weights: 11
## initial value 446.601597
## iter 10 value 426.776944
## iter 20 value 421.009698
## iter 30 value 419.946033
## iter 40 value 419.725947
## iter 50 value 419.343126
## final value 419.226922
## converged
## # weights: 31
## initial value 463.356727
## iter 10 value 443.099760
## iter 20 value 437.933410
## iter 30 value 430.043283
## iter 40 value 415.023351
## iter 50 value 409.351619
## iter 60 value 401.416567
## iter 70 value 397.463658
## iter 80 value 380.843414
## iter 90 value 374.402271
## iter 100 value 343.132419
## final value 343.132419
## stopped after 100 iterations
## # weights: 51
## initial value 494.012485
## iter 10 value 427.119268
## iter 20 value 418.102541
## iter 30 value 409.032406
## iter 40 value 402.448581
## iter 50 value 394.935044
## iter 60 value 376.580226
## iter 70 value 369.580752
## iter 80 value 366.503601
## iter 90 value 355.339590
## iter 100 value 339.507547
## final value 339.507547
## stopped after 100 iterations
## # weights: 11
## initial value 466.485908
## iter 10 value 446.740176
## iter 20 value 446.471658
## iter 30 value 446.301382
## final value 446.201174
## converged
## # weights: 31
## initial value 795.981270
## iter 10 value 470.882667
## iter 20 value 438.980561
## iter 30 value 430.770119
## iter 40 value 427.380653
## iter 50 value 425.900972
## iter 60 value 425.216206
## iter 70 value 425.174533
## iter 80 value 425.168122
## iter 90 value 425.166681
## iter 100 value 425.165704
## final value 425.165704
## stopped after 100 iterations
## # weights: 51
## initial value 496.179893
## iter 10 value 435.058629
## iter 20 value 419.854786
## iter 30 value 404.971679
## iter 40 value 378.322249
## iter 50 value 330.854663
## iter 60 value 325.186757
## iter 70 value 317.068473
## iter 80 value 315.009405
## iter 90 value 314.689852
## iter 100 value 314.601255
## final value 314.601255
## stopped after 100 iterations
## # weights: 11
## initial value 504.134550
## final value 446.857145
## converged
## # weights: 31
## initial value 523.909072
## final value 446.857145
## converged
## # weights: 51
## initial value 466.585000
## iter 10 value 429.768681
## iter 20 value 401.600101
## iter 30 value 395.806385
## iter 40 value 380.310368
## iter 50 value 324.169008
## iter 60 value 317.060681
## iter 70 value 312.934260
## iter 80 value 312.381121
## iter 90 value 312.095945
## iter 100 value 311.773364
## final value 311.773364
## stopped after 100 iterations
## # weights: 11
## initial value 592.806515
## iter 10 value 444.059921
## iter 20 value 417.063664
## iter 30 value 411.176643
## iter 40 value 410.858695
## iter 50 value 400.105499
## iter 60 value 374.633398
## iter 70 value 349.919476
## iter 80 value 325.932975
## iter 90 value 325.532201
## final value 325.498367
## converged
## # weights: 31
## initial value 470.249554
## iter 10 value 429.160047
## iter 20 value 409.601734
## iter 30 value 404.511443
## iter 40 value 395.053965
## iter 50 value 391.459576
## iter 60 value 386.721042
## iter 70 value 373.540664
## iter 80 value 344.318389
## iter 90 value 328.026499
## iter 100 value 319.058435
## final value 319.058435
## stopped after 100 iterations
## # weights: 51
## initial value 505.720093
## iter 10 value 442.770394
## iter 20 value 427.492550
## iter 30 value 412.145056
## iter 40 value 405.313395
## iter 50 value 395.440056
## iter 60 value 390.372326
## iter 70 value 388.175642
## iter 80 value 386.284067
## iter 90 value 382.074381
## iter 100 value 379.860377
## final value 379.860377
## stopped after 100 iterations
## # weights: 11
## initial value 447.634081
## iter 10 value 441.292269
## iter 20 value 440.587289
## iter 30 value 440.119255
## iter 40 value 439.936705
## final value 439.927966
## converged
## # weights: 31
## initial value 530.426505
## iter 10 value 439.703919
## iter 20 value 433.576734
## iter 30 value 431.060526
## iter 40 value 418.948335
## iter 50 value 405.389500
## iter 60 value 394.743188
## iter 70 value 389.505600
## iter 80 value 386.813535
## iter 90 value 385.494069
## iter 100 value 385.146438
## final value 385.146438
## stopped after 100 iterations
## # weights: 51
## initial value 449.309990
## iter 10 value 428.879761
## iter 20 value 411.009135
## iter 30 value 404.232279
## iter 40 value 399.503184
## iter 50 value 397.304509
## iter 60 value 379.690499
## iter 70 value 326.887046
## iter 80 value 323.053451
## iter 90 value 322.221255
## iter 100 value 321.042584
## final value 321.042584
## stopped after 100 iterations
## # weights: 31
## initial value 518.216217
## iter 10 value 487.136798
## iter 20 value 461.393860
## iter 30 value 453.565517
## iter 40 value 452.247425
## iter 50 value 452.146495
## iter 60 value 451.597070
## iter 70 value 448.066461
## iter 80 value 435.395926
## iter 90 value 386.554916
## iter 100 value 366.591081
## final value 366.591081
## stopped after 100 iterations4.d) Compare Algorithms Before Tuning
#results <- resamples(list(LM=fit.lm, LogReg=fit.glm, LDA=fit.lda, CART=fit.cart, NB=fit.nb, kNN=fit.knn, SVM=fit.svm, BagCART=fit.bagcart, RF=fit.rf, AdaBoost=fit.ada, GBM=fit.gbm, NNet=fit.nnet))
results <- resamples(list(LogReg=fit.glm, LDA=fit.lda, CART=fit.cart, NB=fit.nb, kNN=fit.knn, SVM=fit.svm, BagCART=fit.bagcart, RF=fit.rf, AdaBoost=fit.ada, GBM=fit.gbm, NNet=fit.nnet))
summary(results)##
## Call:
## summary.resamples(object = results)
##
## Models: LogReg, LDA, CART, NB, kNN, SVM, BagCART, RF, AdaBoost, GBM, NNet
## Number of resamples: 30
##
## Accuracy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## LogReg 0.6883117 0.7532468 0.7843472 0.7773354 0.8000256 0.8441558 0
## LDA 0.6883117 0.7662338 0.7792208 0.7760367 0.8000256 0.8311688 0
## CART 0.6753247 0.7272727 0.7532468 0.7513329 0.7784945 0.8181818 0
## NB 0.6883117 0.7368421 0.7532468 0.7555821 0.7792208 0.8051948 0
## kNN 0.6710526 0.7012987 0.7500000 0.7408863 0.7737953 0.8051948 0
## SVM 0.6883117 0.7426948 0.7727273 0.7682217 0.7922078 0.8441558 0
## BagCART 0.6493506 0.7272727 0.7532468 0.7526885 0.7792208 0.8289474 0
## RF 0.6883117 0.7402597 0.7662338 0.7674015 0.8000256 0.8441558 0
## AdaBoost 0.6623377 0.7272727 0.7532468 0.7548075 0.7763158 0.8289474 0
## GBM 0.6883117 0.7435065 0.7792208 0.7704090 0.8000256 0.8311688 0
## NNet 0.6363636 0.6883117 0.7385509 0.7287366 0.7629870 0.8441558 0
##
## Kappa
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## LogReg 0.2655008 0.4401342 0.4943168 0.4843861 0.5346588 0.6393443 0
## LDA 0.2786885 0.4425230 0.4897297 0.4802587 0.5346588 0.6057503 0
## CART 0.2786885 0.3886332 0.4307617 0.4292629 0.4878037 0.5792350 0
## NB 0.2786885 0.3980714 0.4627249 0.4488307 0.5033039 0.5531915 0
## kNN 0.2518219 0.3266159 0.4217663 0.4075877 0.4777357 0.5610034 0
## SVM 0.2228764 0.3990107 0.4717440 0.4599820 0.5232890 0.6518463 0
## BagCART 0.2233844 0.3836782 0.4532389 0.4431798 0.5147087 0.6016129 0
## RF 0.2655008 0.4115674 0.4688274 0.4746353 0.5456735 0.6393443 0
## AdaBoost 0.2323620 0.3959656 0.4318361 0.4425152 0.4955021 0.6091772 0
## GBM 0.2655008 0.4029214 0.4749298 0.4620527 0.5303393 0.6091772 0
## NNet 0.0754717 0.2352090 0.3858890 0.3561820 0.4627249 0.6518463 0dotplot(results)
5. Improve Accuracy or Results
After we achieve a short list of machine learning algorithms with good level of accuracy, we can leverage ways to improve the accuracy of the models:
- Search for a combination of parameters for each algorithm using caret that yields the best results.
- Combine the prediction of multiple models into an ensemble prediction using standalone algorithms or the caret Ensemble package.
5.a) Algorithm Tuning
Finally, we will tune the three best-performing algorithms further and see whether we can get more accuracy out of them.
# Tuning algorithm #1 - Logistic Regression
# GLM requires no tuning parameters.
set.seed(seedNum)
fit.final1 <- fit.glm
print(fit.final1)## Generalized Linear Model
##
## 768 samples
## 8 predictor
## 2 classes: 'neg', 'pos'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 692, 691, 691, 691, 691, 692, ...
## Resampling results:
##
## Accuracy Kappa
## 0.7773354 0.4843861# Tuning algorithm #2 - Linear/Quadratic Discriminant Analysis
# LDA requires no tuning parameters.
set.seed(seedNum)
fit.final2 <- fit.lda
print(fit.final2)## Linear Discriminant Analysis
##
## 768 samples
## 8 predictor
## 2 classes: 'neg', 'pos'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 692, 691, 691, 691, 691, 692, ...
## Resampling results:
##
## Accuracy Kappa
## 0.7760367 0.4802587# Tuning algorithm #3 - Stochastic Gradient Boosting
set.seed(seedNum)
fit.final3 <- fit.lda
grid <- expand.grid(.n.trees=c(100, 250, 500), .shrinkage=c(0, 0.001, 0.1, 0.2, 0.3, 0.5, 1), .interaction.depth=c(1,2,3), .n.minobsinnode=c(1, 2, 3))
fit.final3 <- train(classVar~., data=dataset, method="gbm", metric="Accuracy", tuneGrid=grid, trControl=control)## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## 300 1.1790 nan 0.0010 0.0001
## 320 1.1729 nan 0.0010 0.0001
## 340 1.1672 nan 0.0010 0.0001
## 360 1.1616 nan 0.0010 0.0001
## 380 1.1558 nan 0.0010 0.0001
## 400 1.1503 nan 0.0010 0.0001
## 420 1.1449 nan 0.0010 0.0001
## 440 1.1399 nan 0.0010 0.0001
## 460 1.1349 nan 0.0010 0.0001
## 480 1.1299 nan 0.0010 0.0001
## 500 1.1252 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
## 2 1.2935 nan 0.0010 0.0002
## 3 1.2931 nan 0.0010 0.0002
## 4 1.2926 nan 0.0010 0.0002
## 5 1.2921 nan 0.0010 0.0002
## 6 1.2916 nan 0.0010 0.0002
## 7 1.2910 nan 0.0010 0.0002
## 8 1.2906 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2896 nan 0.0010 0.0002
## 20 1.2848 nan 0.0010 0.0002
## 40 1.2757 nan 0.0010 0.0002
## 60 1.2668 nan 0.0010 0.0002
## 80 1.2583 nan 0.0010 0.0002
## 100 1.2500 nan 0.0010 0.0002
## 120 1.2421 nan 0.0010 0.0002
## 140 1.2344 nan 0.0010 0.0002
## 160 1.2269 nan 0.0010 0.0001
## 180 1.2196 nan 0.0010 0.0002
## 200 1.2126 nan 0.0010 0.0002
## 220 1.2057 nan 0.0010 0.0002
## 240 1.1990 nan 0.0010 0.0002
## 260 1.1924 nan 0.0010 0.0001
## 280 1.1860 nan 0.0010 0.0001
## 300 1.1799 nan 0.0010 0.0001
## 320 1.1739 nan 0.0010 0.0001
## 340 1.1681 nan 0.0010 0.0001
## 360 1.1623 nan 0.0010 0.0001
## 380 1.1570 nan 0.0010 0.0001
## 400 1.1516 nan 0.0010 0.0001
## 420 1.1464 nan 0.0010 0.0001
## 440 1.1412 nan 0.0010 0.0001
## 460 1.1362 nan 0.0010 0.0001
## 480 1.1314 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
## 2 1.2936 nan 0.0010 0.0002
## 3 1.2931 nan 0.0010 0.0002
## 4 1.2926 nan 0.0010 0.0002
## 5 1.2921 nan 0.0010 0.0002
## 6 1.2916 nan 0.0010 0.0002
## 7 1.2911 nan 0.0010 0.0002
## 8 1.2906 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2896 nan 0.0010 0.0002
## 20 1.2849 nan 0.0010 0.0002
## 40 1.2757 nan 0.0010 0.0002
## 60 1.2666 nan 0.0010 0.0002
## 80 1.2581 nan 0.0010 0.0002
## 100 1.2499 nan 0.0010 0.0002
## 120 1.2418 nan 0.0010 0.0002
## 140 1.2338 nan 0.0010 0.0002
## 160 1.2264 nan 0.0010 0.0002
## 180 1.2190 nan 0.0010 0.0002
## 200 1.2121 nan 0.0010 0.0002
## 220 1.2050 nan 0.0010 0.0002
## 240 1.1983 nan 0.0010 0.0001
## 260 1.1917 nan 0.0010 0.0001
## 280 1.1852 nan 0.0010 0.0001
## 300 1.1788 nan 0.0010 0.0002
## 320 1.1726 nan 0.0010 0.0001
## 340 1.1667 nan 0.0010 0.0001
## 360 1.1609 nan 0.0010 0.0001
## 380 1.1553 nan 0.0010 0.0001
## 400 1.1499 nan 0.0010 0.0001
## 420 1.1447 nan 0.0010 0.0001
## 440 1.1394 nan 0.0010 0.0001
## 460 1.1344 nan 0.0010 0.0001
## 480 1.1295 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
## 2 1.2934 nan 0.0010 0.0002
## 3 1.2929 nan 0.0010 0.0002
## 4 1.2923 nan 0.0010 0.0003
## 5 1.2917 nan 0.0010 0.0003
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2906 nan 0.0010 0.0002
## 8 1.2901 nan 0.0010 0.0003
## 9 1.2896 nan 0.0010 0.0002
## 10 1.2891 nan 0.0010 0.0002
## 20 1.2835 nan 0.0010 0.0003
## 40 1.2727 nan 0.0010 0.0002
## 60 1.2624 nan 0.0010 0.0002
## 80 1.2523 nan 0.0010 0.0002
## 100 1.2426 nan 0.0010 0.0002
## 120 1.2333 nan 0.0010 0.0002
## 140 1.2243 nan 0.0010 0.0002
## 160 1.2157 nan 0.0010 0.0002
## 180 1.2074 nan 0.0010 0.0001
## 200 1.1989 nan 0.0010 0.0002
## 220 1.1909 nan 0.0010 0.0002
## 240 1.1832 nan 0.0010 0.0002
## 260 1.1758 nan 0.0010 0.0002
## 280 1.1684 nan 0.0010 0.0002
## 300 1.1613 nan 0.0010 0.0001
## 320 1.1544 nan 0.0010 0.0001
## 340 1.1476 nan 0.0010 0.0001
## 360 1.1411 nan 0.0010 0.0001
## 380 1.1347 nan 0.0010 0.0001
## 400 1.1286 nan 0.0010 0.0001
## 420 1.1227 nan 0.0010 0.0001
## 440 1.1170 nan 0.0010 0.0001
## 460 1.1112 nan 0.0010 0.0001
## 480 1.1054 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
## 2 1.2934 nan 0.0010 0.0003
## 3 1.2929 nan 0.0010 0.0002
## 4 1.2923 nan 0.0010 0.0002
## 5 1.2919 nan 0.0010 0.0002
## 6 1.2913 nan 0.0010 0.0003
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2902 nan 0.0010 0.0002
## 9 1.2896 nan 0.0010 0.0003
## 10 1.2891 nan 0.0010 0.0002
## 20 1.2834 nan 0.0010 0.0003
## 40 1.2729 nan 0.0010 0.0003
## 60 1.2626 nan 0.0010 0.0002
## 80 1.2526 nan 0.0010 0.0002
## 100 1.2431 nan 0.0010 0.0002
## 120 1.2335 nan 0.0010 0.0002
## 140 1.2245 nan 0.0010 0.0002
## 160 1.2160 nan 0.0010 0.0001
## 180 1.2074 nan 0.0010 0.0002
## 200 1.1991 nan 0.0010 0.0001
## 220 1.1910 nan 0.0010 0.0002
## 240 1.1834 nan 0.0010 0.0002
## 260 1.1758 nan 0.0010 0.0002
## 280 1.1683 nan 0.0010 0.0002
## 300 1.1611 nan 0.0010 0.0002
## 320 1.1541 nan 0.0010 0.0001
## 340 1.1471 nan 0.0010 0.0001
## 360 1.1406 nan 0.0010 0.0001
## 380 1.1344 nan 0.0010 0.0001
## 400 1.1284 nan 0.0010 0.0001
## 420 1.1223 nan 0.0010 0.0001
## 440 1.1165 nan 0.0010 0.0001
## 460 1.1108 nan 0.0010 0.0001
## 480 1.1051 nan 0.0010 0.0001
## 500 1.0997 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
## 2 1.2934 nan 0.0010 0.0003
## 3 1.2928 nan 0.0010 0.0003
## 4 1.2923 nan 0.0010 0.0002
## 5 1.2917 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0003
## 7 1.2906 nan 0.0010 0.0002
## 8 1.2900 nan 0.0010 0.0003
## 9 1.2895 nan 0.0010 0.0002
## 10 1.2890 nan 0.0010 0.0002
## 20 1.2836 nan 0.0010 0.0003
## 40 1.2731 nan 0.0010 0.0003
## 60 1.2627 nan 0.0010 0.0002
## 80 1.2529 nan 0.0010 0.0002
## 100 1.2433 nan 0.0010 0.0002
## 120 1.2339 nan 0.0010 0.0002
## 140 1.2247 nan 0.0010 0.0002
## 160 1.2160 nan 0.0010 0.0002
## 180 1.2075 nan 0.0010 0.0002
## 200 1.1992 nan 0.0010 0.0002
## 220 1.1913 nan 0.0010 0.0002
## 240 1.1835 nan 0.0010 0.0001
## 260 1.1759 nan 0.0010 0.0002
## 280 1.1687 nan 0.0010 0.0001
## 300 1.1612 nan 0.0010 0.0001
## 320 1.1541 nan 0.0010 0.0001
## 340 1.1473 nan 0.0010 0.0001
## 360 1.1407 nan 0.0010 0.0001
## 380 1.1343 nan 0.0010 0.0001
## 400 1.1280 nan 0.0010 0.0001
## 420 1.1221 nan 0.0010 0.0001
## 440 1.1160 nan 0.0010 0.0001
## 460 1.1104 nan 0.0010 0.0001
## 480 1.1046 nan 0.0010 0.0001
## 500 1.0991 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2604 nan 0.1000 0.0175
## 2 1.2332 nan 0.1000 0.0130
## 3 1.2062 nan 0.1000 0.0105
## 4 1.1849 nan 0.1000 0.0104
## 5 1.1700 nan 0.1000 0.0060
## 6 1.1523 nan 0.1000 0.0072
## 7 1.1373 nan 0.1000 0.0061
## 8 1.1225 nan 0.1000 0.0033
## 9 1.1086 nan 0.1000 0.0060
## 10 1.0929 nan 0.1000 0.0069
## 20 1.0074 nan 0.1000 0.0012
## 40 0.9273 nan 0.1000 -0.0010
## 60 0.8816 nan 0.1000 -0.0001
## 80 0.8559 nan 0.1000 -0.0008
## 100 0.8390 nan 0.1000 -0.0005
## 120 0.8255 nan 0.1000 -0.0008
## 140 0.8135 nan 0.1000 -0.0037
## 160 0.8024 nan 0.1000 -0.0009
## 180 0.7926 nan 0.1000 -0.0001
## 200 0.7839 nan 0.1000 -0.0007
## 220 0.7765 nan 0.1000 -0.0011
## 240 0.7701 nan 0.1000 -0.0016
## 260 0.7651 nan 0.1000 -0.0014
## 280 0.7583 nan 0.1000 -0.0005
## 300 0.7529 nan 0.1000 -0.0014
## 320 0.7478 nan 0.1000 -0.0008
## 340 0.7415 nan 0.1000 -0.0008
## 360 0.7360 nan 0.1000 -0.0009
## 380 0.7313 nan 0.1000 -0.0008
## 400 0.7276 nan 0.1000 -0.0009
## 420 0.7246 nan 0.1000 -0.0010
## 440 0.7215 nan 0.1000 -0.0008
## 460 0.7146 nan 0.1000 -0.0010
## 480 0.7086 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2591 nan 0.1000 0.0164
## 2 1.2282 nan 0.1000 0.0145
## 3 1.2017 nan 0.1000 0.0116
## 4 1.1833 nan 0.1000 0.0093
## 5 1.1635 nan 0.1000 0.0070
## 6 1.1442 nan 0.1000 0.0083
## 7 1.1295 nan 0.1000 0.0053
## 8 1.1151 nan 0.1000 0.0066
## 9 1.0996 nan 0.1000 0.0051
## 10 1.0872 nan 0.1000 0.0036
## 20 1.0012 nan 0.1000 0.0011
## 40 0.9143 nan 0.1000 0.0002
## 60 0.8764 nan 0.1000 -0.0003
## 80 0.8527 nan 0.1000 -0.0002
## 100 0.8330 nan 0.1000 0.0002
## 120 0.8200 nan 0.1000 -0.0013
## 140 0.8091 nan 0.1000 -0.0019
## 160 0.7985 nan 0.1000 -0.0017
## 180 0.7886 nan 0.1000 -0.0003
## 200 0.7812 nan 0.1000 -0.0007
## 220 0.7744 nan 0.1000 -0.0016
## 240 0.7669 nan 0.1000 -0.0001
## 260 0.7615 nan 0.1000 -0.0010
## 280 0.7552 nan 0.1000 -0.0016
## 300 0.7496 nan 0.1000 -0.0007
## 320 0.7449 nan 0.1000 -0.0008
## 340 0.7403 nan 0.1000 -0.0020
## 360 0.7350 nan 0.1000 -0.0006
## 380 0.7281 nan 0.1000 -0.0004
## 400 0.7222 nan 0.1000 -0.0002
## 420 0.7185 nan 0.1000 -0.0010
## 440 0.7139 nan 0.1000 -0.0013
## 460 0.7112 nan 0.1000 -0.0007
## 480 0.7069 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2618 nan 0.1000 0.0172
## 2 1.2319 nan 0.1000 0.0151
## 3 1.2010 nan 0.1000 0.0118
## 4 1.1820 nan 0.1000 0.0077
## 5 1.1666 nan 0.1000 0.0064
## 6 1.1482 nan 0.1000 0.0082
## 7 1.1315 nan 0.1000 0.0076
## 8 1.1178 nan 0.1000 0.0061
## 9 1.1031 nan 0.1000 0.0066
## 10 1.0887 nan 0.1000 0.0053
## 20 1.0065 nan 0.1000 0.0014
## 40 0.9167 nan 0.1000 -0.0001
## 60 0.8806 nan 0.1000 -0.0004
## 80 0.8571 nan 0.1000 -0.0025
## 100 0.8395 nan 0.1000 -0.0008
## 120 0.8237 nan 0.1000 -0.0003
## 140 0.8097 nan 0.1000 -0.0005
## 160 0.7966 nan 0.1000 -0.0010
## 180 0.7856 nan 0.1000 -0.0011
## 200 0.7794 nan 0.1000 -0.0004
## 220 0.7728 nan 0.1000 -0.0014
## 240 0.7674 nan 0.1000 -0.0016
## 260 0.7605 nan 0.1000 -0.0009
## 280 0.7534 nan 0.1000 -0.0013
## 300 0.7472 nan 0.1000 -0.0003
## 320 0.7416 nan 0.1000 -0.0005
## 340 0.7368 nan 0.1000 -0.0017
## 360 0.7319 nan 0.1000 -0.0000
## 380 0.7254 nan 0.1000 -0.0017
## 400 0.7200 nan 0.1000 -0.0003
## 420 0.7153 nan 0.1000 -0.0013
## 440 0.7099 nan 0.1000 -0.0014
## 460 0.7056 nan 0.1000 -0.0019
## 480 0.7001 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2490 nan 0.1000 0.0200
## 2 1.2120 nan 0.1000 0.0160
## 3 1.1790 nan 0.1000 0.0151
## 4 1.1507 nan 0.1000 0.0114
## 5 1.1271 nan 0.1000 0.0098
## 6 1.1061 nan 0.1000 0.0085
## 7 1.0855 nan 0.1000 0.0081
## 8 1.0654 nan 0.1000 0.0078
## 9 1.0485 nan 0.1000 0.0070
## 10 1.0327 nan 0.1000 0.0049
## 20 0.9375 nan 0.1000 0.0008
## 40 0.8467 nan 0.1000 -0.0003
## 60 0.8009 nan 0.1000 -0.0014
## 80 0.7673 nan 0.1000 -0.0023
## 100 0.7409 nan 0.1000 -0.0013
## 120 0.7142 nan 0.1000 -0.0013
## 140 0.6933 nan 0.1000 -0.0015
## 160 0.6752 nan 0.1000 -0.0007
## 180 0.6534 nan 0.1000 -0.0018
## 200 0.6336 nan 0.1000 -0.0018
## 220 0.6172 nan 0.1000 -0.0016
## 240 0.5989 nan 0.1000 -0.0011
## 260 0.5857 nan 0.1000 0.0004
## 280 0.5705 nan 0.1000 -0.0004
## 300 0.5564 nan 0.1000 -0.0014
## 320 0.5456 nan 0.1000 -0.0004
## 340 0.5329 nan 0.1000 -0.0006
## 360 0.5177 nan 0.1000 -0.0011
## 380 0.5063 nan 0.1000 -0.0015
## 400 0.4936 nan 0.1000 -0.0004
## 420 0.4838 nan 0.1000 -0.0007
## 440 0.4748 nan 0.1000 -0.0007
## 460 0.4664 nan 0.1000 -0.0012
## 480 0.4557 nan 0.1000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2519 nan 0.1000 0.0202
## 2 1.2121 nan 0.1000 0.0176
## 3 1.1785 nan 0.1000 0.0157
## 4 1.1466 nan 0.1000 0.0113
## 5 1.1238 nan 0.1000 0.0096
## 6 1.0974 nan 0.1000 0.0088
## 7 1.0782 nan 0.1000 0.0089
## 8 1.0632 nan 0.1000 0.0050
## 9 1.0479 nan 0.1000 0.0052
## 10 1.0314 nan 0.1000 0.0066
## 20 0.9420 nan 0.1000 0.0006
## 40 0.8503 nan 0.1000 -0.0000
## 60 0.8000 nan 0.1000 -0.0013
## 80 0.7643 nan 0.1000 -0.0012
## 100 0.7417 nan 0.1000 -0.0016
## 120 0.7114 nan 0.1000 -0.0010
## 140 0.6879 nan 0.1000 -0.0018
## 160 0.6690 nan 0.1000 -0.0008
## 180 0.6498 nan 0.1000 -0.0015
## 200 0.6309 nan 0.1000 -0.0011
## 220 0.6143 nan 0.1000 -0.0011
## 240 0.5975 nan 0.1000 -0.0007
## 260 0.5834 nan 0.1000 -0.0011
## 280 0.5688 nan 0.1000 -0.0013
## 300 0.5601 nan 0.1000 -0.0011
## 320 0.5476 nan 0.1000 -0.0008
## 340 0.5354 nan 0.1000 -0.0007
## 360 0.5207 nan 0.1000 -0.0005
## 380 0.5104 nan 0.1000 -0.0013
## 400 0.5011 nan 0.1000 -0.0004
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## 460 0.4636 nan 0.1000 -0.0004
## 480 0.4542 nan 0.1000 -0.0007
## 500 0.4449 nan 0.1000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2489 nan 0.1000 0.0198
## 2 1.2049 nan 0.1000 0.0190
## 3 1.1749 nan 0.1000 0.0122
## 4 1.1438 nan 0.1000 0.0123
## 5 1.1182 nan 0.1000 0.0085
## 6 1.0941 nan 0.1000 0.0116
## 7 1.0737 nan 0.1000 0.0074
## 8 1.0543 nan 0.1000 0.0084
## 9 1.0403 nan 0.1000 0.0045
## 10 1.0245 nan 0.1000 0.0050
## 20 0.9340 nan 0.1000 0.0000
## 40 0.8585 nan 0.1000 0.0006
## 60 0.8145 nan 0.1000 -0.0006
## 80 0.7751 nan 0.1000 -0.0008
## 100 0.7433 nan 0.1000 -0.0024
## 120 0.7220 nan 0.1000 -0.0017
## 140 0.6992 nan 0.1000 -0.0017
## 160 0.6804 nan 0.1000 -0.0017
## 180 0.6597 nan 0.1000 -0.0004
## 200 0.6390 nan 0.1000 -0.0009
## 220 0.6208 nan 0.1000 -0.0021
## 240 0.6076 nan 0.1000 -0.0005
## 260 0.5954 nan 0.1000 -0.0026
## 280 0.5782 nan 0.1000 -0.0014
## 300 0.5651 nan 0.1000 -0.0011
## 320 0.5535 nan 0.1000 -0.0008
## 340 0.5413 nan 0.1000 -0.0009
## 360 0.5298 nan 0.1000 -0.0010
## 380 0.5159 nan 0.1000 -0.0003
## 400 0.5065 nan 0.1000 -0.0025
## 420 0.4975 nan 0.1000 -0.0007
## 440 0.4861 nan 0.1000 -0.0011
## 460 0.4754 nan 0.1000 -0.0003
## 480 0.4676 nan 0.1000 -0.0021
## 500 0.4586 nan 0.1000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2431 nan 0.1000 0.0248
## 2 1.2008 nan 0.1000 0.0189
## 3 1.1621 nan 0.1000 0.0166
## 4 1.1254 nan 0.1000 0.0153
## 5 1.0980 nan 0.1000 0.0086
## 6 1.0710 nan 0.1000 0.0106
## 7 1.0472 nan 0.1000 0.0114
## 8 1.0260 nan 0.1000 0.0081
## 9 1.0104 nan 0.1000 0.0049
## 10 0.9987 nan 0.1000 0.0019
## 20 0.9038 nan 0.1000 0.0005
## 40 0.8048 nan 0.1000 -0.0016
## 60 0.7492 nan 0.1000 -0.0011
## 80 0.7085 nan 0.1000 -0.0012
## 100 0.6688 nan 0.1000 -0.0018
## 120 0.6357 nan 0.1000 -0.0017
## 140 0.6061 nan 0.1000 -0.0016
## 160 0.5792 nan 0.1000 -0.0008
## 180 0.5498 nan 0.1000 -0.0021
## 200 0.5252 nan 0.1000 -0.0016
## 220 0.4979 nan 0.1000 0.0000
## 240 0.4779 nan 0.1000 -0.0005
## 260 0.4566 nan 0.1000 -0.0012
## 280 0.4393 nan 0.1000 -0.0008
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## 320 0.4063 nan 0.1000 -0.0011
## 340 0.3915 nan 0.1000 -0.0005
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## 380 0.3606 nan 0.1000 -0.0010
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## 460 0.3147 nan 0.1000 -0.0005
## 480 0.3025 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2450 nan 0.1000 0.0251
## 2 1.2075 nan 0.1000 0.0168
## 3 1.1658 nan 0.1000 0.0204
## 4 1.1290 nan 0.1000 0.0129
## 5 1.0992 nan 0.1000 0.0107
## 6 1.0729 nan 0.1000 0.0089
## 7 1.0529 nan 0.1000 0.0083
## 8 1.0373 nan 0.1000 0.0061
## 9 1.0179 nan 0.1000 0.0084
## 10 0.9986 nan 0.1000 0.0066
## 20 0.8999 nan 0.1000 0.0011
## 40 0.8053 nan 0.1000 -0.0006
## 60 0.7421 nan 0.1000 -0.0013
## 80 0.7014 nan 0.1000 -0.0022
## 100 0.6651 nan 0.1000 -0.0008
## 120 0.6281 nan 0.1000 -0.0010
## 140 0.5928 nan 0.1000 -0.0005
## 160 0.5640 nan 0.1000 -0.0016
## 180 0.5393 nan 0.1000 -0.0014
## 200 0.5187 nan 0.1000 -0.0014
## 220 0.4946 nan 0.1000 -0.0007
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## 260 0.4547 nan 0.1000 -0.0014
## 280 0.4304 nan 0.1000 -0.0009
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## 320 0.3967 nan 0.1000 -0.0011
## 340 0.3825 nan 0.1000 -0.0008
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## 380 0.3528 nan 0.1000 -0.0008
## 400 0.3402 nan 0.1000 -0.0013
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## 460 0.3015 nan 0.1000 -0.0003
## 480 0.2908 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2400 nan 0.1000 0.0265
## 2 1.1940 nan 0.1000 0.0179
## 3 1.1592 nan 0.1000 0.0113
## 4 1.1295 nan 0.1000 0.0104
## 5 1.1020 nan 0.1000 0.0114
## 6 1.0740 nan 0.1000 0.0113
## 7 1.0502 nan 0.1000 0.0086
## 8 1.0296 nan 0.1000 0.0087
## 9 1.0121 nan 0.1000 0.0069
## 10 0.9960 nan 0.1000 0.0048
## 20 0.8978 nan 0.1000 -0.0012
## 40 0.8082 nan 0.1000 -0.0004
## 60 0.7594 nan 0.1000 -0.0016
## 80 0.7176 nan 0.1000 -0.0010
## 100 0.6810 nan 0.1000 -0.0022
## 120 0.6462 nan 0.1000 -0.0007
## 140 0.6181 nan 0.1000 -0.0016
## 160 0.5799 nan 0.1000 -0.0006
## 180 0.5503 nan 0.1000 -0.0012
## 200 0.5213 nan 0.1000 -0.0020
## 220 0.4963 nan 0.1000 -0.0019
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## 280 0.4363 nan 0.1000 -0.0004
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## 320 0.4028 nan 0.1000 -0.0003
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## 380 0.3608 nan 0.1000 -0.0015
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2192 nan 0.2000 0.0308
## 2 1.1762 nan 0.2000 0.0164
## 3 1.1461 nan 0.2000 0.0094
## 4 1.1211 nan 0.2000 0.0096
## 5 1.0935 nan 0.2000 0.0105
## 6 1.0697 nan 0.2000 0.0098
## 7 1.0474 nan 0.2000 0.0068
## 8 1.0294 nan 0.2000 0.0059
## 9 1.0092 nan 0.2000 0.0091
## 10 0.9994 nan 0.2000 0.0016
## 20 0.9219 nan 0.2000 -0.0023
## 40 0.8602 nan 0.2000 -0.0026
## 60 0.8307 nan 0.2000 -0.0002
## 80 0.8017 nan 0.2000 -0.0009
## 100 0.7810 nan 0.2000 -0.0023
## 120 0.7628 nan 0.2000 0.0001
## 140 0.7522 nan 0.2000 -0.0017
## 160 0.7340 nan 0.2000 -0.0010
## 180 0.7263 nan 0.2000 -0.0011
## 200 0.7168 nan 0.2000 -0.0009
## 220 0.7094 nan 0.2000 -0.0036
## 240 0.7004 nan 0.2000 -0.0020
## 260 0.6890 nan 0.2000 -0.0012
## 280 0.6784 nan 0.2000 -0.0021
## 300 0.6731 nan 0.2000 -0.0011
## 320 0.6666 nan 0.2000 -0.0010
## 340 0.6578 nan 0.2000 0.0000
## 360 0.6541 nan 0.2000 -0.0007
## 380 0.6519 nan 0.2000 -0.0031
## 400 0.6409 nan 0.2000 -0.0009
## 420 0.6367 nan 0.2000 -0.0005
## 440 0.6332 nan 0.2000 -0.0028
## 460 0.6271 nan 0.2000 -0.0011
## 480 0.6210 nan 0.2000 -0.0016
## 500 0.6194 nan 0.2000 -0.0029
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2247 nan 0.2000 0.0319
## 2 1.1748 nan 0.2000 0.0239
## 3 1.1426 nan 0.2000 0.0127
## 4 1.1168 nan 0.2000 0.0123
## 5 1.0982 nan 0.2000 0.0052
## 6 1.0745 nan 0.2000 0.0113
## 7 1.0504 nan 0.2000 0.0126
## 8 1.0351 nan 0.2000 0.0040
## 9 1.0159 nan 0.2000 0.0099
## 10 1.0064 nan 0.2000 0.0009
## 20 0.9272 nan 0.2000 0.0014
## 40 0.8584 nan 0.2000 -0.0026
## 60 0.8221 nan 0.2000 -0.0030
## 80 0.7993 nan 0.2000 -0.0012
## 100 0.7847 nan 0.2000 -0.0009
## 120 0.7694 nan 0.2000 -0.0008
## 140 0.7584 nan 0.2000 -0.0018
## 160 0.7467 nan 0.2000 -0.0015
## 180 0.7372 nan 0.2000 -0.0023
## 200 0.7297 nan 0.2000 -0.0008
## 220 0.7200 nan 0.2000 -0.0009
## 240 0.7126 nan 0.2000 -0.0013
## 260 0.7085 nan 0.2000 -0.0012
## 280 0.7013 nan 0.2000 -0.0023
## 300 0.6905 nan 0.2000 -0.0021
## 320 0.6872 nan 0.2000 -0.0024
## 340 0.6778 nan 0.2000 -0.0013
## 360 0.6731 nan 0.2000 -0.0018
## 380 0.6670 nan 0.2000 -0.0007
## 400 0.6595 nan 0.2000 -0.0017
## 420 0.6551 nan 0.2000 -0.0008
## 440 0.6490 nan 0.2000 -0.0013
## 460 0.6476 nan 0.2000 -0.0039
## 480 0.6397 nan 0.2000 -0.0028
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2248 nan 0.2000 0.0306
## 2 1.1755 nan 0.2000 0.0172
## 3 1.1435 nan 0.2000 0.0135
## 4 1.1175 nan 0.2000 0.0093
## 5 1.0883 nan 0.2000 0.0144
## 6 1.0626 nan 0.2000 0.0105
## 7 1.0425 nan 0.2000 0.0090
## 8 1.0273 nan 0.2000 0.0044
## 9 1.0139 nan 0.2000 0.0048
## 10 0.9979 nan 0.2000 0.0070
## 20 0.9199 nan 0.2000 0.0002
## 40 0.8583 nan 0.2000 -0.0012
## 60 0.8298 nan 0.2000 -0.0025
## 80 0.8089 nan 0.2000 -0.0031
## 100 0.7850 nan 0.2000 -0.0021
## 120 0.7672 nan 0.2000 -0.0015
## 140 0.7575 nan 0.2000 -0.0031
## 160 0.7468 nan 0.2000 -0.0010
## 180 0.7380 nan 0.2000 -0.0018
## 200 0.7267 nan 0.2000 -0.0025
## 220 0.7173 nan 0.2000 -0.0015
## 240 0.7082 nan 0.2000 -0.0012
## 260 0.6992 nan 0.2000 -0.0009
## 280 0.6915 nan 0.2000 -0.0013
## 300 0.6814 nan 0.2000 -0.0019
## 320 0.6776 nan 0.2000 -0.0019
## 340 0.6686 nan 0.2000 -0.0011
## 360 0.6597 nan 0.2000 -0.0014
## 380 0.6531 nan 0.2000 -0.0022
## 400 0.6487 nan 0.2000 -0.0022
## 420 0.6428 nan 0.2000 -0.0029
## 440 0.6362 nan 0.2000 -0.0009
## 460 0.6336 nan 0.2000 -0.0023
## 480 0.6268 nan 0.2000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2080 nan 0.2000 0.0413
## 2 1.1478 nan 0.2000 0.0288
## 3 1.0970 nan 0.2000 0.0197
## 4 1.0510 nan 0.2000 0.0188
## 5 1.0197 nan 0.2000 0.0127
## 6 0.9994 nan 0.2000 0.0048
## 7 0.9820 nan 0.2000 0.0050
## 8 0.9603 nan 0.2000 0.0029
## 9 0.9498 nan 0.2000 0.0026
## 10 0.9380 nan 0.2000 0.0014
## 20 0.8510 nan 0.2000 -0.0028
## 40 0.7710 nan 0.2000 -0.0021
## 60 0.7287 nan 0.2000 -0.0026
## 80 0.6934 nan 0.2000 -0.0017
## 100 0.6633 nan 0.2000 -0.0025
## 120 0.6291 nan 0.2000 -0.0008
## 140 0.6043 nan 0.2000 -0.0047
## 160 0.5644 nan 0.2000 -0.0023
## 180 0.5384 nan 0.2000 -0.0024
## 200 0.5137 nan 0.2000 -0.0009
## 220 0.4927 nan 0.2000 -0.0037
## 240 0.4706 nan 0.2000 -0.0029
## 260 0.4441 nan 0.2000 -0.0020
## 280 0.4224 nan 0.2000 -0.0019
## 300 0.4073 nan 0.2000 -0.0017
## 320 0.3900 nan 0.2000 -0.0023
## 340 0.3791 nan 0.2000 -0.0037
## 360 0.3638 nan 0.2000 -0.0021
## 380 0.3432 nan 0.2000 -0.0012
## 400 0.3293 nan 0.2000 -0.0009
## 420 0.3174 nan 0.2000 -0.0012
## 440 0.3038 nan 0.2000 -0.0020
## 460 0.2941 nan 0.2000 -0.0008
## 480 0.2837 nan 0.2000 -0.0010
## 500 0.2746 nan 0.2000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2058 nan 0.2000 0.0412
## 2 1.1396 nan 0.2000 0.0247
## 3 1.0984 nan 0.2000 0.0149
## 4 1.0528 nan 0.2000 0.0125
## 5 1.0276 nan 0.2000 0.0091
## 6 1.0112 nan 0.2000 0.0021
## 7 0.9852 nan 0.2000 0.0033
## 8 0.9685 nan 0.2000 0.0045
## 9 0.9569 nan 0.2000 -0.0020
## 10 0.9425 nan 0.2000 0.0032
## 20 0.8571 nan 0.2000 0.0009
## 40 0.7872 nan 0.2000 -0.0021
## 60 0.7388 nan 0.2000 -0.0051
## 80 0.7001 nan 0.2000 -0.0016
## 100 0.6696 nan 0.2000 -0.0030
## 120 0.6358 nan 0.2000 -0.0027
## 140 0.6000 nan 0.2000 -0.0023
## 160 0.5734 nan 0.2000 -0.0018
## 180 0.5463 nan 0.2000 -0.0029
## 200 0.5221 nan 0.2000 -0.0014
## 220 0.4970 nan 0.2000 -0.0037
## 240 0.4741 nan 0.2000 -0.0025
## 260 0.4580 nan 0.2000 -0.0020
## 280 0.4413 nan 0.2000 -0.0014
## 300 0.4187 nan 0.2000 -0.0012
## 320 0.4013 nan 0.2000 -0.0021
## 340 0.3894 nan 0.2000 -0.0021
## 360 0.3755 nan 0.2000 -0.0006
## 380 0.3627 nan 0.2000 -0.0019
## 400 0.3509 nan 0.2000 -0.0030
## 420 0.3357 nan 0.2000 -0.0014
## 440 0.3241 nan 0.2000 -0.0010
## 460 0.3134 nan 0.2000 -0.0014
## 480 0.3029 nan 0.2000 -0.0008
## 500 0.2939 nan 0.2000 -0.0013
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2094 nan 0.2000 0.0361
## 2 1.1368 nan 0.2000 0.0331
## 3 1.0960 nan 0.2000 0.0113
## 4 1.0569 nan 0.2000 0.0160
## 5 1.0279 nan 0.2000 0.0084
## 6 1.0013 nan 0.2000 0.0073
## 7 0.9784 nan 0.2000 0.0082
## 8 0.9610 nan 0.2000 0.0050
## 9 0.9491 nan 0.2000 0.0030
## 10 0.9374 nan 0.2000 0.0003
## 20 0.8513 nan 0.2000 -0.0021
## 40 0.7765 nan 0.2000 -0.0024
## 60 0.7248 nan 0.2000 -0.0029
## 80 0.6987 nan 0.2000 -0.0009
## 100 0.6681 nan 0.2000 -0.0019
## 120 0.6283 nan 0.2000 -0.0022
## 140 0.6067 nan 0.2000 -0.0034
## 160 0.5785 nan 0.2000 -0.0021
## 180 0.5486 nan 0.2000 -0.0036
## 200 0.5247 nan 0.2000 -0.0028
## 220 0.5026 nan 0.2000 -0.0031
## 240 0.4831 nan 0.2000 -0.0027
## 260 0.4612 nan 0.2000 -0.0026
## 280 0.4422 nan 0.2000 -0.0023
## 300 0.4268 nan 0.2000 -0.0013
## 320 0.4085 nan 0.2000 -0.0010
## 340 0.3950 nan 0.2000 -0.0025
## 360 0.3744 nan 0.2000 -0.0018
## 380 0.3626 nan 0.2000 -0.0020
## 400 0.3465 nan 0.2000 -0.0008
## 420 0.3338 nan 0.2000 -0.0022
## 440 0.3248 nan 0.2000 -0.0024
## 460 0.3144 nan 0.2000 -0.0024
## 480 0.2992 nan 0.2000 -0.0005
## 500 0.2865 nan 0.2000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1908 nan 0.2000 0.0427
## 2 1.1149 nan 0.2000 0.0351
## 3 1.0688 nan 0.2000 0.0164
## 4 1.0212 nan 0.2000 0.0207
## 5 0.9944 nan 0.2000 0.0076
## 6 0.9672 nan 0.2000 0.0081
## 7 0.9412 nan 0.2000 0.0067
## 8 0.9224 nan 0.2000 0.0058
## 9 0.9058 nan 0.2000 0.0038
## 10 0.8923 nan 0.2000 -0.0001
## 20 0.7961 nan 0.2000 -0.0008
## 40 0.7010 nan 0.2000 -0.0017
## 60 0.6334 nan 0.2000 -0.0047
## 80 0.5772 nan 0.2000 -0.0023
## 100 0.5496 nan 0.2000 -0.0046
## 120 0.5041 nan 0.2000 -0.0031
## 140 0.4638 nan 0.2000 -0.0047
## 160 0.4256 nan 0.2000 -0.0014
## 180 0.3916 nan 0.2000 -0.0033
## 200 0.3629 nan 0.2000 -0.0017
## 220 0.3309 nan 0.2000 0.0003
## 240 0.3078 nan 0.2000 -0.0009
## 260 0.2856 nan 0.2000 -0.0019
## 280 0.2681 nan 0.2000 -0.0029
## 300 0.2485 nan 0.2000 -0.0010
## 320 0.2308 nan 0.2000 -0.0011
## 340 0.2186 nan 0.2000 -0.0018
## 360 0.2048 nan 0.2000 -0.0008
## 380 0.1904 nan 0.2000 -0.0008
## 400 0.1788 nan 0.2000 -0.0002
## 420 0.1680 nan 0.2000 -0.0006
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## 460 0.1451 nan 0.2000 -0.0006
## 480 0.1369 nan 0.2000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2056 nan 0.2000 0.0347
## 2 1.1331 nan 0.2000 0.0300
## 3 1.0757 nan 0.2000 0.0262
## 4 1.0360 nan 0.2000 0.0158
## 5 1.0060 nan 0.2000 0.0102
## 6 0.9812 nan 0.2000 0.0061
## 7 0.9554 nan 0.2000 0.0092
## 8 0.9419 nan 0.2000 -0.0045
## 9 0.9228 nan 0.2000 0.0036
## 10 0.9080 nan 0.2000 0.0038
## 20 0.8122 nan 0.2000 -0.0024
## 40 0.7196 nan 0.2000 -0.0013
## 60 0.6444 nan 0.2000 -0.0016
## 80 0.5872 nan 0.2000 -0.0015
## 100 0.5363 nan 0.2000 -0.0017
## 120 0.4911 nan 0.2000 -0.0006
## 140 0.4443 nan 0.2000 -0.0036
## 160 0.4075 nan 0.2000 -0.0015
## 180 0.3739 nan 0.2000 -0.0037
## 200 0.3772 nan 0.2000 -0.0012
## 220 0.3273 nan 0.2000 -0.0016
## 240 0.3003 nan 0.2000 -0.0006
## 260 0.2774 nan 0.2000 -0.0010
## 280 0.2579 nan 0.2000 -0.0012
## 300 0.2380 nan 0.2000 -0.0006
## 320 0.2183 nan 0.2000 -0.0013
## 340 0.2050 nan 0.2000 -0.0006
## 360 0.1914 nan 0.2000 -0.0002
## 380 0.1787 nan 0.2000 -0.0015
## 400 0.1665 nan 0.2000 -0.0013
## 420 0.1559 nan 0.2000 -0.0007
## 440 0.1460 nan 0.2000 -0.0013
## 460 0.1361 nan 0.2000 -0.0014
## 480 0.1273 nan 0.2000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1948 nan 0.2000 0.0432
## 2 1.1169 nan 0.2000 0.0323
## 3 1.0731 nan 0.2000 0.0129
## 4 1.0264 nan 0.2000 0.0189
## 5 0.9970 nan 0.2000 0.0073
## 6 0.9792 nan 0.2000 0.0019
## 7 0.9626 nan 0.2000 0.0009
## 8 0.9369 nan 0.2000 0.0020
## 9 0.9185 nan 0.2000 0.0022
## 10 0.9040 nan 0.2000 0.0045
## 20 0.8123 nan 0.2000 -0.0019
## 40 0.7199 nan 0.2000 -0.0008
## 60 0.6546 nan 0.2000 -0.0015
## 80 0.5996 nan 0.2000 -0.0046
## 100 0.5444 nan 0.2000 -0.0034
## 120 0.5031 nan 0.2000 -0.0016
## 140 0.4657 nan 0.2000 -0.0026
## 160 0.4283 nan 0.2000 -0.0012
## 180 0.3958 nan 0.2000 -0.0037
## 200 0.3634 nan 0.2000 -0.0018
## 220 0.3349 nan 0.2000 -0.0029
## 240 0.3112 nan 0.2000 -0.0033
## 260 0.2865 nan 0.2000 -0.0020
## 280 0.2682 nan 0.2000 -0.0002
## 300 0.2490 nan 0.2000 -0.0004
## 320 0.2329 nan 0.2000 -0.0013
## 340 0.2179 nan 0.2000 -0.0017
## 360 0.2022 nan 0.2000 -0.0011
## 380 0.1896 nan 0.2000 -0.0022
## 400 0.1803 nan 0.2000 -0.0012
## 420 0.1693 nan 0.2000 -0.0009
## 440 0.1594 nan 0.2000 -0.0006
## 460 0.1488 nan 0.2000 -0.0010
## 480 0.1401 nan 0.2000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1976 nan 0.3000 0.0422
## 2 1.1515 nan 0.3000 0.0196
## 3 1.1085 nan 0.3000 0.0179
## 4 1.0697 nan 0.3000 0.0178
## 5 1.0347 nan 0.3000 0.0137
## 6 1.0169 nan 0.3000 0.0035
## 7 0.9958 nan 0.3000 0.0073
## 8 0.9801 nan 0.3000 0.0037
## 9 0.9728 nan 0.3000 -0.0036
## 10 0.9609 nan 0.3000 0.0063
## 20 0.8941 nan 0.3000 0.0000
## 40 0.8337 nan 0.3000 -0.0020
## 60 0.8029 nan 0.3000 -0.0042
## 80 0.7870 nan 0.3000 -0.0028
## 100 0.7683 nan 0.3000 -0.0019
## 120 0.7477 nan 0.3000 -0.0023
## 140 0.7329 nan 0.3000 -0.0021
## 160 0.7231 nan 0.3000 -0.0020
## 180 0.7031 nan 0.3000 -0.0049
## 200 0.6885 nan 0.3000 -0.0038
## 220 0.6778 nan 0.3000 -0.0025
## 240 0.6654 nan 0.3000 -0.0022
## 260 0.6536 nan 0.3000 -0.0027
## 280 0.6448 nan 0.3000 -0.0026
## 300 0.6323 nan 0.3000 -0.0033
## 320 0.6229 nan 0.3000 -0.0041
## 340 0.6172 nan 0.3000 -0.0053
## 360 0.6062 nan 0.3000 -0.0022
## 380 0.6021 nan 0.3000 -0.0040
## 400 0.5916 nan 0.3000 -0.0027
## 420 0.5845 nan 0.3000 -0.0033
## 440 0.5762 nan 0.3000 -0.0012
## 460 0.5693 nan 0.3000 -0.0022
## 480 0.5639 nan 0.3000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1969 nan 0.3000 0.0455
## 2 1.1452 nan 0.3000 0.0237
## 3 1.1030 nan 0.3000 0.0170
## 4 1.0562 nan 0.3000 0.0129
## 5 1.0330 nan 0.3000 0.0117
## 6 1.0243 nan 0.3000 -0.0071
## 7 1.0042 nan 0.3000 0.0082
## 8 0.9869 nan 0.3000 0.0047
## 9 0.9728 nan 0.3000 0.0050
## 10 0.9619 nan 0.3000 0.0026
## 20 0.8796 nan 0.3000 -0.0009
## 40 0.8254 nan 0.3000 -0.0044
## 60 0.7991 nan 0.3000 -0.0054
## 80 0.7734 nan 0.3000 -0.0043
## 100 0.7549 nan 0.3000 -0.0046
## 120 0.7343 nan 0.3000 -0.0031
## 140 0.7232 nan 0.3000 -0.0018
## 160 0.7083 nan 0.3000 -0.0038
## 180 0.6950 nan 0.3000 -0.0025
## 200 0.6875 nan 0.3000 -0.0007
## 220 0.6785 nan 0.3000 -0.0023
## 240 0.6705 nan 0.3000 -0.0010
## 260 0.6633 nan 0.3000 -0.0015
## 280 0.6485 nan 0.3000 -0.0022
## 300 0.6380 nan 0.3000 -0.0037
## 320 0.6297 nan 0.3000 -0.0035
## 340 0.6195 nan 0.3000 -0.0021
## 360 0.6117 nan 0.3000 -0.0005
## 380 0.6033 nan 0.3000 -0.0006
## 400 0.5949 nan 0.3000 -0.0028
## 420 0.5900 nan 0.3000 -0.0036
## 440 0.5839 nan 0.3000 -0.0010
## 460 0.5754 nan 0.3000 -0.0041
## 480 0.5707 nan 0.3000 -0.0021
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2059 nan 0.3000 0.0421
## 2 1.1561 nan 0.3000 0.0194
## 3 1.1093 nan 0.3000 0.0131
## 4 1.0654 nan 0.3000 0.0109
## 5 1.0358 nan 0.3000 0.0142
## 6 1.0110 nan 0.3000 0.0104
## 7 0.9979 nan 0.3000 0.0019
## 8 0.9817 nan 0.3000 0.0050
## 9 0.9570 nan 0.3000 0.0028
## 10 0.9479 nan 0.3000 0.0015
## 20 0.8853 nan 0.3000 0.0015
## 40 0.8314 nan 0.3000 -0.0004
## 60 0.7979 nan 0.3000 -0.0061
## 80 0.7743 nan 0.3000 -0.0033
## 100 0.7580 nan 0.3000 -0.0029
## 120 0.7400 nan 0.3000 -0.0009
## 140 0.7287 nan 0.3000 -0.0042
## 160 0.7131 nan 0.3000 -0.0034
## 180 0.6985 nan 0.3000 -0.0015
## 200 0.6865 nan 0.3000 -0.0010
## 220 0.6804 nan 0.3000 -0.0017
## 240 0.6692 nan 0.3000 -0.0040
## 260 0.6606 nan 0.3000 -0.0050
## 280 0.6502 nan 0.3000 -0.0024
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## 320 0.6365 nan 0.3000 -0.0026
## 340 0.6238 nan 0.3000 -0.0021
## 360 0.6162 nan 0.3000 -0.0051
## 380 0.6089 nan 0.3000 -0.0031
## 400 0.6033 nan 0.3000 -0.0032
## 420 0.5960 nan 0.3000 -0.0014
## 440 0.5936 nan 0.3000 -0.0020
## 460 0.5844 nan 0.3000 -0.0002
## 480 0.5787 nan 0.3000 -0.0027
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1852 nan 0.3000 0.0455
## 2 1.1112 nan 0.3000 0.0325
## 3 1.0663 nan 0.3000 0.0085
## 4 1.0264 nan 0.3000 0.0096
## 5 0.9870 nan 0.3000 0.0101
## 6 0.9587 nan 0.3000 0.0111
## 7 0.9456 nan 0.3000 0.0008
## 8 0.9368 nan 0.3000 -0.0080
## 9 0.9271 nan 0.3000 -0.0047
## 10 0.9119 nan 0.3000 0.0011
## 20 0.8400 nan 0.3000 -0.0025
## 40 0.7323 nan 0.3000 -0.0007
## 60 0.6816 nan 0.3000 -0.0077
## 80 0.6560 nan 0.3000 -0.0033
## 100 1.5037 nan 0.3000 -0.0028
## 120 1.4806 nan 0.3000 -0.0027
## 140 1.4549 nan 0.3000 -0.0020
## 160 1.4426 nan 0.3000 -0.0063
## 180 1.4326 nan 0.3000 -0.0021
## 200 1.4187 nan 0.3000 -0.0043
## 220 1.3849 nan 0.3000 -0.0007
## 240 1.3739 nan 0.3000 -0.0006
## 260 1.3669 nan 0.3000 -0.0027
## 280 1.3528 nan 0.3000 -0.0023
## 300 1.3497 nan 0.3000 -0.0005
## 320 1.3431 nan 0.3000 -0.0013
## 340 1.3315 nan 0.3000 -0.0010
## 360 1.3180 nan 0.3000 -0.0008
## 380 1.3158 nan 0.3000 -0.0000
## 400 1.2966 nan 0.3000 -0.0033
## 420 1.2941 nan 0.3000 -0.0031
## 440 1.2909 nan 0.3000 -0.0001
## 460 1.2838 nan 0.3000 -0.0003
## 480 1.2808 nan 0.3000 -0.0019
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1701 nan 0.3000 0.0583
## 2 1.1008 nan 0.3000 0.0327
## 3 1.0479 nan 0.3000 0.0107
## 4 1.0100 nan 0.3000 0.0093
## 5 0.9846 nan 0.3000 0.0092
## 6 0.9564 nan 0.3000 0.0100
## 7 0.9333 nan 0.3000 0.0087
## 8 0.9251 nan 0.3000 -0.0093
## 9 0.9162 nan 0.3000 -0.0040
## 10 0.9042 nan 0.3000 -0.0010
## 20 0.8228 nan 0.3000 -0.0007
## 40 0.7340 nan 0.3000 -0.0036
## 60 0.6624 nan 0.3000 -0.0022
## 80 0.6044 nan 0.3000 -0.0040
## 100 0.6325 nan 0.3000 -0.0053
## 120 0.5887 nan 0.3000 -0.0063
## 140 0.5557 nan 0.3000 -0.0057
## 160 0.5306 nan 0.3000 -0.0431
## 180 1171456550472.2358 nan 0.3000 0.0001
## 200 1171456550473.5171 nan 0.3000 0.0001
## 220 1171457221375.7810 nan 0.3000 -0.0009
## 240 1171457234185.0596 nan 0.3000 0.0001
## 260 inf nan 0.3000 nan
## 280 inf nan 0.3000 nan
## 300 inf nan 0.3000 nan
## 320 inf nan 0.3000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1752 nan 0.3000 0.0473
## 2 1.1059 nan 0.3000 0.0282
## 3 1.0499 nan 0.3000 0.0255
## 4 1.0049 nan 0.3000 0.0130
## 5 0.9746 nan 0.3000 0.0074
## 6 0.9544 nan 0.3000 0.0030
## 7 0.9379 nan 0.3000 0.0010
## 8 0.9261 nan 0.3000 -0.0023
## 9 0.9127 nan 0.3000 0.0030
## 10 0.9015 nan 0.3000 -0.0019
## 20 0.8230 nan 0.3000 -0.0049
## 40 0.7396 nan 0.3000 -0.0052
## 60 0.6944 nan 0.3000 -0.0043
## 80 0.6384 nan 0.3000 -0.0016
## 100 0.6015 nan 0.3000 -0.0010
## 120 0.5650 nan 0.3000 -0.0020
## 140 0.5354 nan 0.3000 -0.0013
## 160 0.4988 nan 0.3000 -0.0024
## 180 0.4682 nan 0.3000 -0.0055
## 200 0.4384 nan 0.3000 -0.0037
## 220 0.4188 nan 0.3000 -0.0036
## 240 0.3925 nan 0.3000 -0.0047
## 260 0.3689 nan 0.3000 -0.0017
## 280 0.3429 nan 0.3000 -0.0014
## 300 0.3253 nan 0.3000 -0.0025
## 320 0.3083 nan 0.3000 -0.0032
## 340 0.2943 nan 0.3000 -0.0005
## 360 0.2799 nan 0.3000 -0.0011
## 380 0.2643 nan 0.3000 -0.0014
## 400 0.2529 nan 0.3000 -0.0017
## 420 0.2397 nan 0.3000 -0.0018
## 440 0.2298 nan 0.3000 -0.0006
## 460 0.2196 nan 0.3000 -0.0024
## 480 0.2072 nan 0.3000 -0.0018
## 500 0.1950 nan 0.3000 -0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1574 nan 0.3000 0.0710
## 2 1.0678 nan 0.3000 0.0379
## 3 1.0106 nan 0.3000 0.0231
## 4 0.9796 nan 0.3000 0.0076
## 5 0.9555 nan 0.3000 0.0048
## 6 0.9340 nan 0.3000 0.0007
## 7 0.9034 nan 0.3000 0.0080
## 8 0.8871 nan 0.3000 0.0036
## 9 0.8668 nan 0.3000 0.0033
## 10 0.8562 nan 0.3000 -0.0030
## 20 0.7773 nan 0.3000 -0.0056
## 40 0.6859 nan 0.3000 -0.0266
## 60 0.5933 nan 0.3000 -0.0020
## 80 0.5039 nan 0.3000 0.0001
## 100 0.4518 nan 0.3000 -0.0017
## 120 0.4117 nan 0.3000 -0.0042
## 140 0.3666 nan 0.3000 -0.0016
## 160 0.3410 nan 0.3000 -0.0042
## 180 0.2997 nan 0.3000 -0.0036
## 200 0.2681 nan 0.3000 -0.0013
## 220 0.2434 nan 0.3000 -0.0011
## 240 0.2164 nan 0.3000 -0.0009
## 260 0.1965 nan 0.3000 -0.0018
## 280 0.1808 nan 0.3000 -0.0009
## 300 0.1632 nan 0.3000 -0.0017
## 320 0.1531 nan 0.3000 -0.0006
## 340 0.1393 nan 0.3000 -0.0006
## 360 0.1283 nan 0.3000 -0.0014
## 380 0.1169 nan 0.3000 -0.0011
## 400 0.1088 nan 0.3000 -0.0010
## 420 0.0976 nan 0.3000 -0.0002
## 440 0.0914 nan 0.3000 -0.0004
## 460 0.0844 nan 0.3000 -0.0003
## 480 0.0776 nan 0.3000 -0.0002
## 500 0.0726 nan 0.3000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1461 nan 0.3000 0.0680
## 2 1.0700 nan 0.3000 0.0313
## 3 1.0015 nan 0.3000 0.0141
## 4 0.9621 nan 0.3000 0.0123
## 5 0.9384 nan 0.3000 0.0015
## 6 0.9124 nan 0.3000 0.0064
## 7 0.8910 nan 0.3000 -0.0026
## 8 0.8648 nan 0.3000 0.0035
## 9 0.8555 nan 0.3000 -0.0038
## 10 0.8490 nan 0.3000 -0.0024
## 20 0.7729 nan 0.3000 -0.0067
## 40 0.6477 nan 0.3000 -0.0020
## 60 0.5495 nan 0.3000 -0.0056
## 80 0.4976 nan 0.3000 -0.0038
## 100 0.4357 nan 0.3000 -0.0034
## 120 0.3874 nan 0.3000 -0.0030
## 140 0.3458 nan 0.3000 -0.0016
## 160 0.3108 nan 0.3000 -0.0004
## 180 0.2823 nan 0.3000 -0.0026
## 200 0.2540 nan 0.3000 -0.0039
## 220 0.2263 nan 0.3000 -0.0008
## 240 0.2089 nan 0.3000 -0.0018
## 260 0.1931 nan 0.3000 -0.0019
## 280 0.1751 nan 0.3000 -0.0025
## 300 0.1562 nan 0.3000 -0.0001
## 320 0.1401 nan 0.3000 -0.0010
## 340 0.1274 nan 0.3000 -0.0010
## 360 0.1184 nan 0.3000 -0.0007
## 380 0.1084 nan 0.3000 -0.0008
## 400 0.1009 nan 0.3000 -0.0012
## 420 0.0915 nan 0.3000 -0.0007
## 440 0.0844 nan 0.3000 -0.0006
## 460 0.0787 nan 0.3000 -0.0008
## 480 0.0739 nan 0.3000 -0.0015
## 500 0.0675 nan 0.3000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1512 nan 0.3000 0.0688
## 2 1.0611 nan 0.3000 0.0389
## 3 1.0059 nan 0.3000 0.0244
## 4 0.9664 nan 0.3000 0.0051
## 5 0.9372 nan 0.3000 0.0028
## 6 0.9182 nan 0.3000 0.0015
## 7 0.8990 nan 0.3000 0.0005
## 8 0.8802 nan 0.3000 0.0015
## 9 0.8608 nan 0.3000 0.0066
## 10 0.8444 nan 0.3000 0.0026
## 20 0.7680 nan 0.3000 -0.0045
## 40 0.6492 nan 0.3000 -0.0036
## 60 0.5829 nan 0.3000 -0.0080
## 80 0.5257 nan 0.3000 -0.0034
## 100 0.4490 nan 0.3000 -0.0041
## 120 0.3853 nan 0.3000 -0.0014
## 140 0.3483 nan 0.3000 -0.0031
## 160 0.3128 nan 0.3000 -0.0048
## 180 0.2876 nan 0.3000 -0.0020
## 200 0.2589 nan 0.3000 -0.0029
## 220 0.2334 nan 0.3000 -0.0019
## 240 0.2079 nan 0.3000 -0.0010
## 260 0.1925 nan 0.3000 -0.0010
## 280 0.1741 nan 0.3000 -0.0015
## 300 0.1601 nan 0.3000 -0.0017
## 320 0.1463 nan 0.3000 -0.0015
## 340 0.1333 nan 0.3000 -0.0011
## 360 0.1198 nan 0.3000 -0.0007
## 380 0.1129 nan 0.3000 -0.0013
## 400 0.1047 nan 0.3000 -0.0005
## 420 0.0941 nan 0.3000 -0.0010
## 440 0.0865 nan 0.3000 -0.0009
## 460 0.0791 nan 0.3000 -0.0010
## 480 0.0722 nan 0.3000 -0.0004
## 500 0.0666 nan 0.3000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1525 nan 0.5000 0.0649
## 2 1.0875 nan 0.5000 0.0253
## 3 1.0346 nan 0.5000 0.0207
## 4 0.9898 nan 0.5000 0.0199
## 5 0.9755 nan 0.5000 -0.0057
## 6 0.9640 nan 0.5000 -0.0025
## 7 0.9498 nan 0.5000 -0.0042
## 8 0.9338 nan 0.5000 -0.0002
## 9 0.9126 nan 0.5000 0.0081
## 10 0.9117 nan 0.5000 -0.0088
## 20 0.8606 nan 0.5000 -0.0148
## 40 0.8126 nan 0.5000 -0.0137
## 60 0.7747 nan 0.5000 -0.0066
## 80 0.7467 nan 0.5000 -0.0077
## 100 0.7186 nan 0.5000 -0.0019
## 120 0.6875 nan 0.5000 -0.0036
## 140 0.6725 nan 0.5000 -0.0036
## 160 0.7299 nan 0.5000 -0.0068
## 180 1716.2939 nan 0.5000 -0.0011
## 200 1716.2841 nan 0.5000 0.0000
## 220 1716.2901 nan 0.5000 -0.0026
## 240 1716.2782 nan 0.5000 0.0004
## 260 1716.2708 nan 0.5000 -0.0022
## 280 1716.2592 nan 0.5000 -0.0011
## 300 1716.2484 nan 0.5000 -0.0001
## 320 1716.2452 nan 0.5000 -0.0000
## 340 1716.2417 nan 0.5000 -0.0006
## 360 1716.2379 nan 0.5000 -0.0000
## 380 1716.2320 nan 0.5000 -0.0015
## 400 1716.2260 nan 0.5000 -0.0024
## 420 1716.2253 nan 0.5000 -0.0064
## 440 1716.2185 nan 0.5000 -0.0000
## 460 1716.2142 nan 0.5000 -0.0016
## 480 1716.2140 nan 0.5000 0.0010
## 500 1716.2007 nan 0.5000 -0.0049
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1663 nan 0.5000 0.0637
## 2 1.0989 nan 0.5000 0.0306
## 3 1.0627 nan 0.5000 0.0049
## 4 1.0032 nan 0.5000 0.0261
## 5 0.9780 nan 0.5000 0.0080
## 6 0.9450 nan 0.5000 0.0135
## 7 0.9401 nan 0.5000 -0.0021
## 8 0.9348 nan 0.5000 -0.0042
## 9 0.9170 nan 0.5000 0.0076
## 10 0.9186 nan 0.5000 -0.0127
## 20 0.8529 nan 0.5000 -0.0030
## 40 0.7989 nan 0.5000 -0.0060
## 60 0.7473 nan 0.5000 -0.0009
## 80 0.7368 nan 0.5000 -0.0083
## 100 0.7161 nan 0.5000 -0.0054
## 120 0.7016 nan 0.5000 -0.0079
## 140 0.6772 nan 0.5000 -0.0077
## 160 0.6601 nan 0.5000 -0.0043
## 180 0.6508 nan 0.5000 -0.0029
## 200 0.6339 nan 0.5000 -0.0040
## 220 0.6188 nan 0.5000 -0.0062
## 240 0.6079 nan 0.5000 -0.0046
## 260 0.5946 nan 0.5000 -0.0102
## 280 0.5858 nan 0.5000 -0.0055
## 300 0.5796 nan 0.5000 -0.0055
## 320 0.5691 nan 0.5000 -0.0046
## 340 0.5562 nan 0.5000 -0.0008
## 360 0.5420 nan 0.5000 -0.0036
## 380 0.5366 nan 0.5000 -0.0027
## 400 0.5271 nan 0.5000 -0.0070
## 420 0.5184 nan 0.5000 -0.0063
## 440 0.5173 nan 0.5000 -0.0082
## 460 0.5028 nan 0.5000 -0.0032
## 480 0.4939 nan 0.5000 -0.0007
## 500 0.4943 nan 0.5000 -0.0030
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1543 nan 0.5000 0.0589
## 2 1.0898 nan 0.5000 0.0268
## 3 1.0365 nan 0.5000 0.0207
## 4 0.9952 nan 0.5000 0.0061
## 5 0.9765 nan 0.5000 -0.0004
## 6 0.9587 nan 0.5000 -0.0002
## 7 0.9418 nan 0.5000 -0.0026
## 8 0.9233 nan 0.5000 0.0065
## 9 0.9303 nan 0.5000 -0.0158
## 10 0.9219 nan 0.5000 0.0023
## 20 0.8634 nan 0.5000 -0.0021
## 40 0.8012 nan 0.5000 -0.0046
## 60 0.7725 nan 0.5000 -0.0070
## 80 0.7457 nan 0.5000 -0.0056
## 100 0.7187 nan 0.5000 -0.0021
## 120 0.6977 nan 0.5000 -0.0053
## 140 0.6878 nan 0.5000 -0.0084
## 160 0.6602 nan 0.5000 -0.0021
## 180 0.6455 nan 0.5000 -0.0045
## 200 0.6187 nan 0.5000 -0.0034
## 220 0.6088 nan 0.5000 -0.0046
## 240 0.6016 nan 0.5000 -0.0058
## 260 0.5923 nan 0.5000 -0.0015
## 280 0.5837 nan 0.5000 -0.0067
## 300 0.5867 nan 0.5000 -0.0024
## 320 0.5735 nan 0.5000 -0.0060
## 340 0.5625 nan 0.5000 -0.0021
## 360 0.5550 nan 0.5000 -0.0034
## 380 0.5472 nan 0.5000 -0.0038
## 400 0.5368 nan 0.5000 -0.0011
## 420 0.5299 nan 0.5000 -0.0071
## 440 0.5230 nan 0.5000 -0.0041
## 460 0.5240 nan 0.5000 -0.0106
## 480 0.5086 nan 0.5000 -0.0051
## 500 0.4938 nan 0.5000 -0.0041
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1295 nan 0.5000 0.0840
## 2 1.0371 nan 0.5000 0.0361
## 3 0.9830 nan 0.5000 0.0176
## 4 0.9487 nan 0.5000 0.0028
## 5 0.9214 nan 0.5000 -0.0043
## 6 0.8987 nan 0.5000 0.0060
## 7 0.8876 nan 0.5000 -0.0035
## 8 0.8755 nan 0.5000 -0.0058
## 9 0.8751 nan 0.5000 -0.0114
## 10 0.8656 nan 0.5000 -0.0040
## 20 0.7769 nan 0.5000 -0.0117
## 40 0.7044 nan 0.5000 -0.0159
## 60 0.6295 nan 0.5000 -0.0124
## 80 0.5649 nan 0.5000 -0.0085
## 100 0.4889 nan 0.5000 -0.0048
## 120 0.4411 nan 0.5000 -0.0071
## 140 0.3963 nan 0.5000 -0.0048
## 160 0.3469 nan 0.5000 -0.0030
## 180 0.3191 nan 0.5000 -0.0040
## 200 0.2952 nan 0.5000 -0.0006
## 220 0.2835 nan 0.5000 -0.0074
## 240 0.2468 nan 0.5000 -0.0029
## 260 0.2269 nan 0.5000 -0.0027
## 280 0.1978 nan 0.5000 -0.0019
## 300 0.1817 nan 0.5000 -0.0015
## 320 0.1636 nan 0.5000 -0.0014
## 340 0.1473 nan 0.5000 -0.0013
## 360 0.1338 nan 0.5000 -0.0010
## 380 0.1221 nan 0.5000 -0.0004
## 400 0.1099 nan 0.5000 -0.0028
## 420 0.1012 nan 0.5000 -0.0003
## 440 0.0952 nan 0.5000 -0.0013
## 460 0.0899 nan 0.5000 -0.0012
## 480 0.0849 nan 0.5000 -0.0012
## 500 0.0782 nan 0.5000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1207 nan 0.5000 0.0748
## 2 1.0155 nan 0.5000 0.0349
## 3 0.9816 nan 0.5000 0.0079
## 4 0.9573 nan 0.5000 0.0020
## 5 0.9348 nan 0.5000 0.0067
## 6 0.9093 nan 0.5000 0.0044
## 7 0.8896 nan 0.5000 -0.0021
## 8 0.8720 nan 0.5000 -0.0050
## 9 0.8616 nan 0.5000 -0.0029
## 10 0.8452 nan 0.5000 0.0011
## 20 0.7620 nan 0.5000 -0.0048
## 40 0.6820 nan 0.5000 -0.0128
## 60 0.6016 nan 0.5000 -0.0060
## 80 0.5633 nan 0.5000 -0.0082
## 100 0.5151 nan 0.5000 -0.0038
## 120 0.4646 nan 0.5000 -0.0109
## 140 0.4209 nan 0.5000 -0.0092
## 160 0.3780 nan 0.5000 -0.0084
## 180 0.3378 nan 0.5000 -0.0002
## 200 0.3243 nan 0.5000 -0.0019
## 220 0.2979 nan 0.5000 -0.0100
## 240 0.2622 nan 0.5000 -0.0028
## 260 0.2395 nan 0.5000 -0.0065
## 280 0.2207 nan 0.5000 -0.0040
## 300 0.2006 nan 0.5000 -0.0016
## 320 0.1813 nan 0.5000 -0.0025
## 340 0.1681 nan 0.5000 -0.0024
## 360 0.1586 nan 0.5000 0.0001
## 380 0.1472 nan 0.5000 -0.0009
## 400 0.1363 nan 0.5000 -0.0038
## 420 0.1231 nan 0.5000 -0.0013
## 440 0.1174 nan 0.5000 -0.0042
## 460 0.1050 nan 0.5000 -0.0010
## 480 0.0960 nan 0.5000 -0.0001
## 500 0.0905 nan 0.5000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1093 nan 0.5000 0.0706
## 2 1.0138 nan 0.5000 0.0437
## 3 0.9690 nan 0.5000 0.0140
## 4 0.9372 nan 0.5000 0.0100
## 5 0.9144 nan 0.5000 0.0077
## 6 0.8957 nan 0.5000 0.0012
## 7 0.8741 nan 0.5000 -0.0038
## 8 0.8613 nan 0.5000 -0.0031
## 9 0.8506 nan 0.5000 -0.0036
## 10 0.8401 nan 0.5000 -0.0045
## 20 0.7748 nan 0.5000 -0.0020
## 40 0.6877 nan 0.5000 -0.0093
## 60 0.6360 nan 0.5000 -0.0187
## 80 0.5677 nan 0.5000 -0.0032
## 100 0.5203 nan 0.5000 -0.0079
## 120 0.4711 nan 0.5000 -0.0099
## 140 0.4194 nan 0.5000 -0.0027
## 160 0.3960 nan 0.5000 -0.0041
## 180 0.3549 nan 0.5000 -0.0084
## 200 0.3240 nan 0.5000 -0.0034
## 220 0.6802 nan 0.5000 -0.2890
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0763 nan 0.5000 0.0994
## 2 0.9961 nan 0.5000 0.0292
## 3 0.9262 nan 0.5000 0.0261
## 4 0.9056 nan 0.5000 -0.0033
## 5 0.8717 nan 0.5000 0.0055
## 6 0.8608 nan 0.5000 -0.0064
## 7 0.8375 nan 0.5000 -0.0024
## 8 0.8237 nan 0.5000 -0.0126
## 9 0.8121 nan 0.5000 -0.0074
## 10 0.8055 nan 0.5000 -0.0180
## 20 0.7026 nan 0.5000 -0.0084
## 40 0.5635 nan 0.5000 -0.0046
## 60 inf nan 0.5000 nan
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1334 nan 0.5000 0.0607
## 2 1.0117 nan 0.5000 0.0526
## 3 0.9480 nan 0.5000 0.0202
## 4 0.9122 nan 0.5000 0.0071
## 5 0.8869 nan 0.5000 -0.0072
## 6 0.8641 nan 0.5000 -0.0085
## 7 0.8422 nan 0.5000 -0.0042
## 8 0.8230 nan 0.5000 -0.0093
## 9 0.8088 nan 0.5000 -0.0055
## 10 0.7955 nan 0.5000 -0.0099
## 20 0.7025 nan 0.5000 -0.0166
## 40 0.5545 nan 0.5000 -0.0093
## 60 0.4491 nan 0.5000 -0.0020
## 80 0.3908 nan 0.5000 -0.0088
## 100 0.3030 nan 0.5000 -0.0061
## 120 0.2542 nan 0.5000 -0.0066
## 140 0.2175 nan 0.5000 -0.0038
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0768 nan 0.5000 0.0945
## 2 0.9682 nan 0.5000 0.0416
## 3 0.9184 nan 0.5000 0.0178
## 4 0.8762 nan 0.5000 0.0050
## 5 0.8730 nan 0.5000 -0.0137
## 6 0.8634 nan 0.5000 -0.0170
## 7 0.8466 nan 0.5000 -0.0150
## 8 0.8433 nan 0.5000 -0.0216
## 9 0.8238 nan 0.5000 -0.0082
## 10 0.8019 nan 0.5000 -0.0057
## 20 0.6814 nan 0.5000 -0.0089
## 40 4.1041 nan 0.5000 -0.0107
## 60 3.9566 nan 0.5000 -0.0179
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
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## 280 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1391 nan 1.0000 0.0372
## 2 1.0529 nan 1.0000 0.0151
## 3 1.0032 nan 1.0000 0.0222
## 4 0.9934 nan 1.0000 -0.0208
## 5 1.0069 nan 1.0000 -0.0294
## 6 0.9609 nan 1.0000 0.0058
## 7 0.9346 nan 1.0000 0.0124
## 8 0.9308 nan 1.0000 -0.0077
## 9 0.9249 nan 1.0000 -0.0096
## 10 0.9369 nan 1.0000 -0.0303
## 20 0.8722 nan 1.0000 -0.0064
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1279 nan 1.0000 0.0464
## 2 1.0953 nan 1.0000 -0.0026
## 3 1.0060 nan 1.0000 0.0363
## 4 0.9876 nan 1.0000 -0.0055
## 5 0.9605 nan 1.0000 0.0077
## 6 0.9465 nan 1.0000 -0.0086
## 7 0.9488 nan 1.0000 -0.0117
## 8 0.9429 nan 1.0000 -0.0047
## 9 0.9253 nan 1.0000 -0.0034
## 10 0.9394 nan 1.0000 -0.0217
## 20 0.9342 nan 1.0000 -0.0318
## 40 0.8437 nan 1.0000 -0.0063
## 60 0.8421 nan 1.0000 -0.0170
## 80 0.8354 nan 1.0000 -0.0282
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1181 nan 1.0000 0.0592
## 2 1.0403 nan 1.0000 0.0262
## 3 1.0009 nan 1.0000 -0.0306
## 4 0.9831 nan 1.0000 -0.0038
## 5 0.9793 nan 1.0000 -0.0248
## 6 0.9637 nan 1.0000 -0.0228
## 7 0.9235 nan 1.0000 0.0037
## 8 0.9070 nan 1.0000 0.0063
## 9 0.9229 nan 1.0000 -0.0357
## 10 0.9401 nan 1.0000 -0.0307
## 20 0.8856 nan 1.0000 -0.0134
## 40 0.9057 nan 1.0000 -0.0086
## 60 3.5014 nan 1.0000 -0.0015
## 80 3.4953 nan 1.0000 -0.0112
## 100 3.5007 nan 1.0000 -0.0036
## 120 3.4778 nan 1.0000 0.0000
## 140 3.4688 nan 1.0000 -0.0016
## 160 3.4740 nan 1.0000 -0.0045
## 180 3.4651 nan 1.0000 -0.0000
## 200 3.4570 nan 1.0000 -0.0008
## 220 3.4557 nan 1.0000 -0.0019
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0799 nan 1.0000 0.0775
## 2 0.9895 nan 1.0000 0.0383
## 3 0.9361 nan 1.0000 0.0127
## 4 0.9197 nan 1.0000 -0.0206
## 5 0.9065 nan 1.0000 -0.0283
## 6 0.8929 nan 1.0000 -0.0137
## 7 0.9096 nan 1.0000 -0.0428
## 8 0.9614 nan 1.0000 -0.0780
## 9 0.9407 nan 1.0000 -0.0337
## 10 0.9539 nan 1.0000 -0.0395
## 20 4.6669 nan 1.0000 -0.0207
## 40 5.4855 nan 1.0000 -0.0116
## 60 12.2630 nan 1.0000 -3.2427
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0430 nan 1.0000 0.1295
## 2 0.9864 nan 1.0000 0.0111
## 3 0.9641 nan 1.0000 -0.0370
## 4 0.9633 nan 1.0000 -0.0319
## 5 1.0178 nan 1.0000 -0.0800
## 6 0.9808 nan 1.0000 -0.0083
## 7 0.9601 nan 1.0000 -0.0237
## 8 0.9719 nan 1.0000 -0.0508
## 9 0.9539 nan 1.0000 -0.0278
## 10 0.9553 nan 1.0000 -0.0237
## 20 1.9161 nan 1.0000 -0.0418
## 40 2.2259 nan 1.0000 -0.0343
## 60 1255.6231 nan 1.0000 -0.0530
## 80 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0508 nan 1.0000 0.0957
## 2 0.9677 nan 1.0000 0.0354
## 3 0.9898 nan 1.0000 -0.0567
## 4 0.9887 nan 1.0000 -0.0401
## 5 0.9769 nan 1.0000 -0.0181
## 6 0.9718 nan 1.0000 -0.0234
## 7 1.8630 nan 1.0000 -0.4881
## 8 1.8485 nan 1.0000 -0.0045
## 9 1.8691 nan 1.0000 -0.0628
## 10 1.8677 nan 1.0000 -0.0500
## 20 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0189 nan 1.0000 0.1245
## 2 0.9796 nan 1.0000 -0.0235
## 3 0.9873 nan 1.0000 -0.0587
## 4 0.9764 nan 1.0000 -0.0579
## 5 2.0290 nan 1.0000 -1.1181
## 6 2.0455 nan 1.0000 -0.0609
## 7 2.0602 nan 1.0000 -0.0637
## 8 2.0396 nan 1.0000 -0.0319
## 9 2.0241 nan 1.0000 -0.0017
## 10 1.9838 nan 1.0000 -0.0189
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0523 nan 1.0000 0.0841
## 2 0.9418 nan 1.0000 0.0341
## 3 0.9254 nan 1.0000 -0.0332
## 4 0.9628 nan 1.0000 -0.0880
## 5 0.9401 nan 1.0000 -0.0114
## 6 0.9196 nan 1.0000 -0.0155
## 7 0.9006 nan 1.0000 -0.0063
## 8 0.8983 nan 1.0000 -0.0492
## 9 0.9317 nan 1.0000 -0.0787
## 10 0.9098 nan 1.0000 -0.0318
## 20 1.0648 nan 1.0000 -0.0513
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9982 nan 1.0000 0.1173
## 2 0.9378 nan 1.0000 0.0118
## 3 0.9120 nan 1.0000 -0.0129
## 4 0.9080 nan 1.0000 -0.0406
## 5 0.9083 nan 1.0000 -0.0420
## 6 0.8711 nan 1.0000 -0.0169
## 7 0.8772 nan 1.0000 -0.0569
## 8 0.8534 nan 1.0000 -0.0133
## 9 0.8425 nan 1.0000 -0.0462
## 10 0.8741 nan 1.0000 -0.0688
## 20 0.9207 nan 1.0000 -0.0179
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 40 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2921 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2910 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2903 nan 0.0010 0.0002
## 9 1.2899 nan 0.0010 0.0002
## 10 1.2895 nan 0.0010 0.0002
## 20 1.2858 nan 0.0010 0.0002
## 40 1.2784 nan 0.0010 0.0002
## 60 1.2714 nan 0.0010 0.0002
## 80 1.2646 nan 0.0010 0.0001
## 100 1.2582 nan 0.0010 0.0001
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## 140 1.2458 nan 0.0010 0.0001
## 160 1.2400 nan 0.0010 0.0001
## 180 1.2346 nan 0.0010 0.0001
## 200 1.2294 nan 0.0010 0.0001
## 220 1.2240 nan 0.0010 0.0001
## 240 1.2188 nan 0.0010 0.0001
## 260 1.2140 nan 0.0010 0.0001
## 280 1.2092 nan 0.0010 0.0001
## 300 1.2045 nan 0.0010 0.0001
## 320 1.1997 nan 0.0010 0.0001
## 340 1.1954 nan 0.0010 0.0001
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## 380 1.1869 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2914 nan 0.0010 0.0002
## 6 1.2910 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0001
## 8 1.2903 nan 0.0010 0.0002
## 9 1.2899 nan 0.0010 0.0002
## 10 1.2895 nan 0.0010 0.0002
## 20 1.2857 nan 0.0010 0.0002
## 40 1.2784 nan 0.0010 0.0002
## 60 1.2715 nan 0.0010 0.0002
## 80 1.2647 nan 0.0010 0.0001
## 100 1.2583 nan 0.0010 0.0001
## 120 1.2518 nan 0.0010 0.0001
## 140 1.2459 nan 0.0010 0.0001
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## 480 1.1665 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2921 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2914 nan 0.0010 0.0002
## 6 1.2910 nan 0.0010 0.0002
## 7 1.2906 nan 0.0010 0.0002
## 8 1.2903 nan 0.0010 0.0002
## 9 1.2899 nan 0.0010 0.0002
## 10 1.2895 nan 0.0010 0.0001
## 20 1.2858 nan 0.0010 0.0002
## 40 1.2787 nan 0.0010 0.0002
## 60 1.2718 nan 0.0010 0.0002
## 80 1.2650 nan 0.0010 0.0002
## 100 1.2584 nan 0.0010 0.0001
## 120 1.2520 nan 0.0010 0.0001
## 140 1.2460 nan 0.0010 0.0002
## 160 1.2401 nan 0.0010 0.0001
## 180 1.2345 nan 0.0010 0.0001
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## 220 1.2236 nan 0.0010 0.0001
## 240 1.2183 nan 0.0010 0.0001
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## 380 1.1867 nan 0.0010 0.0001
## 400 1.1826 nan 0.0010 0.0001
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## 480 1.1669 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2903 nan 0.0010 0.0002
## 7 1.2898 nan 0.0010 0.0002
## 8 1.2893 nan 0.0010 0.0002
## 9 1.2888 nan 0.0010 0.0002
## 10 1.2884 nan 0.0010 0.0002
## 20 1.2836 nan 0.0010 0.0002
## 40 1.2745 nan 0.0010 0.0002
## 60 1.2655 nan 0.0010 0.0002
## 80 1.2567 nan 0.0010 0.0002
## 100 1.2484 nan 0.0010 0.0002
## 120 1.2402 nan 0.0010 0.0002
## 140 1.2322 nan 0.0010 0.0002
## 160 1.2244 nan 0.0010 0.0002
## 180 1.2170 nan 0.0010 0.0002
## 200 1.2095 nan 0.0010 0.0001
## 220 1.2026 nan 0.0010 0.0001
## 240 1.1957 nan 0.0010 0.0001
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## 280 1.1824 nan 0.0010 0.0001
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## 360 1.1580 nan 0.0010 0.0001
## 380 1.1524 nan 0.0010 0.0001
## 400 1.1468 nan 0.0010 0.0001
## 420 1.1414 nan 0.0010 0.0001
## 440 1.1363 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2899 nan 0.0010 0.0002
## 8 1.2894 nan 0.0010 0.0002
## 9 1.2889 nan 0.0010 0.0002
## 10 1.2884 nan 0.0010 0.0002
## 20 1.2834 nan 0.0010 0.0002
## 40 1.2743 nan 0.0010 0.0002
## 60 1.2653 nan 0.0010 0.0002
## 80 1.2566 nan 0.0010 0.0002
## 100 1.2481 nan 0.0010 0.0002
## 120 1.2398 nan 0.0010 0.0002
## 140 1.2319 nan 0.0010 0.0002
## 160 1.2244 nan 0.0010 0.0002
## 180 1.2171 nan 0.0010 0.0002
## 200 1.2095 nan 0.0010 0.0002
## 220 1.2025 nan 0.0010 0.0001
## 240 1.1957 nan 0.0010 0.0002
## 260 1.1892 nan 0.0010 0.0001
## 280 1.1827 nan 0.0010 0.0001
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## 360 1.1584 nan 0.0010 0.0001
## 380 1.1528 nan 0.0010 0.0001
## 400 1.1473 nan 0.0010 0.0001
## 420 1.1418 nan 0.0010 0.0001
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## 480 1.1264 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2895 nan 0.0010 0.0002
## 9 1.2890 nan 0.0010 0.0002
## 10 1.2885 nan 0.0010 0.0002
## 20 1.2837 nan 0.0010 0.0002
## 40 1.2744 nan 0.0010 0.0002
## 60 1.2653 nan 0.0010 0.0002
## 80 1.2566 nan 0.0010 0.0002
## 100 1.2480 nan 0.0010 0.0002
## 120 1.2397 nan 0.0010 0.0002
## 140 1.2319 nan 0.0010 0.0002
## 160 1.2241 nan 0.0010 0.0002
## 180 1.2167 nan 0.0010 0.0002
## 200 1.2094 nan 0.0010 0.0002
## 220 1.2023 nan 0.0010 0.0001
## 240 1.1955 nan 0.0010 0.0001
## 260 1.1887 nan 0.0010 0.0002
## 280 1.1824 nan 0.0010 0.0002
## 300 1.1759 nan 0.0010 0.0001
## 320 1.1700 nan 0.0010 0.0001
## 340 1.1641 nan 0.0010 0.0001
## 360 1.1582 nan 0.0010 0.0001
## 380 1.1526 nan 0.0010 0.0001
## 400 1.1472 nan 0.0010 0.0001
## 420 1.1420 nan 0.0010 0.0001
## 440 1.1367 nan 0.0010 0.0001
## 460 1.1317 nan 0.0010 0.0001
## 480 1.1268 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2923 nan 0.0010 0.0003
## 3 1.2917 nan 0.0010 0.0003
## 4 1.2911 nan 0.0010 0.0003
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0003
## 7 1.2896 nan 0.0010 0.0002
## 8 1.2890 nan 0.0010 0.0003
## 9 1.2885 nan 0.0010 0.0002
## 10 1.2880 nan 0.0010 0.0002
## 20 1.2827 nan 0.0010 0.0002
## 40 1.2721 nan 0.0010 0.0002
## 60 1.2617 nan 0.0010 0.0002
## 80 1.2518 nan 0.0010 0.0002
## 100 1.2421 nan 0.0010 0.0002
## 120 1.2328 nan 0.0010 0.0002
## 140 1.2238 nan 0.0010 0.0002
## 160 1.2151 nan 0.0010 0.0002
## 180 1.2067 nan 0.0010 0.0002
## 200 1.1983 nan 0.0010 0.0002
## 220 1.1900 nan 0.0010 0.0002
## 240 1.1824 nan 0.0010 0.0002
## 260 1.1748 nan 0.0010 0.0002
## 280 1.1675 nan 0.0010 0.0001
## 300 1.1602 nan 0.0010 0.0001
## 320 1.1529 nan 0.0010 0.0002
## 340 1.1461 nan 0.0010 0.0001
## 360 1.1392 nan 0.0010 0.0001
## 380 1.1330 nan 0.0010 0.0001
## 400 1.1267 nan 0.0010 0.0002
## 420 1.1204 nan 0.0010 0.0001
## 440 1.1145 nan 0.0010 0.0001
## 460 1.1086 nan 0.0010 0.0001
## 480 1.1031 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0002
## 5 1.2905 nan 0.0010 0.0003
## 6 1.2899 nan 0.0010 0.0003
## 7 1.2893 nan 0.0010 0.0003
## 8 1.2888 nan 0.0010 0.0002
## 9 1.2883 nan 0.0010 0.0002
## 10 1.2877 nan 0.0010 0.0002
## 20 1.2820 nan 0.0010 0.0003
## 40 1.2710 nan 0.0010 0.0002
## 60 1.2607 nan 0.0010 0.0002
## 80 1.2508 nan 0.0010 0.0002
## 100 1.2413 nan 0.0010 0.0002
## 120 1.2318 nan 0.0010 0.0002
## 140 1.2228 nan 0.0010 0.0002
## 160 1.2140 nan 0.0010 0.0002
## 180 1.2053 nan 0.0010 0.0001
## 200 1.1968 nan 0.0010 0.0002
## 220 1.1888 nan 0.0010 0.0002
## 240 1.1809 nan 0.0010 0.0002
## 260 1.1733 nan 0.0010 0.0002
## 280 1.1657 nan 0.0010 0.0002
## 300 1.1587 nan 0.0010 0.0001
## 320 1.1518 nan 0.0010 0.0001
## 340 1.1452 nan 0.0010 0.0001
## 360 1.1386 nan 0.0010 0.0001
## 380 1.1322 nan 0.0010 0.0001
## 400 1.1260 nan 0.0010 0.0001
## 420 1.1198 nan 0.0010 0.0001
## 440 1.1138 nan 0.0010 0.0001
## 460 1.1083 nan 0.0010 0.0001
## 480 1.1027 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0003
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0003
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2900 nan 0.0010 0.0003
## 7 1.2895 nan 0.0010 0.0002
## 8 1.2889 nan 0.0010 0.0002
## 9 1.2884 nan 0.0010 0.0002
## 10 1.2878 nan 0.0010 0.0003
## 20 1.2824 nan 0.0010 0.0002
## 40 1.2714 nan 0.0010 0.0002
## 60 1.2612 nan 0.0010 0.0002
## 80 1.2513 nan 0.0010 0.0002
## 100 1.2417 nan 0.0010 0.0002
## 120 1.2324 nan 0.0010 0.0002
## 140 1.2232 nan 0.0010 0.0002
## 160 1.2144 nan 0.0010 0.0002
## 180 1.2059 nan 0.0010 0.0002
## 200 1.1975 nan 0.0010 0.0002
## 220 1.1895 nan 0.0010 0.0002
## 240 1.1816 nan 0.0010 0.0002
## 260 1.1737 nan 0.0010 0.0001
## 280 1.1662 nan 0.0010 0.0001
## 300 1.1591 nan 0.0010 0.0001
## 320 1.1523 nan 0.0010 0.0001
## 340 1.1455 nan 0.0010 0.0001
## 360 1.1392 nan 0.0010 0.0002
## 380 1.1326 nan 0.0010 0.0001
## 400 1.1264 nan 0.0010 0.0001
## 420 1.1203 nan 0.0010 0.0001
## 440 1.1144 nan 0.0010 0.0001
## 460 1.1085 nan 0.0010 0.0001
## 480 1.1027 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2577 nan 0.1000 0.0159
## 2 1.2290 nan 0.1000 0.0136
## 3 1.1998 nan 0.1000 0.0110
## 4 1.1799 nan 0.1000 0.0085
## 5 1.1625 nan 0.1000 0.0079
## 6 1.1460 nan 0.1000 0.0074
## 7 1.1297 nan 0.1000 0.0077
## 8 1.1143 nan 0.1000 0.0067
## 9 1.1026 nan 0.1000 0.0055
## 10 1.0907 nan 0.1000 0.0044
## 20 1.0058 nan 0.1000 0.0011
## 40 0.9209 nan 0.1000 0.0000
## 60 0.8771 nan 0.1000 -0.0002
## 80 0.8507 nan 0.1000 -0.0009
## 100 0.8315 nan 0.1000 -0.0005
## 120 0.8187 nan 0.1000 -0.0005
## 140 0.8048 nan 0.1000 -0.0003
## 160 0.7952 nan 0.1000 -0.0021
## 180 0.7886 nan 0.1000 -0.0006
## 200 0.7815 nan 0.1000 -0.0007
## 220 0.7743 nan 0.1000 -0.0016
## 240 0.7673 nan 0.1000 -0.0012
## 260 0.7598 nan 0.1000 -0.0011
## 280 0.7525 nan 0.1000 -0.0005
## 300 0.7454 nan 0.1000 -0.0009
## 320 0.7399 nan 0.1000 -0.0008
## 340 0.7331 nan 0.1000 -0.0009
## 360 0.7264 nan 0.1000 -0.0005
## 380 0.7228 nan 0.1000 -0.0010
## 400 0.7179 nan 0.1000 -0.0005
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## 440 0.7069 nan 0.1000 -0.0004
## 460 0.7019 nan 0.1000 -0.0012
## 480 0.6970 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2579 nan 0.1000 0.0164
## 2 1.2245 nan 0.1000 0.0121
## 3 1.2008 nan 0.1000 0.0099
## 4 1.1756 nan 0.1000 0.0100
## 5 1.1581 nan 0.1000 0.0067
## 6 1.1411 nan 0.1000 0.0052
## 7 1.1253 nan 0.1000 0.0048
## 8 1.1102 nan 0.1000 0.0057
## 9 1.0965 nan 0.1000 0.0046
## 10 1.0828 nan 0.1000 0.0062
## 20 0.9959 nan 0.1000 0.0011
## 40 0.9196 nan 0.1000 -0.0007
## 60 0.8761 nan 0.1000 -0.0002
## 80 0.8481 nan 0.1000 -0.0004
## 100 0.8288 nan 0.1000 -0.0012
## 120 0.8145 nan 0.1000 -0.0014
## 140 0.8025 nan 0.1000 -0.0009
## 160 0.7926 nan 0.1000 -0.0005
## 180 0.7862 nan 0.1000 -0.0007
## 200 0.7783 nan 0.1000 -0.0011
## 220 0.7690 nan 0.1000 -0.0003
## 240 0.7620 nan 0.1000 -0.0008
## 260 0.7563 nan 0.1000 -0.0015
## 280 0.7489 nan 0.1000 -0.0003
## 300 0.7440 nan 0.1000 -0.0008
## 320 0.7390 nan 0.1000 -0.0010
## 340 0.7338 nan 0.1000 -0.0017
## 360 0.7271 nan 0.1000 -0.0010
## 380 0.7238 nan 0.1000 -0.0005
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## 460 0.7065 nan 0.1000 -0.0005
## 480 0.7033 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2599 nan 0.1000 0.0148
## 2 1.2273 nan 0.1000 0.0163
## 3 1.2031 nan 0.1000 0.0113
## 4 1.1823 nan 0.1000 0.0110
## 5 1.1626 nan 0.1000 0.0085
## 6 1.1444 nan 0.1000 0.0079
## 7 1.1284 nan 0.1000 0.0061
## 8 1.1137 nan 0.1000 0.0062
## 9 1.0989 nan 0.1000 0.0067
## 10 1.0870 nan 0.1000 0.0046
## 20 0.9948 nan 0.1000 0.0015
## 40 0.9211 nan 0.1000 0.0014
## 60 0.8805 nan 0.1000 -0.0006
## 80 0.8539 nan 0.1000 -0.0005
## 100 0.8360 nan 0.1000 -0.0007
## 120 0.8246 nan 0.1000 -0.0009
## 140 0.8148 nan 0.1000 -0.0023
## 160 0.8024 nan 0.1000 -0.0004
## 180 0.7928 nan 0.1000 -0.0007
## 200 0.7849 nan 0.1000 -0.0002
## 220 0.7754 nan 0.1000 0.0000
## 240 0.7685 nan 0.1000 -0.0011
## 260 0.7615 nan 0.1000 -0.0013
## 280 0.7554 nan 0.1000 -0.0010
## 300 0.7503 nan 0.1000 -0.0017
## 320 0.7449 nan 0.1000 -0.0010
## 340 0.7386 nan 0.1000 -0.0007
## 360 0.7306 nan 0.1000 -0.0010
## 380 0.7256 nan 0.1000 -0.0018
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## 460 0.7088 nan 0.1000 -0.0003
## 480 0.7049 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2457 nan 0.1000 0.0204
## 2 1.2046 nan 0.1000 0.0153
## 3 1.1690 nan 0.1000 0.0147
## 4 1.1418 nan 0.1000 0.0116
## 5 1.1200 nan 0.1000 0.0094
## 6 1.1000 nan 0.1000 0.0087
## 7 1.0834 nan 0.1000 0.0047
## 8 1.0643 nan 0.1000 0.0063
## 9 1.0473 nan 0.1000 0.0066
## 10 1.0285 nan 0.1000 0.0078
## 20 0.9387 nan 0.1000 0.0000
## 40 0.8479 nan 0.1000 -0.0008
## 60 0.8046 nan 0.1000 -0.0008
## 80 0.7664 nan 0.1000 -0.0014
## 100 0.7401 nan 0.1000 -0.0007
## 120 0.7155 nan 0.1000 -0.0026
## 140 0.6955 nan 0.1000 -0.0007
## 160 0.6735 nan 0.1000 -0.0015
## 180 0.6542 nan 0.1000 -0.0011
## 200 0.6340 nan 0.1000 -0.0011
## 220 0.6135 nan 0.1000 -0.0002
## 240 0.5964 nan 0.1000 -0.0014
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## 280 0.5653 nan 0.1000 -0.0015
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## 320 0.5390 nan 0.1000 -0.0007
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## 380 0.4994 nan 0.1000 -0.0005
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## 440 0.4661 nan 0.1000 -0.0010
## 460 0.4543 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2449 nan 0.1000 0.0226
## 2 1.2047 nan 0.1000 0.0167
## 3 1.1706 nan 0.1000 0.0160
## 4 1.1417 nan 0.1000 0.0130
## 5 1.1193 nan 0.1000 0.0066
## 6 1.0988 nan 0.1000 0.0067
## 7 1.0763 nan 0.1000 0.0082
## 8 1.0598 nan 0.1000 0.0067
## 9 1.0421 nan 0.1000 0.0086
## 10 1.0280 nan 0.1000 0.0038
## 20 0.9340 nan 0.1000 0.0024
## 40 0.8464 nan 0.1000 -0.0001
## 60 0.7960 nan 0.1000 -0.0033
## 80 0.7628 nan 0.1000 -0.0014
## 100 0.7336 nan 0.1000 -0.0018
## 120 0.7056 nan 0.1000 -0.0003
## 140 0.6830 nan 0.1000 -0.0027
## 160 0.6628 nan 0.1000 -0.0001
## 180 0.6437 nan 0.1000 -0.0002
## 200 0.6241 nan 0.1000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2466 nan 0.1000 0.0201
## 2 1.2135 nan 0.1000 0.0164
## 3 1.1821 nan 0.1000 0.0155
## 4 1.1481 nan 0.1000 0.0151
## 5 1.1246 nan 0.1000 0.0114
## 6 1.1023 nan 0.1000 0.0079
## 7 1.0835 nan 0.1000 0.0087
## 8 1.0656 nan 0.1000 0.0075
## 9 1.0444 nan 0.1000 0.0069
## 10 1.0288 nan 0.1000 0.0057
## 20 0.9260 nan 0.1000 0.0013
## 40 0.8423 nan 0.1000 -0.0012
## 60 0.7983 nan 0.1000 -0.0014
## 80 0.7594 nan 0.1000 -0.0004
## 100 0.7323 nan 0.1000 -0.0013
## 120 0.7080 nan 0.1000 -0.0004
## 140 0.6841 nan 0.1000 -0.0015
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## 180 0.6512 nan 0.1000 -0.0009
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## 320 0.5426 nan 0.1000 -0.0009
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## 380 0.5037 nan 0.1000 -0.0005
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## 460 0.4627 nan 0.1000 -0.0014
## 480 0.4532 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2374 nan 0.1000 0.0257
## 2 1.1950 nan 0.1000 0.0188
## 3 1.1509 nan 0.1000 0.0197
## 4 1.1240 nan 0.1000 0.0111
## 5 1.0936 nan 0.1000 0.0081
## 6 1.0696 nan 0.1000 0.0098
## 7 1.0456 nan 0.1000 0.0103
## 8 1.0252 nan 0.1000 0.0071
## 9 1.0029 nan 0.1000 0.0082
## 10 0.9871 nan 0.1000 0.0046
## 20 0.8820 nan 0.1000 0.0009
## 40 0.7909 nan 0.1000 0.0005
## 60 0.7294 nan 0.1000 -0.0020
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## 360 0.3522 nan 0.1000 -0.0005
## 380 0.3389 nan 0.1000 -0.0007
## 400 0.3253 nan 0.1000 -0.0003
## 420 0.3119 nan 0.1000 -0.0004
## 440 0.2997 nan 0.1000 -0.0005
## 460 0.2876 nan 0.1000 -0.0008
## 480 0.2774 nan 0.1000 -0.0003
## 500 0.2681 nan 0.1000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2439 nan 0.1000 0.0246
## 2 1.1984 nan 0.1000 0.0221
## 3 1.1573 nan 0.1000 0.0152
## 4 1.1247 nan 0.1000 0.0131
## 5 1.0904 nan 0.1000 0.0151
## 6 1.0648 nan 0.1000 0.0095
## 7 1.0441 nan 0.1000 0.0066
## 8 1.0262 nan 0.1000 0.0058
## 9 1.0088 nan 0.1000 0.0066
## 10 0.9950 nan 0.1000 0.0027
## 20 0.8897 nan 0.1000 -0.0007
## 40 0.7978 nan 0.1000 -0.0022
## 60 0.7389 nan 0.1000 -0.0025
## 80 0.6938 nan 0.1000 -0.0014
## 100 0.6560 nan 0.1000 -0.0012
## 120 0.6185 nan 0.1000 -0.0023
## 140 0.5923 nan 0.1000 -0.0013
## 160 0.5675 nan 0.1000 -0.0020
## 180 0.5425 nan 0.1000 -0.0013
## 200 0.5169 nan 0.1000 -0.0023
## 220 0.4953 nan 0.1000 -0.0012
## 240 0.4724 nan 0.1000 -0.0007
## 260 0.4532 nan 0.1000 -0.0005
## 280 0.4304 nan 0.1000 -0.0003
## 300 0.4088 nan 0.1000 -0.0006
## 320 0.3897 nan 0.1000 -0.0018
## 340 0.3740 nan 0.1000 -0.0008
## 360 0.3569 nan 0.1000 -0.0001
## 380 0.3438 nan 0.1000 -0.0006
## 400 0.3298 nan 0.1000 -0.0005
## 420 0.3176 nan 0.1000 -0.0005
## 440 0.3076 nan 0.1000 -0.0002
## 460 0.2948 nan 0.1000 -0.0002
## 480 0.2833 nan 0.1000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2411 nan 0.1000 0.0243
## 2 1.2011 nan 0.1000 0.0159
## 3 1.1598 nan 0.1000 0.0185
## 4 1.1309 nan 0.1000 0.0136
## 5 1.1032 nan 0.1000 0.0101
## 6 1.0810 nan 0.1000 0.0077
## 7 1.0551 nan 0.1000 0.0108
## 8 1.0349 nan 0.1000 0.0084
## 9 1.0120 nan 0.1000 0.0053
## 10 0.9975 nan 0.1000 0.0037
## 20 0.8942 nan 0.1000 0.0001
## 40 0.7990 nan 0.1000 -0.0012
## 60 0.7461 nan 0.1000 -0.0007
## 80 0.6957 nan 0.1000 -0.0009
## 100 0.6558 nan 0.1000 -0.0022
## 120 0.6224 nan 0.1000 -0.0014
## 140 0.5926 nan 0.1000 -0.0009
## 160 0.5698 nan 0.1000 -0.0018
## 180 0.5431 nan 0.1000 -0.0016
## 200 0.5210 nan 0.1000 -0.0011
## 220 0.4995 nan 0.1000 -0.0007
## 240 0.4770 nan 0.1000 -0.0012
## 260 0.4562 nan 0.1000 -0.0023
## 280 0.4374 nan 0.1000 -0.0009
## 300 0.4212 nan 0.1000 -0.0018
## 320 0.4023 nan 0.1000 -0.0009
## 340 0.3858 nan 0.1000 -0.0016
## 360 0.3683 nan 0.1000 -0.0009
## 380 0.3579 nan 0.1000 -0.0010
## 400 0.3418 nan 0.1000 -0.0008
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## 440 0.3183 nan 0.1000 -0.0011
## 460 0.3064 nan 0.1000 -0.0006
## 480 0.2920 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2239 nan 0.2000 0.0319
## 2 1.1787 nan 0.2000 0.0230
## 3 1.1484 nan 0.2000 0.0126
## 4 1.1129 nan 0.2000 0.0105
## 5 1.0857 nan 0.2000 0.0123
## 6 1.0632 nan 0.2000 0.0071
## 7 1.0379 nan 0.2000 0.0112
## 8 1.0277 nan 0.2000 0.0003
## 9 1.0194 nan 0.2000 0.0005
## 10 1.0054 nan 0.2000 0.0017
## 20 0.9179 nan 0.2000 -0.0022
## 40 0.8535 nan 0.2000 -0.0015
## 60 0.8200 nan 0.2000 -0.0025
## 80 0.8000 nan 0.2000 -0.0014
## 100 0.7872 nan 0.2000 -0.0046
## 120 0.7703 nan 0.2000 -0.0000
## 140 0.7587 nan 0.2000 -0.0027
## 160 0.7431 nan 0.2000 -0.0010
## 180 0.7325 nan 0.2000 -0.0006
## 200 0.7256 nan 0.2000 -0.0027
## 220 0.7136 nan 0.2000 -0.0034
## 240 0.7028 nan 0.2000 -0.0011
## 260 0.6936 nan 0.2000 -0.0013
## 280 0.6882 nan 0.2000 -0.0035
## 300 0.6806 nan 0.2000 -0.0021
## 320 0.6770 nan 0.2000 -0.0011
## 340 0.6660 nan 0.2000 -0.0012
## 360 0.6569 nan 0.2000 -0.0020
## 380 0.6510 nan 0.2000 -0.0019
## 400 0.6459 nan 0.2000 -0.0004
## 420 0.6392 nan 0.2000 -0.0030
## 440 0.6347 nan 0.2000 -0.0011
## 460 0.6283 nan 0.2000 -0.0004
## 480 0.6245 nan 0.2000 -0.0022
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2283 nan 0.2000 0.0308
## 2 1.1914 nan 0.2000 0.0156
## 3 1.1455 nan 0.2000 0.0169
## 4 1.1104 nan 0.2000 0.0145
## 5 1.0788 nan 0.2000 0.0118
## 6 1.0586 nan 0.2000 0.0076
## 7 1.0480 nan 0.2000 -0.0030
## 8 1.0271 nan 0.2000 0.0065
## 9 1.0103 nan 0.2000 0.0052
## 10 0.9926 nan 0.2000 0.0067
## 20 0.9154 nan 0.2000 -0.0017
## 40 0.8499 nan 0.2000 -0.0006
## 60 0.8266 nan 0.2000 -0.0024
## 80 0.8074 nan 0.2000 -0.0026
## 100 0.7832 nan 0.2000 -0.0022
## 120 0.7736 nan 0.2000 -0.0028
## 140 0.7606 nan 0.2000 -0.0009
## 160 0.7465 nan 0.2000 -0.0021
## 180 0.7351 nan 0.2000 -0.0037
## 200 0.7241 nan 0.2000 -0.0017
## 220 0.7193 nan 0.2000 -0.0024
## 240 0.7102 nan 0.2000 -0.0027
## 260 0.6988 nan 0.2000 -0.0014
## 280 0.6910 nan 0.2000 -0.0011
## 300 0.6842 nan 0.2000 -0.0027
## 320 0.6755 nan 0.2000 -0.0020
## 340 0.6701 nan 0.2000 -0.0029
## 360 0.6649 nan 0.2000 -0.0017
## 380 0.6595 nan 0.2000 -0.0033
## 400 0.6544 nan 0.2000 -0.0020
## 420 0.6469 nan 0.2000 -0.0011
## 440 0.6400 nan 0.2000 -0.0017
## 460 0.6350 nan 0.2000 -0.0020
## 480 0.6288 nan 0.2000 -0.0023
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2235 nan 0.2000 0.0285
## 2 1.1772 nan 0.2000 0.0209
## 3 1.1403 nan 0.2000 0.0109
## 4 1.1030 nan 0.2000 0.0156
## 5 1.0832 nan 0.2000 0.0077
## 6 1.0636 nan 0.2000 0.0066
## 7 1.0456 nan 0.2000 0.0074
## 8 1.0236 nan 0.2000 0.0086
## 9 1.0133 nan 0.2000 0.0011
## 10 0.9991 nan 0.2000 0.0041
## 20 0.9040 nan 0.2000 0.0012
## 40 0.8472 nan 0.2000 -0.0008
## 60 0.8205 nan 0.2000 -0.0038
## 80 0.8066 nan 0.2000 -0.0006
## 100 0.7856 nan 0.2000 -0.0018
## 120 0.7713 nan 0.2000 -0.0012
## 140 0.7617 nan 0.2000 -0.0034
## 160 0.7464 nan 0.2000 -0.0007
## 180 0.7388 nan 0.2000 -0.0022
## 200 0.7283 nan 0.2000 -0.0025
## 220 0.7201 nan 0.2000 -0.0023
## 240 0.7110 nan 0.2000 -0.0021
## 260 0.7027 nan 0.2000 -0.0001
## 280 0.6897 nan 0.2000 -0.0018
## 300 0.6814 nan 0.2000 -0.0015
## 320 0.6710 nan 0.2000 -0.0012
## 340 0.6635 nan 0.2000 -0.0012
## 360 0.6571 nan 0.2000 -0.0023
## 380 0.6539 nan 0.2000 -0.0020
## 400 0.6475 nan 0.2000 -0.0018
## 420 0.6431 nan 0.2000 -0.0018
## 440 0.6363 nan 0.2000 -0.0017
## 460 0.6336 nan 0.2000 -0.0002
## 480 0.6273 nan 0.2000 -0.0025
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2043 nan 0.2000 0.0410
## 2 1.1363 nan 0.2000 0.0299
## 3 1.0978 nan 0.2000 0.0124
## 4 1.0496 nan 0.2000 0.0164
## 5 1.0210 nan 0.2000 0.0122
## 6 0.9911 nan 0.2000 0.0060
## 7 0.9748 nan 0.2000 0.0027
## 8 0.9555 nan 0.2000 0.0066
## 9 0.9399 nan 0.2000 0.0039
## 10 0.9273 nan 0.2000 0.0014
## 20 0.8599 nan 0.2000 -0.0002
## 40 0.7827 nan 0.2000 -0.0055
## 60 0.7387 nan 0.2000 0.0012
## 80 0.6927 nan 0.2000 -0.0008
## 100 0.6539 nan 0.2000 -0.0022
## 120 0.6232 nan 0.2000 -0.0030
## 140 0.5938 nan 0.2000 -0.0035
## 160 0.5474 nan 0.2000 -0.0027
## 180 0.5181 nan 0.2000 -0.0042
## 200 0.4997 nan 0.2000 -0.0031
## 220 0.4763 nan 0.2000 -0.0004
## 240 0.4477 nan 0.2000 -0.0036
## 260 0.4274 nan 0.2000 -0.0024
## 280 0.4045 nan 0.2000 -0.0015
## 300 0.3854 nan 0.2000 -0.0008
## 320 0.3671 nan 0.2000 -0.0009
## 340 0.3518 nan 0.2000 -0.0004
## 360 0.3414 nan 0.2000 -0.0010
## 380 0.3318 nan 0.2000 -0.0008
## 400 0.3226 nan 0.2000 -0.0014
## 420 0.3091 nan 0.2000 -0.0028
## 440 0.2953 nan 0.2000 -0.0010
## 460 0.2844 nan 0.2000 -0.0013
## 480 0.2716 nan 0.2000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2052 nan 0.2000 0.0312
## 2 1.1387 nan 0.2000 0.0321
## 3 1.0967 nan 0.2000 0.0166
## 4 1.0627 nan 0.2000 0.0115
## 5 1.0379 nan 0.2000 0.0070
## 6 1.0110 nan 0.2000 0.0055
## 7 0.9849 nan 0.2000 0.0094
## 8 0.9743 nan 0.2000 -0.0013
## 9 0.9573 nan 0.2000 0.0032
## 10 0.9413 nan 0.2000 0.0019
## 20 0.8553 nan 0.2000 -0.0023
## 40 0.7765 nan 0.2000 -0.0055
## 60 0.7192 nan 0.2000 -0.0011
## 80 0.6567 nan 0.2000 -0.0017
## 100 0.6213 nan 0.2000 -0.0027
## 120 0.5908 nan 0.2000 -0.0038
## 140 0.5672 nan 0.2000 -0.0033
## 160 0.5317 nan 0.2000 -0.0030
## 180 0.5114 nan 0.2000 -0.0025
## 200 0.4900 nan 0.2000 -0.0026
## 220 0.4704 nan 0.2000 -0.0012
## 240 0.4499 nan 0.2000 -0.0027
## 260 0.4257 nan 0.2000 -0.0009
## 280 0.4086 nan 0.2000 -0.0013
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## 320 0.3702 nan 0.2000 -0.0006
## 340 0.3554 nan 0.2000 -0.0032
## 360 0.3395 nan 0.2000 -0.0023
## 380 0.3262 nan 0.2000 -0.0026
## 400 0.3112 nan 0.2000 -0.0011
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## 460 0.2737 nan 0.2000 -0.0012
## 480 0.2646 nan 0.2000 -0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1924 nan 0.2000 0.0403
## 2 1.1270 nan 0.2000 0.0303
## 3 1.0839 nan 0.2000 0.0143
## 4 1.0488 nan 0.2000 0.0149
## 5 1.0175 nan 0.2000 0.0148
## 6 0.9966 nan 0.2000 0.0071
## 7 0.9766 nan 0.2000 0.0068
## 8 0.9524 nan 0.2000 0.0087
## 9 0.9396 nan 0.2000 -0.0002
## 10 0.9213 nan 0.2000 0.0036
## 20 0.8498 nan 0.2000 -0.0008
## 40 0.7668 nan 0.2000 -0.0027
## 60 0.7244 nan 0.2000 -0.0031
## 80 0.6724 nan 0.2000 -0.0020
## 100 0.6419 nan 0.2000 -0.0030
## 120 0.5988 nan 0.2000 -0.0010
## 140 0.5711 nan 0.2000 -0.0004
## 160 0.5392 nan 0.2000 -0.0023
## 180 0.5192 nan 0.2000 -0.0018
## 200 0.4962 nan 0.2000 -0.0023
## 220 0.4714 nan 0.2000 -0.0016
## 240 0.4560 nan 0.2000 -0.0010
## 260 0.4312 nan 0.2000 -0.0013
## 280 0.4111 nan 0.2000 -0.0017
## 300 0.3900 nan 0.2000 -0.0016
## 320 0.3757 nan 0.2000 -0.0010
## 340 0.3624 nan 0.2000 -0.0016
## 360 0.3455 nan 0.2000 -0.0026
## 380 0.3321 nan 0.2000 -0.0006
## 400 0.3171 nan 0.2000 -0.0005
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## 460 0.2852 nan 0.2000 -0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1925 nan 0.2000 0.0403
## 2 1.1136 nan 0.2000 0.0304
## 3 1.0554 nan 0.2000 0.0221
## 4 1.0103 nan 0.2000 0.0197
## 5 0.9847 nan 0.2000 0.0062
## 6 0.9601 nan 0.2000 0.0089
## 7 0.9317 nan 0.2000 0.0110
## 8 0.9146 nan 0.2000 0.0029
## 9 0.9022 nan 0.2000 0.0009
## 10 0.8896 nan 0.2000 0.0019
## 20 0.8106 nan 0.2000 -0.0025
## 40 0.7106 nan 0.2000 -0.0040
## 60 0.6290 nan 0.2000 -0.0024
## 80 0.5685 nan 0.2000 -0.0047
## 100 0.5188 nan 0.2000 -0.0013
## 120 0.4744 nan 0.2000 -0.0038
## 140 0.4301 nan 0.2000 -0.0020
## 160 0.3912 nan 0.2000 -0.0021
## 180 0.3554 nan 0.2000 -0.0003
## 200 0.3351 nan 0.2000 -0.0040
## 220 0.3115 nan 0.2000 -0.0017
## 240 0.2898 nan 0.2000 -0.0011
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## 280 0.2464 nan 0.2000 -0.0012
## 300 0.2289 nan 0.2000 -0.0016
## 320 0.2164 nan 0.2000 -0.0004
## 340 0.1996 nan 0.2000 -0.0004
## 360 0.1848 nan 0.2000 -0.0017
## 380 0.1719 nan 0.2000 -0.0003
## 400 0.1625 nan 0.2000 -0.0011
## 420 0.1514 nan 0.2000 -0.0007
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## 480 0.1271 nan 0.2000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2032 nan 0.2000 0.0359
## 2 1.1263 nan 0.2000 0.0326
## 3 1.0725 nan 0.2000 0.0182
## 4 1.0286 nan 0.2000 0.0157
## 5 1.0027 nan 0.2000 0.0066
## 6 0.9745 nan 0.2000 0.0089
## 7 0.9471 nan 0.2000 0.0075
## 8 0.9207 nan 0.2000 0.0083
## 9 0.8994 nan 0.2000 0.0058
## 10 0.8859 nan 0.2000 0.0016
## 20 0.7949 nan 0.2000 -0.0016
## 40 0.7143 nan 0.2000 -0.0020
## 60 0.6468 nan 0.2000 -0.0006
## 80 0.5879 nan 0.2000 -0.0032
## 100 0.5345 nan 0.2000 -0.0025
## 120 0.4862 nan 0.2000 -0.0012
## 140 0.4431 nan 0.2000 -0.0011
## 160 0.4047 nan 0.2000 -0.0018
## 180 0.3607 nan 0.2000 -0.0020
## 200 0.3350 nan 0.2000 -0.0030
## 220 0.3085 nan 0.2000 -0.0035
## 240 0.2878 nan 0.2000 -0.0022
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## 280 0.2469 nan 0.2000 -0.0025
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## 320 0.2179 nan 0.2000 -0.0014
## 340 0.2042 nan 0.2000 -0.0012
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## 380 0.1791 nan 0.2000 -0.0012
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## 460 0.1372 nan 0.2000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1906 nan 0.2000 0.0429
## 2 1.1242 nan 0.2000 0.0234
## 3 1.0717 nan 0.2000 0.0158
## 4 1.0324 nan 0.2000 0.0121
## 5 0.9855 nan 0.2000 0.0214
## 6 0.9584 nan 0.2000 0.0102
## 7 0.9339 nan 0.2000 0.0080
## 8 0.9111 nan 0.2000 0.0067
## 9 0.8983 nan 0.2000 -0.0010
## 10 0.8799 nan 0.2000 0.0032
## 20 0.7868 nan 0.2000 -0.0006
## 40 0.6919 nan 0.2000 -0.0060
## 60 0.6228 nan 0.2000 -0.0041
## 80 0.5621 nan 0.2000 -0.0063
## 100 0.5088 nan 0.2000 -0.0024
## 120 0.4669 nan 0.2000 0.0001
## 140 0.4383 nan 0.2000 -0.0011
## 160 0.4013 nan 0.2000 -0.0021
## 180 0.3727 nan 0.2000 -0.0005
## 200 0.3445 nan 0.2000 -0.0038
## 220 0.3230 nan 0.2000 -0.0021
## 240 0.2994 nan 0.2000 -0.0008
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## 280 0.2596 nan 0.2000 -0.0019
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## 380 0.1772 nan 0.2000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2042 nan 0.3000 0.0475
## 2 1.1523 nan 0.3000 0.0201
## 3 1.1060 nan 0.3000 0.0159
## 4 1.0687 nan 0.3000 0.0106
## 5 1.0362 nan 0.3000 0.0130
## 6 1.0012 nan 0.3000 0.0143
## 7 0.9854 nan 0.3000 0.0051
## 8 0.9712 nan 0.3000 0.0018
## 9 0.9483 nan 0.3000 0.0103
## 10 0.9400 nan 0.3000 -0.0006
## 20 0.8690 nan 0.3000 -0.0009
## 40 0.8143 nan 0.3000 -0.0071
## 60 0.7842 nan 0.3000 -0.0029
## 80 0.7698 nan 0.3000 -0.0001
## 100 0.7495 nan 0.3000 -0.0007
## 120 0.7318 nan 0.3000 -0.0033
## 140 0.7128 nan 0.3000 0.0000
## 160 0.7055 nan 0.3000 -0.0039
## 180 0.6916 nan 0.3000 0.0002
## 200 0.6823 nan 0.3000 -0.0047
## 220 0.6754 nan 0.3000 -0.0031
## 240 0.6656 nan 0.3000 -0.0017
## 260 0.6506 nan 0.3000 -0.0021
## 280 0.6414 nan 0.3000 -0.0032
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## 460 0.5737 nan 0.3000 -0.0010
## 480 0.5671 nan 0.3000 -0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1943 nan 0.3000 0.0441
## 2 1.1438 nan 0.3000 0.0221
## 3 1.0993 nan 0.3000 0.0174
## 4 1.0556 nan 0.3000 0.0100
## 5 1.0237 nan 0.3000 0.0156
## 6 0.9963 nan 0.3000 0.0073
## 7 0.9806 nan 0.3000 0.0051
## 8 0.9676 nan 0.3000 0.0036
## 9 0.9461 nan 0.3000 0.0061
## 10 0.9380 nan 0.3000 0.0016
## 20 0.8652 nan 0.3000 -0.0020
## 40 0.8194 nan 0.3000 -0.0010
## 60 0.7829 nan 0.3000 -0.0048
## 80 0.7682 nan 0.3000 -0.0026
## 100 0.7555 nan 0.3000 -0.0013
## 120 0.7358 nan 0.3000 -0.0026
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## 160 0.7060 nan 0.3000 -0.0013
## 180 0.7000 nan 0.3000 -0.0050
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## 220 0.6702 nan 0.3000 -0.0038
## 240 0.6606 nan 0.3000 -0.0028
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## 280 0.6357 nan 0.3000 -0.0027
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## 440 0.5786 nan 0.3000 -0.0034
## 460 0.5676 nan 0.3000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2061 nan 0.3000 0.0464
## 2 1.1432 nan 0.3000 0.0319
## 3 1.0981 nan 0.3000 0.0144
## 4 1.0538 nan 0.3000 0.0125
## 5 1.0357 nan 0.3000 0.0061
## 6 1.0239 nan 0.3000 -0.0040
## 7 0.9923 nan 0.3000 0.0135
## 8 0.9751 nan 0.3000 0.0074
## 9 0.9643 nan 0.3000 -0.0029
## 10 0.9469 nan 0.3000 0.0054
## 20 0.8832 nan 0.3000 -0.0024
## 40 0.8387 nan 0.3000 -0.0052
## 60 0.8055 nan 0.3000 -0.0015
## 80 0.7809 nan 0.3000 -0.0018
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## 120 0.7457 nan 0.3000 -0.0043
## 140 0.7300 nan 0.3000 -0.0043
## 160 0.7145 nan 0.3000 -0.0034
## 180 0.7026 nan 0.3000 -0.0023
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1672 nan 0.3000 0.0595
## 2 1.0987 nan 0.3000 0.0297
## 3 1.0432 nan 0.3000 0.0100
## 4 1.0038 nan 0.3000 0.0128
## 5 0.9783 nan 0.3000 0.0051
## 6 0.9548 nan 0.3000 0.0041
## 7 0.9315 nan 0.3000 0.0018
## 8 0.9208 nan 0.3000 0.0015
## 9 0.9024 nan 0.3000 0.0021
## 10 0.8976 nan 0.3000 -0.0093
## 20 0.8117 nan 0.3000 -0.0020
## 40 0.7211 nan 0.3000 -0.0010
## 60 0.6660 nan 0.3000 -0.0021
## 80 0.6150 nan 0.3000 -0.0015
## 100 0.5700 nan 0.3000 -0.0060
## 120 0.5152 nan 0.3000 -0.0058
## 140 0.4755 nan 0.3000 -0.0017
## 160 0.4503 nan 0.3000 -0.0041
## 180 0.4173 nan 0.3000 -0.0021
## 200 0.3860 nan 0.3000 -0.0049
## 220 0.3628 nan 0.3000 -0.0007
## 240 0.3364 nan 0.3000 -0.0005
## 260 0.3097 nan 0.3000 -0.0018
## 280 0.2906 nan 0.3000 -0.0011
## 300 0.2722 nan 0.3000 -0.0018
## 320 0.2591 nan 0.3000 -0.0008
## 340 0.2455 nan 0.3000 -0.0023
## 360 0.2332 nan 0.3000 -0.0008
## 380 0.2240 nan 0.3000 -0.0019
## 400 0.2100 nan 0.3000 -0.0005
## 420 0.2040 nan 0.3000 -0.0001
## 440 0.1946 nan 0.3000 -0.0009
## 460 0.1860 nan 0.3000 -0.0017
## 480 0.1730 nan 0.3000 -0.0016
## 500 0.1641 nan 0.3000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1593 nan 0.3000 0.0502
## 2 1.0833 nan 0.3000 0.0364
## 3 1.0407 nan 0.3000 0.0142
## 4 1.0000 nan 0.3000 0.0157
## 5 0.9641 nan 0.3000 0.0073
## 6 0.9406 nan 0.3000 0.0040
## 7 0.9177 nan 0.3000 0.0093
## 8 0.9003 nan 0.3000 0.0016
## 9 0.8917 nan 0.3000 -0.0066
## 10 0.8843 nan 0.3000 -0.0051
## 20 0.7986 nan 0.3000 -0.0032
## 40 0.7144 nan 0.3000 -0.0009
## 60 0.6534 nan 0.3000 -0.0009
## 80 0.6247 nan 0.3000 -0.0033
## 100 0.5794 nan 0.3000 -0.0032
## 120 0.5491 nan 0.3000 -0.0040
## 140 0.5037 nan 0.3000 -0.0024
## 160 0.4749 nan 0.3000 -0.0021
## 180 0.4359 nan 0.3000 -0.0014
## 200 0.4201 nan 0.3000 -0.0039
## 220 0.3880 nan 0.3000 -0.0020
## 240 0.3682 nan 0.3000 -0.0022
## 260 0.3459 nan 0.3000 -0.0023
## 280 0.3207 nan 0.3000 -0.0008
## 300 0.3050 nan 0.3000 -0.0009
## 320 0.2844 nan 0.3000 -0.0017
## 340 0.2648 nan 0.3000 -0.0015
## 360 0.2500 nan 0.3000 -0.0018
## 380 0.2342 nan 0.3000 -0.0027
## 400 0.2203 nan 0.3000 -0.0019
## 420 0.2062 nan 0.3000 -0.0008
## 440 0.1957 nan 0.3000 -0.0015
## 460 0.1850 nan 0.3000 -0.0003
## 480 0.1754 nan 0.3000 -0.0005
## 500 0.1667 nan 0.3000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1743 nan 0.3000 0.0547
## 2 1.1062 nan 0.3000 0.0287
## 3 1.0504 nan 0.3000 0.0268
## 4 1.0045 nan 0.3000 0.0126
## 5 0.9728 nan 0.3000 0.0145
## 6 0.9476 nan 0.3000 0.0069
## 7 0.9236 nan 0.3000 0.0038
## 8 0.9032 nan 0.3000 0.0063
## 9 0.8905 nan 0.3000 0.0005
## 10 0.8775 nan 0.3000 -0.0013
## 20 0.8154 nan 0.3000 -0.0023
## 40 0.7271 nan 0.3000 -0.0025
## 60 0.6722 nan 0.3000 -0.0034
## 80 0.6114 nan 0.3000 -0.0029
## 100 0.5718 nan 0.3000 -0.0039
## 120 0.5353 nan 0.3000 -0.0019
## 140 0.4929 nan 0.3000 -0.0031
## 160 0.4623 nan 0.3000 -0.0033
## 180 0.4363 nan 0.3000 -0.0026
## 200 0.4109 nan 0.3000 -0.0024
## 220 0.3771 nan 0.3000 -0.0008
## 240 0.3606 nan 0.3000 -0.0037
## 260 0.3366 nan 0.3000 -0.0032
## 280 0.3191 nan 0.3000 -0.0017
## 300 0.2976 nan 0.3000 -0.0007
## 320 0.2780 nan 0.3000 -0.0033
## 340 0.2649 nan 0.3000 -0.0114
## 360 0.2514 nan 0.3000 -0.0022
## 380 0.2433 nan 0.3000 -0.0029
## 400 0.2259 nan 0.3000 -0.0024
## 420 0.2125 nan 0.3000 -0.0013
## 440 0.2006 nan 0.3000 -0.0013
## 460 0.1921 nan 0.3000 -0.0011
## 480 0.1833 nan 0.3000 -0.0019
## 500 0.1733 nan 0.3000 -0.0013
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1468 nan 0.3000 0.0664
## 2 1.0552 nan 0.3000 0.0391
## 3 0.9990 nan 0.3000 0.0213
## 4 0.9556 nan 0.3000 0.0142
## 5 0.9301 nan 0.3000 0.0086
## 6 0.9011 nan 0.3000 0.0061
## 7 0.8775 nan 0.3000 0.0029
## 8 0.8604 nan 0.3000 -0.0020
## 9 0.8480 nan 0.3000 -0.0034
## 10 0.8282 nan 0.3000 0.0020
## 20 0.7472 nan 0.3000 -0.0061
## 40 0.6452 nan 0.3000 -0.0022
## 60 0.5631 nan 0.3000 -0.0003
## 80 0.4964 nan 0.3000 -0.0060
## 100 inf nan 0.3000 nan
## 120 inf nan 0.3000 nan
## 140 inf nan 0.3000 nan
## 160 inf nan 0.3000 nan
## 180 inf nan 0.3000 nan
## 200 inf nan 0.3000 nan
## 220 inf nan 0.3000 nan
## 240 inf nan 0.3000 nan
## 260 inf nan 0.3000 nan
## 280 inf nan 0.3000 nan
## 300 inf nan 0.3000 nan
## 320 inf nan 0.3000 nan
## 340 inf nan 0.3000 nan
## 360 inf nan 0.3000 nan
## 380 inf nan 0.3000 nan
## 400 inf nan 0.3000 nan
## 420 inf nan 0.3000 nan
## 440 inf nan 0.3000 nan
## 460 inf nan 0.3000 nan
## 480 inf nan 0.3000 nan
## 500 inf nan 0.3000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1526 nan 0.3000 0.0604
## 2 1.0599 nan 0.3000 0.0339
## 3 1.0071 nan 0.3000 0.0188
## 4 0.9620 nan 0.3000 0.0119
## 5 0.9280 nan 0.3000 0.0108
## 6 0.9074 nan 0.3000 -0.0058
## 7 0.8799 nan 0.3000 0.0015
## 8 0.8619 nan 0.3000 0.0019
## 9 0.8499 nan 0.3000 -0.0046
## 10 0.8404 nan 0.3000 -0.0036
## 20 0.7590 nan 0.3000 0.0007
## 40 0.6422 nan 0.3000 -0.0050
## 60 0.5499 nan 0.3000 -0.0054
## 80 0.4860 nan 0.3000 -0.0018
## 100 0.4111 nan 0.3000 -0.0046
## 120 0.3623 nan 0.3000 -0.0040
## 140 0.3174 nan 0.3000 -0.0022
## 160 0.2858 nan 0.3000 -0.0023
## 180 0.2582 nan 0.3000 -0.0027
## 200 0.2329 nan 0.3000 -0.0023
## 220 0.2089 nan 0.3000 -0.0018
## 240 0.1909 nan 0.3000 -0.0020
## 260 0.1756 nan 0.3000 -0.0012
## 280 0.1583 nan 0.3000 -0.0015
## 300 0.1433 nan 0.3000 -0.0000
## 320 0.1278 nan 0.3000 -0.0021
## 340 0.1154 nan 0.3000 -0.0011
## 360 0.1050 nan 0.3000 -0.0004
## 380 0.0987 nan 0.3000 -0.0012
## 400 0.0887 nan 0.3000 -0.0008
## 420 0.0809 nan 0.3000 -0.0011
## 440 0.0749 nan 0.3000 -0.0012
## 460 0.0686 nan 0.3000 -0.0002
## 480 0.0635 nan 0.3000 -0.0003
## 500 0.0589 nan 0.3000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1628 nan 0.3000 0.0596
## 2 1.0736 nan 0.3000 0.0375
## 3 1.0220 nan 0.3000 0.0117
## 4 0.9727 nan 0.3000 0.0187
## 5 0.9355 nan 0.3000 0.0114
## 6 0.9162 nan 0.3000 -0.0052
## 7 0.8936 nan 0.3000 0.0019
## 8 0.8909 nan 0.3000 -0.0112
## 9 0.8729 nan 0.3000 -0.0011
## 10 0.8635 nan 0.3000 -0.0083
## 20 0.7835 nan 0.3000 -0.0050
## 40 0.6677 nan 0.3000 -0.0048
## 60 0.5718 nan 0.3000 -0.0059
## 80 0.5054 nan 0.3000 -0.0002
## 100 0.4488 nan 0.3000 -0.0031
## 120 0.4087 nan 0.3000 -0.0010
## 140 0.3607 nan 0.3000 -0.0028
## 160 0.3114 nan 0.3000 -0.0019
## 180 0.2813 nan 0.3000 -0.0033
## 200 0.2478 nan 0.3000 -0.0019
## 220 0.2267 nan 0.3000 -0.0005
## 240 0.2020 nan 0.3000 -0.0017
## 260 0.1842 nan 0.3000 -0.0012
## 280 0.1675 nan 0.3000 -0.0016
## 300 0.1497 nan 0.3000 -0.0004
## 320 0.1338 nan 0.3000 -0.0008
## 340 0.1213 nan 0.3000 -0.0010
## 360 0.1105 nan 0.3000 -0.0009
## 380 0.1009 nan 0.3000 -0.0011
## 400 0.0923 nan 0.3000 -0.0014
## 420 0.0838 nan 0.3000 -0.0002
## 440 0.0766 nan 0.3000 -0.0005
## 460 0.0683 nan 0.3000 -0.0004
## 480 0.0623 nan 0.3000 -0.0007
## 500 0.0577 nan 0.3000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1523 nan 0.5000 0.0704
## 2 1.0840 nan 0.5000 0.0263
## 3 1.0362 nan 0.5000 0.0221
## 4 0.9858 nan 0.5000 0.0195
## 5 0.9747 nan 0.5000 -0.0052
## 6 0.9660 nan 0.5000 -0.0036
## 7 0.9443 nan 0.5000 0.0027
## 8 0.9261 nan 0.5000 0.0051
## 9 0.9100 nan 0.5000 0.0039
## 10 0.9070 nan 0.5000 -0.0083
## 20 0.8518 nan 0.5000 -0.0050
## 40 0.7982 nan 0.5000 -0.0045
## 60 0.7822 nan 0.5000 -0.0099
## 80 0.7468 nan 0.5000 -0.0063
## 100 0.7205 nan 0.5000 -0.0042
## 120 0.7074 nan 0.5000 -0.0135
## 140 0.6816 nan 0.5000 -0.0075
## 160 0.6700 nan 0.5000 -0.0069
## 180 0.6615 nan 0.5000 -0.0099
## 200 0.6463 nan 0.5000 -0.0023
## 220 0.6379 nan 0.5000 0.0029
## 240 0.6192 nan 0.5000 -0.0036
## 260 0.6045 nan 0.5000 -0.0028
## 280 0.5943 nan 0.5000 -0.0038
## 300 0.5939 nan 0.5000 -0.0035
## 320 0.5752 nan 0.5000 -0.0012
## 340 0.5638 nan 0.5000 -0.0039
## 360 0.5637 nan 0.5000 -0.0064
## 380 0.5540 nan 0.5000 -0.0035
## 400 0.5480 nan 0.5000 -0.0010
## 420 0.5337 nan 0.5000 -0.0036
## 440 0.5348 nan 0.5000 -0.0072
## 460 0.5177 nan 0.5000 -0.0035
## 480 0.5102 nan 0.5000 -0.0055
## 500 0.5010 nan 0.5000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1418 nan 0.5000 0.0607
## 2 1.0793 nan 0.5000 0.0230
## 3 1.0287 nan 0.5000 0.0209
## 4 0.9895 nan 0.5000 0.0209
## 5 0.9710 nan 0.5000 0.0051
## 6 0.9609 nan 0.5000 -0.0018
## 7 0.9415 nan 0.5000 0.0079
## 8 0.9364 nan 0.5000 -0.0063
## 9 0.9285 nan 0.5000 -0.0019
## 10 0.9166 nan 0.5000 0.0033
## 20 0.8512 nan 0.5000 -0.0026
## 40 0.8006 nan 0.5000 -0.0088
## 60 0.7766 nan 0.5000 -0.0131
## 80 0.7477 nan 0.5000 -0.0066
## 100 0.7147 nan 0.5000 -0.0068
## 120 0.6973 nan 0.5000 0.0004
## 140 0.6772 nan 0.5000 -0.0031
## 160 0.6545 nan 0.5000 -0.0056
## 180 0.6283 nan 0.5000 -0.0042
## 200 0.6157 nan 0.5000 -0.0039
## 220 0.6057 nan 0.5000 -0.0039
## 240 0.5932 nan 0.5000 -0.0062
## 260 0.5733 nan 0.5000 -0.0074
## 280 0.5580 nan 0.5000 -0.0028
## 300 0.5466 nan 0.5000 -0.0020
## 320 0.5457 nan 0.5000 -0.0070
## 340 0.5300 nan 0.5000 -0.0038
## 360 0.5264 nan 0.5000 -0.0129
## 380 0.5194 nan 0.5000 -0.0036
## 400 0.5136 nan 0.5000 -0.0107
## 420 0.5022 nan 0.5000 -0.0040
## 440 0.4935 nan 0.5000 -0.0022
## 460 0.4792 nan 0.5000 -0.0028
## 480 0.4797 nan 0.5000 -0.0025
## 500 0.4714 nan 0.5000 -0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1533 nan 0.5000 0.0645
## 2 1.0902 nan 0.5000 0.0143
## 3 1.0355 nan 0.5000 0.0244
## 4 1.0113 nan 0.5000 0.0110
## 5 0.9690 nan 0.5000 0.0121
## 6 0.9457 nan 0.5000 0.0086
## 7 0.9403 nan 0.5000 -0.0061
## 8 0.9246 nan 0.5000 0.0082
## 9 0.9143 nan 0.5000 0.0033
## 10 0.9058 nan 0.5000 -0.0082
## 20 0.8644 nan 0.5000 -0.0066
## 40 0.8211 nan 0.5000 -0.0020
## 60 0.7803 nan 0.5000 -0.0016
## 80 0.7583 nan 0.5000 -0.0063
## 100 0.7417 nan 0.5000 -0.0147
## 120 0.7140 nan 0.5000 -0.0015
## 140 0.7060 nan 0.5000 -0.0068
## 160 0.6719 nan 0.5000 -0.0000
## 180 0.6557 nan 0.5000 0.0002
## 200 0.6515 nan 0.5000 -0.0005
## 220 0.6352 nan 0.5000 -0.0072
## 240 0.6117 nan 0.5000 -0.0016
## 260 0.5944 nan 0.5000 -0.0047
## 280 0.5804 nan 0.5000 -0.0034
## 300 0.5705 nan 0.5000 -0.0027
## 320 0.5617 nan 0.5000 -0.0018
## 340 0.5448 nan 0.5000 -0.0079
## 360 0.5370 nan 0.5000 -0.0061
## 380 0.5295 nan 0.5000 -0.0033
## 400 0.5230 nan 0.5000 -0.0040
## 420 0.5113 nan 0.5000 -0.0042
## 440 0.4989 nan 0.5000 -0.0041
## 460 0.4962 nan 0.5000 -0.0050
## 480 0.4891 nan 0.5000 -0.0023
## 500 0.4897 nan 0.5000 -0.0024
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1260 nan 0.5000 0.0637
## 2 1.0534 nan 0.5000 0.0362
## 3 1.0144 nan 0.5000 -0.0001
## 4 0.9595 nan 0.5000 0.0254
## 5 0.9416 nan 0.5000 0.0020
## 6 0.9218 nan 0.5000 0.0010
## 7 0.9096 nan 0.5000 -0.0076
## 8 0.9062 nan 0.5000 -0.0142
## 9 0.8820 nan 0.5000 0.0028
## 10 0.8714 nan 0.5000 -0.0036
## 20 0.7920 nan 0.5000 -0.0164
## 40 0.7028 nan 0.5000 -0.0082
## 60 0.6293 nan 0.5000 -0.0176
## 80 0.5658 nan 0.5000 -0.0075
## 100 0.5153 nan 0.5000 -0.0065
## 120 0.4833 nan 0.5000 -0.0011
## 140 6.3036 nan 0.5000 -0.0079
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 -0.0001
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 0.0002
## 500 inf nan 0.5000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1169 nan 0.5000 0.0753
## 2 1.0221 nan 0.5000 0.0435
## 3 0.9848 nan 0.5000 0.0066
## 4 0.9550 nan 0.5000 0.0035
## 5 0.9228 nan 0.5000 0.0008
## 6 0.9038 nan 0.5000 -0.0022
## 7 0.8946 nan 0.5000 -0.0086
## 8 0.8765 nan 0.5000 0.0024
## 9 0.8613 nan 0.5000 -0.0083
## 10 0.8510 nan 0.5000 -0.0012
## 20 0.8128 nan 0.5000 -0.0092
## 40 0.6925 nan 0.5000 -0.0138
## 60 0.6251 nan 0.5000 -0.0029
## 80 0.5455 nan 0.5000 0.0030
## 100 0.4994 nan 0.5000 -0.0017
## 120 0.4586 nan 0.5000 -0.0031
## 140 0.4710 nan 0.5000 -0.0033
## 160 0.4232 nan 0.5000 -0.0033
## 180 0.3818 nan 0.5000 0.0000
## 200 0.3474 nan 0.5000 -0.0019
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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## 480 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1236 nan 0.5000 0.0586
## 2 1.0318 nan 0.5000 0.0296
## 3 0.9745 nan 0.5000 0.0163
## 4 0.9439 nan 0.5000 0.0090
## 5 0.9267 nan 0.5000 -0.0069
## 6 0.9011 nan 0.5000 0.0029
## 7 0.8905 nan 0.5000 -0.0049
## 8 0.8765 nan 0.5000 -0.0085
## 9 0.8583 nan 0.5000 0.0003
## 10 0.8454 nan 0.5000 0.0017
## 20 0.8124 nan 0.5000 -0.0166
## 40 0.6928 nan 0.5000 -0.0148
## 60 0.5995 nan 0.5000 -0.0015
## 80 0.5367 nan 0.5000 -0.0055
## 100 0.4913 nan 0.5000 -0.0041
## 120 0.4383 nan 0.5000 -0.0020
## 140 0.3953 nan 0.5000 -0.0068
## 160 0.3474 nan 0.5000 -0.0046
## 180 0.3046 nan 0.5000 -0.0020
## 200 0.2814 nan 0.5000 -0.0013
## 220 0.2487 nan 0.5000 -0.0024
## 240 0.2213 nan 0.5000 -0.0021
## 260 0.1953 nan 0.5000 -0.0001
## 280 0.1789 nan 0.5000 -0.0015
## 300 0.1643 nan 0.5000 -0.0031
## 320 0.1493 nan 0.5000 -0.0019
## 340 0.1383 nan 0.5000 -0.0032
## 360 0.1296 nan 0.5000 -0.0014
## 380 0.1200 nan 0.5000 -0.0023
## 400 0.1084 nan 0.5000 -0.0010
## 420 0.0999 nan 0.5000 -0.0007
## 440 0.0915 nan 0.5000 -0.0007
## 460 0.0865 nan 0.5000 -0.0019
## 480 0.0766 nan 0.5000 -0.0004
## 500 0.0709 nan 0.5000 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0767 nan 0.5000 0.0957
## 2 0.9825 nan 0.5000 0.0356
## 3 0.9535 nan 0.5000 -0.0049
## 4 0.9234 nan 0.5000 -0.0135
## 5 0.8881 nan 0.5000 0.0047
## 6 0.8790 nan 0.5000 -0.0101
## 7 0.8558 nan 0.5000 -0.0052
## 8 0.8346 nan 0.5000 -0.0016
## 9 0.8264 nan 0.5000 -0.0089
## 10 0.8085 nan 0.5000 -0.0088
## 20 0.7798 nan 0.5000 -0.0189
## 40 0.6201 nan 0.5000 -0.0149
## 60 0.4674 nan 0.5000 -0.0101
## 80 0.3606 nan 0.5000 -0.0034
## 100 0.2820 nan 0.5000 -0.0030
## 120 0.2343 nan 0.5000 -0.0063
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 -0.0012
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
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## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0857 nan 0.5000 0.0888
## 2 1.0038 nan 0.5000 0.0166
## 3 0.9626 nan 0.5000 0.0067
## 4 0.9176 nan 0.5000 0.0145
## 5 0.8976 nan 0.5000 -0.0018
## 6 0.8654 nan 0.5000 0.0095
## 7 0.8409 nan 0.5000 0.0054
## 8 0.8394 nan 0.5000 -0.0221
## 9 0.8189 nan 0.5000 0.0042
## 10 0.8058 nan 0.5000 -0.0102
## 20 0.7344 nan 0.5000 -0.0163
## 40 3.2705 nan 0.5000 -0.0086
## 60 inf nan 0.5000 nan
## 80 207.9548 nan 0.5000 -0.0012
## 100 207.9103 nan 0.5000 -0.0096
## 120 207.8613 nan 0.5000 -0.0040
## 140 207.8522 nan 0.5000 -0.0108
## 160 207.8261 nan 0.5000 -0.0041
## 180 207.8477 nan 0.5000 -0.0029
## 200 207.8418 nan 0.5000 -0.0119
## 220 207.8285 nan 0.5000 -0.0073
## 240 207.8107 nan 0.5000 -0.0041
## 260 213.7601 nan 0.5000 -0.0015
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0961 nan 0.5000 0.0903
## 2 1.0039 nan 0.5000 0.0375
## 3 0.9536 nan 0.5000 0.0122
## 4 0.9207 nan 0.5000 -0.0022
## 5 0.8823 nan 0.5000 0.0101
## 6 0.8557 nan 0.5000 0.0079
## 7 0.8450 nan 0.5000 -0.0076
## 8 0.8365 nan 0.5000 -0.0111
## 9 0.8208 nan 0.5000 -0.0042
## 10 0.8070 nan 0.5000 -0.0063
## 20 0.7014 nan 0.5000 -0.0141
## 40 0.5825 nan 0.5000 -0.0111
## 60 0.4665 nan 0.5000 -0.0031
## 80 0.3921 nan 0.5000 -0.0148
## 100 0.3276 nan 0.5000 -0.0027
## 120 0.2674 nan 0.5000 -0.0020
## 140 0.2254 nan 0.5000 -0.0029
## 160 0.1908 nan 0.5000 -0.0039
## 180 0.1656 nan 0.5000 -0.0019
## 200 0.1399 nan 0.5000 -0.0010
## 220 0.1210 nan 0.5000 0.0000
## 240 0.1027 nan 0.5000 -0.0013
## 260 0.0879 nan 0.5000 -0.0015
## 280 0.0738 nan 0.5000 -0.0014
## 300 0.0643 nan 0.5000 -0.0013
## 320 0.0545 nan 0.5000 -0.0001
## 340 0.0480 nan 0.5000 -0.0010
## 360 0.0433 nan 0.5000 -0.0011
## 380 0.0385 nan 0.5000 -0.0002
## 400 0.0339 nan 0.5000 -0.0001
## 420 0.0295 nan 0.5000 -0.0007
## 440 0.0258 nan 0.5000 -0.0002
## 460 0.0230 nan 0.5000 -0.0003
## 480 0.0206 nan 0.5000 -0.0002
## 500 0.0183 nan 0.5000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1116 nan 1.0000 0.0824
## 2 1.0445 nan 1.0000 0.0310
## 3 0.9954 nan 1.0000 0.0095
## 4 0.9868 nan 1.0000 -0.0113
## 5 0.9783 nan 1.0000 -0.0067
## 6 0.9430 nan 1.0000 0.0142
## 7 0.9285 nan 1.0000 -0.0092
## 8 0.9434 nan 1.0000 -0.0322
## 9 0.9440 nan 1.0000 -0.0134
## 10 0.9557 nan 1.0000 -0.0228
## 20 0.9453 nan 1.0000 0.0156
## 40 0.8512 nan 1.0000 -0.0007
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1110 nan 1.0000 0.0836
## 2 1.0590 nan 1.0000 0.0018
## 3 1.0146 nan 1.0000 0.0147
## 4 0.9797 nan 1.0000 0.0036
## 5 0.9782 nan 1.0000 -0.0207
## 6 0.9654 nan 1.0000 -0.0100
## 7 0.9571 nan 1.0000 -0.0160
## 8 0.9443 nan 1.0000 0.0022
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## 10 0.9490 nan 1.0000 -0.0420
## 20 1.6920 nan 1.0000 -0.7357
## 40 11172874824984.1309 nan 1.0000 -0.0195
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1202 nan 1.0000 0.0609
## 2 1.0518 nan 1.0000 0.0140
## 3 0.9826 nan 1.0000 0.0348
## 4 0.9664 nan 1.0000 0.0016
## 5 0.9374 nan 1.0000 -0.0103
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## 7 0.9295 nan 1.0000 0.0053
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## 10 0.9524 nan 1.0000 -0.0315
## 20 0.9166 nan 1.0000 -0.0327
## 40 80297868352.9160 nan 1.0000 -0.0212
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0514 nan 1.0000 0.1097
## 2 1.0170 nan 1.0000 -0.0311
## 3 0.9656 nan 1.0000 0.0171
## 4 0.9523 nan 1.0000 -0.0106
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## 10 0.9716 nan 1.0000 -0.0602
## 20 0.9391 nan 1.0000 -0.0626
## 40 0.9278 nan 1.0000 -0.0644
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0526 nan 1.0000 0.1003
## 2 0.9848 nan 1.0000 0.0182
## 3 0.9651 nan 1.0000 -0.0158
## 4 0.9703 nan 1.0000 -0.0407
## 5 0.9456 nan 1.0000 -0.0090
## 6 0.9221 nan 1.0000 -0.0082
## 7 0.9176 nan 1.0000 -0.0203
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## 9 1.0421 nan 1.0000 -0.1560
## 10 1.2935 nan 1.0000 -0.3296
## 20 275001.8140 nan 1.0000 -0.0451
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0573 nan 1.0000 0.1231
## 2 0.9832 nan 1.0000 0.0245
## 3 0.9501 nan 1.0000 -0.0063
## 4 0.9667 nan 1.0000 -0.0430
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## 6 0.9419 nan 1.0000 -0.0248
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## 8 0.9634 nan 1.0000 -0.0348
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## 10 0.9394 nan 1.0000 -0.0052
## 20 0.8747 nan 1.0000 -0.0506
## 40 1.9035 nan 1.0000 -0.0009
## 60 78210123774891.4531 nan 1.0000 -3.2459
## 80 78210123774892.3594 nan 1.0000 -0.0210
## 100 78210123774922.4219 nan 1.0000 0.1194
## 120 78210123835107.1406 nan 1.0000 -60098.9314
## 140 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0796 nan 1.0000 0.0523
## 2 1.0059 nan 1.0000 -0.0062
## 3 0.9606 nan 1.0000 0.0078
## 4 0.9283 nan 1.0000 -0.0077
## 5 0.9192 nan 1.0000 -0.0223
## 6 0.9392 nan 1.0000 -0.0689
## 7 0.9133 nan 1.0000 -0.0417
## 8 1.1763 nan 1.0000 -0.3099
## 9 1.1816 nan 1.0000 -0.0368
## 10 1.1537 nan 1.0000 -0.0126
## 20 4.0216 nan 1.0000 -2.7921
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0163 nan 1.0000 0.1231
## 2 0.9453 nan 1.0000 0.0186
## 3 0.9612 nan 1.0000 -0.0618
## 4 0.9313 nan 1.0000 -0.0134
## 5 0.9028 nan 1.0000 -0.0248
## 6 0.8789 nan 1.0000 -0.0273
## 7 0.8702 nan 1.0000 -0.0342
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## 10 0.8674 nan 1.0000 -0.0653
## 20 1.6924 nan 1.0000 -0.0568
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0015 nan 1.0000 0.1490
## 2 0.9260 nan 1.0000 0.0029
## 3 0.9414 nan 1.0000 -0.0716
## 4 0.9520 nan 1.0000 -0.0420
## 5 0.9515 nan 1.0000 -0.0600
## 6 0.9439 nan 1.0000 -0.0347
## 7 0.8952 nan 1.0000 -0.0063
## 8 0.8809 nan 1.0000 -0.0256
## 9 0.8827 nan 1.0000 -0.0390
## 10 1.0275 nan 1.0000 -0.1530
## 20 29353799.6357 nan 1.0000 -0.0184
## 40 29353799.5020 nan 1.0000 0.0194
## 60 29353800.9091 nan 1.0000 -0.2188
## 80 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0001
## 3 1.2922 nan 0.0010 0.0001
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2905 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2898 nan 0.0010 0.0002
## 20 1.2863 nan 0.0010 0.0001
## 40 1.2794 nan 0.0010 0.0001
## 60 1.2731 nan 0.0010 0.0002
## 80 1.2667 nan 0.0010 0.0002
## 100 1.2607 nan 0.0010 0.0001
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## 140 1.2488 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0001
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0001
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2900 nan 0.0010 0.0002
## 10 1.2896 nan 0.0010 0.0002
## 20 1.2862 nan 0.0010 0.0001
## 40 1.2796 nan 0.0010 0.0002
## 60 1.2729 nan 0.0010 0.0001
## 80 1.2668 nan 0.0010 0.0002
## 100 1.2607 nan 0.0010 0.0002
## 120 1.2550 nan 0.0010 0.0001
## 140 1.2492 nan 0.0010 0.0001
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## 180 1.2385 nan 0.0010 0.0001
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## 380 1.1923 nan 0.0010 0.0001
## 400 1.1883 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2927 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0001
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2900 nan 0.0010 0.0002
## 10 1.2896 nan 0.0010 0.0002
## 20 1.2862 nan 0.0010 0.0002
## 40 1.2794 nan 0.0010 0.0002
## 60 1.2728 nan 0.0010 0.0001
## 80 1.2664 nan 0.0010 0.0001
## 100 1.2604 nan 0.0010 0.0001
## 120 1.2542 nan 0.0010 0.0001
## 140 1.2484 nan 0.0010 0.0001
## 160 1.2432 nan 0.0010 0.0001
## 180 1.2379 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2906 nan 0.0010 0.0002
## 7 1.2902 nan 0.0010 0.0002
## 8 1.2897 nan 0.0010 0.0002
## 9 1.2892 nan 0.0010 0.0002
## 10 1.2888 nan 0.0010 0.0002
## 20 1.2843 nan 0.0010 0.0002
## 40 1.2752 nan 0.0010 0.0002
## 60 1.2663 nan 0.0010 0.0002
## 80 1.2580 nan 0.0010 0.0002
## 100 1.2496 nan 0.0010 0.0001
## 120 1.2417 nan 0.0010 0.0001
## 140 1.2341 nan 0.0010 0.0002
## 160 1.2268 nan 0.0010 0.0001
## 180 1.2194 nan 0.0010 0.0002
## 200 1.2124 nan 0.0010 0.0001
## 220 1.2055 nan 0.0010 0.0002
## 240 1.1991 nan 0.0010 0.0001
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## 280 1.1865 nan 0.0010 0.0001
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## 380 1.1579 nan 0.0010 0.0001
## 400 1.1525 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2921 nan 0.0010 0.0002
## 4 1.2916 nan 0.0010 0.0002
## 5 1.2911 nan 0.0010 0.0002
## 6 1.2907 nan 0.0010 0.0002
## 7 1.2902 nan 0.0010 0.0002
## 8 1.2898 nan 0.0010 0.0002
## 9 1.2893 nan 0.0010 0.0002
## 10 1.2888 nan 0.0010 0.0002
## 20 1.2843 nan 0.0010 0.0002
## 40 1.2752 nan 0.0010 0.0002
## 60 1.2665 nan 0.0010 0.0002
## 80 1.2579 nan 0.0010 0.0002
## 100 1.2499 nan 0.0010 0.0002
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## 140 1.2344 nan 0.0010 0.0002
## 160 1.2269 nan 0.0010 0.0001
## 180 1.2196 nan 0.0010 0.0002
## 200 1.2127 nan 0.0010 0.0001
## 220 1.2059 nan 0.0010 0.0001
## 240 1.1994 nan 0.0010 0.0001
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## 380 1.1582 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2906 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
## 9 1.2891 nan 0.0010 0.0002
## 10 1.2887 nan 0.0010 0.0002
## 20 1.2840 nan 0.0010 0.0002
## 40 1.2748 nan 0.0010 0.0002
## 60 1.2661 nan 0.0010 0.0002
## 80 1.2577 nan 0.0010 0.0002
## 100 1.2496 nan 0.0010 0.0001
## 120 1.2418 nan 0.0010 0.0002
## 140 1.2342 nan 0.0010 0.0002
## 160 1.2267 nan 0.0010 0.0002
## 180 1.2194 nan 0.0010 0.0002
## 200 1.2124 nan 0.0010 0.0001
## 220 1.2056 nan 0.0010 0.0002
## 240 1.1992 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0003
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2895 nan 0.0010 0.0003
## 8 1.2889 nan 0.0010 0.0002
## 9 1.2884 nan 0.0010 0.0002
## 10 1.2878 nan 0.0010 0.0003
## 20 1.2828 nan 0.0010 0.0002
## 40 1.2722 nan 0.0010 0.0002
## 60 1.2620 nan 0.0010 0.0002
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## 140 1.2247 nan 0.0010 0.0002
## 160 1.2158 nan 0.0010 0.0002
## 180 1.2075 nan 0.0010 0.0002
## 200 1.1994 nan 0.0010 0.0002
## 220 1.1920 nan 0.0010 0.0002
## 240 1.1844 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2895 nan 0.0010 0.0002
## 8 1.2890 nan 0.0010 0.0002
## 9 1.2885 nan 0.0010 0.0002
## 10 1.2880 nan 0.0010 0.0003
## 20 1.2828 nan 0.0010 0.0002
## 40 1.2723 nan 0.0010 0.0003
## 60 1.2620 nan 0.0010 0.0002
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## 140 1.2244 nan 0.0010 0.0001
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## 180 1.2074 nan 0.0010 0.0002
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## 280 1.1690 nan 0.0010 0.0002
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## 380 1.1361 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0003
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0003
## 6 1.2900 nan 0.0010 0.0003
## 7 1.2895 nan 0.0010 0.0003
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## 9 1.2883 nan 0.0010 0.0002
## 10 1.2877 nan 0.0010 0.0002
## 20 1.2824 nan 0.0010 0.0002
## 40 1.2719 nan 0.0010 0.0002
## 60 1.2617 nan 0.0010 0.0002
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## 120 1.2330 nan 0.0010 0.0002
## 140 1.2240 nan 0.0010 0.0002
## 160 1.2153 nan 0.0010 0.0002
## 180 1.2071 nan 0.0010 0.0002
## 200 1.1991 nan 0.0010 0.0002
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## 240 1.1835 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2576 nan 0.1000 0.0167
## 2 1.2271 nan 0.1000 0.0118
## 3 1.2029 nan 0.1000 0.0101
## 4 1.1832 nan 0.1000 0.0099
## 5 1.1649 nan 0.1000 0.0070
## 6 1.1466 nan 0.1000 0.0073
## 7 1.1337 nan 0.1000 0.0048
## 8 1.1194 nan 0.1000 0.0052
## 9 1.1046 nan 0.1000 0.0058
## 10 1.0940 nan 0.1000 0.0037
## 20 1.0073 nan 0.1000 0.0017
## 40 0.9201 nan 0.1000 0.0004
## 60 0.8772 nan 0.1000 -0.0009
## 80 0.8476 nan 0.1000 -0.0003
## 100 0.8271 nan 0.1000 -0.0007
## 120 0.8126 nan 0.1000 -0.0005
## 140 0.8007 nan 0.1000 -0.0015
## 160 0.7921 nan 0.1000 -0.0013
## 180 0.7790 nan 0.1000 -0.0003
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## 220 0.7638 nan 0.1000 -0.0001
## 240 0.7560 nan 0.1000 -0.0008
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## 280 0.7436 nan 0.1000 -0.0006
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## 480 0.6924 nan 0.1000 -0.0021
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2593 nan 0.1000 0.0145
## 2 1.2273 nan 0.1000 0.0151
## 3 1.2031 nan 0.1000 0.0112
## 4 1.1859 nan 0.1000 0.0086
## 5 1.1683 nan 0.1000 0.0069
## 6 1.1498 nan 0.1000 0.0068
## 7 1.1335 nan 0.1000 0.0044
## 8 1.1220 nan 0.1000 0.0034
## 9 1.1077 nan 0.1000 0.0063
## 10 1.0956 nan 0.1000 0.0055
## 20 1.0017 nan 0.1000 0.0013
## 40 0.9203 nan 0.1000 0.0003
## 60 0.8806 nan 0.1000 -0.0006
## 80 0.8484 nan 0.1000 -0.0012
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## 120 0.8121 nan 0.1000 -0.0011
## 140 0.7993 nan 0.1000 -0.0013
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## 180 0.7815 nan 0.1000 -0.0012
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## 220 0.7637 nan 0.1000 -0.0009
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## 340 0.7257 nan 0.1000 -0.0010
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## 460 0.6946 nan 0.1000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2593 nan 0.1000 0.0160
## 2 1.2314 nan 0.1000 0.0137
## 3 1.2088 nan 0.1000 0.0104
## 4 1.1914 nan 0.1000 0.0066
## 5 1.1701 nan 0.1000 0.0102
## 6 1.1523 nan 0.1000 0.0083
## 7 1.1351 nan 0.1000 0.0067
## 8 1.1241 nan 0.1000 0.0047
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## 10 1.0990 nan 0.1000 0.0032
## 20 1.0091 nan 0.1000 0.0019
## 40 0.9221 nan 0.1000 0.0014
## 60 0.8759 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2462 nan 0.1000 0.0222
## 2 1.2075 nan 0.1000 0.0161
## 3 1.1733 nan 0.1000 0.0156
## 4 1.1486 nan 0.1000 0.0102
## 5 1.1224 nan 0.1000 0.0100
## 6 1.0985 nan 0.1000 0.0099
## 7 1.0756 nan 0.1000 0.0074
## 8 1.0610 nan 0.1000 0.0046
## 9 1.0451 nan 0.1000 0.0053
## 10 1.0295 nan 0.1000 0.0065
## 20 0.9318 nan 0.1000 0.0020
## 40 0.8499 nan 0.1000 -0.0006
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## 180 0.6400 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2437 nan 0.1000 0.0214
## 2 1.2081 nan 0.1000 0.0156
## 3 1.1749 nan 0.1000 0.0142
## 4 1.1472 nan 0.1000 0.0110
## 5 1.1228 nan 0.1000 0.0088
## 6 1.0990 nan 0.1000 0.0098
## 7 1.0817 nan 0.1000 0.0061
## 8 1.0644 nan 0.1000 0.0070
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## 10 1.0351 nan 0.1000 0.0047
## 20 0.9313 nan 0.1000 0.0018
## 40 0.8439 nan 0.1000 -0.0010
## 60 0.7982 nan 0.1000 -0.0004
## 80 0.7543 nan 0.1000 -0.0006
## 100 0.7272 nan 0.1000 -0.0003
## 120 0.7049 nan 0.1000 -0.0017
## 140 0.6818 nan 0.1000 -0.0018
## 160 0.6630 nan 0.1000 -0.0005
## 180 0.6438 nan 0.1000 -0.0014
## 200 0.6232 nan 0.1000 -0.0009
## 220 0.6044 nan 0.1000 -0.0008
## 240 0.5887 nan 0.1000 -0.0015
## 260 0.5706 nan 0.1000 -0.0011
## 280 0.5557 nan 0.1000 -0.0006
## 300 0.5410 nan 0.1000 -0.0004
## 320 0.5264 nan 0.1000 -0.0011
## 340 0.5158 nan 0.1000 -0.0006
## 360 0.5044 nan 0.1000 -0.0004
## 380 0.4897 nan 0.1000 -0.0017
## 400 0.4779 nan 0.1000 -0.0008
## 420 0.4681 nan 0.1000 -0.0013
## 440 0.4598 nan 0.1000 -0.0014
## 460 0.4464 nan 0.1000 -0.0005
## 480 0.4374 nan 0.1000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2434 nan 0.1000 0.0237
## 2 1.2075 nan 0.1000 0.0152
## 3 1.1752 nan 0.1000 0.0140
## 4 1.1486 nan 0.1000 0.0112
## 5 1.1248 nan 0.1000 0.0106
## 6 1.1076 nan 0.1000 0.0066
## 7 1.0840 nan 0.1000 0.0095
## 8 1.0709 nan 0.1000 0.0049
## 9 1.0569 nan 0.1000 0.0056
## 10 1.0428 nan 0.1000 0.0057
## 20 0.9407 nan 0.1000 0.0012
## 40 0.8462 nan 0.1000 0.0008
## 60 0.7966 nan 0.1000 -0.0017
## 80 0.7595 nan 0.1000 -0.0020
## 100 0.7278 nan 0.1000 -0.0023
## 120 0.7027 nan 0.1000 -0.0001
## 140 0.6786 nan 0.1000 -0.0015
## 160 0.6559 nan 0.1000 -0.0006
## 180 0.6375 nan 0.1000 -0.0021
## 200 0.6216 nan 0.1000 -0.0005
## 220 0.6037 nan 0.1000 -0.0012
## 240 0.5825 nan 0.1000 -0.0006
## 260 0.5655 nan 0.1000 -0.0009
## 280 0.5505 nan 0.1000 -0.0010
## 300 0.5384 nan 0.1000 -0.0011
## 320 0.5283 nan 0.1000 -0.0008
## 340 0.5117 nan 0.1000 -0.0010
## 360 0.4987 nan 0.1000 -0.0005
## 380 0.4870 nan 0.1000 -0.0008
## 400 0.4748 nan 0.1000 -0.0014
## 420 0.4624 nan 0.1000 -0.0007
## 440 0.4532 nan 0.1000 -0.0008
## 460 0.4444 nan 0.1000 -0.0011
## 480 0.4326 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2415 nan 0.1000 0.0223
## 2 1.1976 nan 0.1000 0.0184
## 3 1.1649 nan 0.1000 0.0121
## 4 1.1275 nan 0.1000 0.0170
## 5 1.0966 nan 0.1000 0.0108
## 6 1.0689 nan 0.1000 0.0119
## 7 1.0501 nan 0.1000 0.0068
## 8 1.0316 nan 0.1000 0.0083
## 9 1.0092 nan 0.1000 0.0078
## 10 0.9947 nan 0.1000 0.0031
## 20 0.8865 nan 0.1000 0.0022
## 40 0.7911 nan 0.1000 -0.0019
## 60 0.7327 nan 0.1000 -0.0013
## 80 0.6851 nan 0.1000 -0.0007
## 100 0.6445 nan 0.1000 -0.0024
## 120 0.6097 nan 0.1000 -0.0002
## 140 0.5777 nan 0.1000 -0.0021
## 160 0.5490 nan 0.1000 -0.0009
## 180 0.5220 nan 0.1000 -0.0017
## 200 0.4974 nan 0.1000 -0.0003
## 220 0.4747 nan 0.1000 -0.0009
## 240 0.4547 nan 0.1000 -0.0017
## 260 0.4334 nan 0.1000 -0.0004
## 280 0.4179 nan 0.1000 -0.0007
## 300 0.3999 nan 0.1000 -0.0006
## 320 0.3814 nan 0.1000 -0.0008
## 340 0.3667 nan 0.1000 -0.0011
## 360 0.3530 nan 0.1000 -0.0014
## 380 0.3416 nan 0.1000 -0.0012
## 400 0.3291 nan 0.1000 -0.0004
## 420 0.3171 nan 0.1000 -0.0011
## 440 0.3039 nan 0.1000 -0.0008
## 460 0.2930 nan 0.1000 -0.0009
## 480 0.2826 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2404 nan 0.1000 0.0250
## 2 1.1938 nan 0.1000 0.0193
## 3 1.1569 nan 0.1000 0.0130
## 4 1.1219 nan 0.1000 0.0145
## 5 1.0947 nan 0.1000 0.0110
## 6 1.0730 nan 0.1000 0.0086
## 7 1.0483 nan 0.1000 0.0084
## 8 1.0262 nan 0.1000 0.0094
## 9 1.0089 nan 0.1000 0.0057
## 10 0.9909 nan 0.1000 0.0061
## 20 0.8878 nan 0.1000 0.0008
## 40 0.7905 nan 0.1000 -0.0004
## 60 0.7330 nan 0.1000 -0.0022
## 80 0.6905 nan 0.1000 -0.0013
## 100 0.6529 nan 0.1000 -0.0006
## 120 0.6143 nan 0.1000 -0.0006
## 140 0.5833 nan 0.1000 -0.0016
## 160 0.5491 nan 0.1000 -0.0012
## 180 0.5193 nan 0.1000 -0.0009
## 200 0.4940 nan 0.1000 -0.0009
## 220 0.4722 nan 0.1000 -0.0001
## 240 0.4463 nan 0.1000 -0.0006
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## 280 0.4084 nan 0.1000 -0.0007
## 300 0.3912 nan 0.1000 -0.0010
## 320 0.3808 nan 0.1000 -0.0016
## 340 0.3649 nan 0.1000 -0.0011
## 360 0.3497 nan 0.1000 -0.0005
## 380 0.3367 nan 0.1000 -0.0008
## 400 0.3246 nan 0.1000 -0.0003
## 420 0.3134 nan 0.1000 -0.0013
## 440 0.3020 nan 0.1000 -0.0005
## 460 0.2897 nan 0.1000 -0.0003
## 480 0.2779 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2328 nan 0.1000 0.0259
## 2 1.1934 nan 0.1000 0.0160
## 3 1.1601 nan 0.1000 0.0142
## 4 1.1327 nan 0.1000 0.0067
## 5 1.1063 nan 0.1000 0.0115
## 6 1.0807 nan 0.1000 0.0104
## 7 1.0566 nan 0.1000 0.0092
## 8 1.0413 nan 0.1000 0.0027
## 9 1.0202 nan 0.1000 0.0074
## 10 1.0022 nan 0.1000 0.0060
## 20 0.9020 nan 0.1000 0.0005
## 40 0.8024 nan 0.1000 -0.0011
## 60 0.7452 nan 0.1000 -0.0017
## 80 0.6976 nan 0.1000 -0.0009
## 100 0.6581 nan 0.1000 -0.0024
## 120 0.6257 nan 0.1000 -0.0010
## 140 0.5903 nan 0.1000 -0.0009
## 160 0.5622 nan 0.1000 -0.0014
## 180 0.5365 nan 0.1000 -0.0016
## 200 0.5097 nan 0.1000 -0.0007
## 220 0.4928 nan 0.1000 -0.0006
## 240 0.4648 nan 0.1000 -0.0001
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## 280 0.4291 nan 0.1000 -0.0015
## 300 0.4100 nan 0.1000 -0.0016
## 320 0.3947 nan 0.1000 -0.0013
## 340 0.3801 nan 0.1000 -0.0011
## 360 0.3667 nan 0.1000 -0.0014
## 380 0.3498 nan 0.1000 -0.0007
## 400 0.3365 nan 0.1000 -0.0006
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## 460 0.2960 nan 0.1000 -0.0002
## 480 0.2839 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2356 nan 0.2000 0.0266
## 2 1.1848 nan 0.2000 0.0230
## 3 1.1462 nan 0.2000 0.0136
## 4 1.1219 nan 0.2000 0.0071
## 5 1.0966 nan 0.2000 0.0104
## 6 1.0698 nan 0.2000 0.0109
## 7 1.0512 nan 0.2000 0.0058
## 8 1.0309 nan 0.2000 0.0085
## 9 1.0201 nan 0.2000 0.0022
## 10 1.0045 nan 0.2000 0.0048
## 20 0.9214 nan 0.2000 0.0000
## 40 0.8641 nan 0.2000 0.0007
## 60 0.8190 nan 0.2000 0.0012
## 80 0.7922 nan 0.2000 -0.0039
## 100 0.7783 nan 0.2000 -0.0029
## 120 0.7614 nan 0.2000 -0.0007
## 140 0.7530 nan 0.2000 -0.0001
## 160 0.7349 nan 0.2000 -0.0006
## 180 0.7259 nan 0.2000 -0.0029
## 200 0.7128 nan 0.2000 -0.0029
## 220 0.7007 nan 0.2000 -0.0016
## 240 0.6906 nan 0.2000 -0.0025
## 260 0.6829 nan 0.2000 -0.0015
## 280 0.6732 nan 0.2000 -0.0019
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## 320 0.6620 nan 0.2000 -0.0009
## 340 0.6541 nan 0.2000 -0.0010
## 360 0.6469 nan 0.2000 -0.0027
## 380 0.6411 nan 0.2000 -0.0016
## 400 0.6361 nan 0.2000 -0.0018
## 420 0.6331 nan 0.2000 -0.0019
## 440 0.6308 nan 0.2000 -0.0032
## 460 0.6227 nan 0.2000 -0.0011
## 480 0.6150 nan 0.2000 -0.0008
## 500 0.6096 nan 0.2000 -0.0031
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2273 nan 0.2000 0.0298
## 2 1.1779 nan 0.2000 0.0215
## 3 1.1492 nan 0.2000 0.0115
## 4 1.1222 nan 0.2000 0.0112
## 5 1.0979 nan 0.2000 0.0111
## 6 1.0759 nan 0.2000 0.0061
## 7 1.0524 nan 0.2000 0.0102
## 8 1.0348 nan 0.2000 0.0061
## 9 1.0180 nan 0.2000 0.0058
## 10 1.0056 nan 0.2000 0.0032
## 20 0.9252 nan 0.2000 -0.0019
## 40 0.8487 nan 0.2000 -0.0028
## 60 0.8153 nan 0.2000 -0.0020
## 80 0.7924 nan 0.2000 -0.0002
## 100 0.7731 nan 0.2000 -0.0011
## 120 0.7589 nan 0.2000 -0.0011
## 140 0.7455 nan 0.2000 -0.0053
## 160 0.7310 nan 0.2000 -0.0013
## 180 0.7216 nan 0.2000 -0.0014
## 200 0.7064 nan 0.2000 -0.0003
## 220 0.6987 nan 0.2000 -0.0039
## 240 0.6880 nan 0.2000 -0.0021
## 260 0.6830 nan 0.2000 -0.0033
## 280 0.6742 nan 0.2000 -0.0038
## 300 0.6694 nan 0.2000 -0.0008
## 320 0.6608 nan 0.2000 -0.0016
## 340 0.6533 nan 0.2000 -0.0021
## 360 0.6468 nan 0.2000 -0.0005
## 380 0.6386 nan 0.2000 -0.0017
## 400 0.6313 nan 0.2000 -0.0010
## 420 0.6281 nan 0.2000 -0.0028
## 440 0.6211 nan 0.2000 -0.0014
## 460 0.6169 nan 0.2000 -0.0018
## 480 0.6122 nan 0.2000 -0.0030
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2303 nan 0.2000 0.0305
## 2 1.1835 nan 0.2000 0.0200
## 3 1.1525 nan 0.2000 0.0119
## 4 1.1249 nan 0.2000 0.0128
## 5 1.0937 nan 0.2000 0.0105
## 6 1.0744 nan 0.2000 0.0065
## 7 1.0530 nan 0.2000 0.0109
## 8 1.0331 nan 0.2000 0.0071
## 9 1.0204 nan 0.2000 0.0014
## 10 1.0072 nan 0.2000 0.0007
## 20 0.9175 nan 0.2000 0.0001
## 40 0.8511 nan 0.2000 -0.0005
## 60 0.8148 nan 0.2000 -0.0016
## 80 0.7924 nan 0.2000 -0.0012
## 100 0.7791 nan 0.2000 0.0000
## 120 0.7617 nan 0.2000 -0.0020
## 140 0.7495 nan 0.2000 -0.0013
## 160 0.7344 nan 0.2000 -0.0015
## 180 0.7212 nan 0.2000 -0.0014
## 200 0.7115 nan 0.2000 -0.0015
## 220 0.7035 nan 0.2000 -0.0013
## 240 0.6957 nan 0.2000 -0.0025
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## 280 0.6832 nan 0.2000 -0.0014
## 300 0.6735 nan 0.2000 -0.0013
## 320 0.6634 nan 0.2000 -0.0022
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## 380 0.6405 nan 0.2000 -0.0011
## 400 0.6328 nan 0.2000 -0.0009
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## 460 0.6173 nan 0.2000 -0.0029
## 480 0.6124 nan 0.2000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2025 nan 0.2000 0.0398
## 2 1.1367 nan 0.2000 0.0247
## 3 1.0847 nan 0.2000 0.0224
## 4 1.0504 nan 0.2000 0.0120
## 5 1.0243 nan 0.2000 0.0096
## 6 0.9991 nan 0.2000 0.0062
## 7 0.9756 nan 0.2000 0.0048
## 8 0.9630 nan 0.2000 0.0023
## 9 0.9456 nan 0.2000 0.0046
## 10 0.9323 nan 0.2000 0.0031
## 20 0.8367 nan 0.2000 -0.0032
## 40 0.7598 nan 0.2000 -0.0039
## 60 0.7023 nan 0.2000 -0.0005
## 80 0.6539 nan 0.2000 -0.0019
## 100 0.6148 nan 0.2000 -0.0017
## 120 0.5801 nan 0.2000 -0.0019
## 140 0.5558 nan 0.2000 -0.0032
## 160 0.5203 nan 0.2000 -0.0020
## 180 0.4980 nan 0.2000 -0.0021
## 200 0.4739 nan 0.2000 -0.0002
## 220 0.4529 nan 0.2000 -0.0016
## 240 0.4346 nan 0.2000 -0.0014
## 260 0.4178 nan 0.2000 -0.0015
## 280 0.3990 nan 0.2000 -0.0009
## 300 0.3859 nan 0.2000 -0.0013
## 320 0.3714 nan 0.2000 -0.0013
## 340 0.3564 nan 0.2000 -0.0033
## 360 0.3455 nan 0.2000 -0.0001
## 380 0.3309 nan 0.2000 -0.0015
## 400 0.3187 nan 0.2000 -0.0008
## 420 0.3048 nan 0.2000 -0.0007
## 440 0.2901 nan 0.2000 -0.0004
## 460 0.2801 nan 0.2000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2182 nan 0.2000 0.0336
## 2 1.1512 nan 0.2000 0.0327
## 3 1.1059 nan 0.2000 0.0188
## 4 1.0682 nan 0.2000 0.0152
## 5 1.0334 nan 0.2000 0.0171
## 6 1.0089 nan 0.2000 0.0078
## 7 0.9844 nan 0.2000 0.0064
## 8 0.9706 nan 0.2000 0.0044
## 9 0.9551 nan 0.2000 0.0026
## 10 0.9393 nan 0.2000 0.0048
## 20 0.8571 nan 0.2000 -0.0025
## 40 0.7628 nan 0.2000 -0.0052
## 60 0.7050 nan 0.2000 -0.0022
## 80 0.6449 nan 0.2000 -0.0004
## 100 0.6049 nan 0.2000 -0.0010
## 120 0.5770 nan 0.2000 -0.0044
## 140 0.5389 nan 0.2000 -0.0020
## 160 0.5102 nan 0.2000 -0.0016
## 180 0.4874 nan 0.2000 -0.0020
## 200 0.4676 nan 0.2000 -0.0012
## 220 0.4540 nan 0.2000 -0.0027
## 240 0.4378 nan 0.2000 -0.0036
## 260 0.4168 nan 0.2000 -0.0016
## 280 0.4029 nan 0.2000 -0.0019
## 300 0.3855 nan 0.2000 -0.0036
## 320 0.3703 nan 0.2000 -0.0029
## 340 0.3559 nan 0.2000 -0.0007
## 360 0.3389 nan 0.2000 -0.0002
## 380 0.3254 nan 0.2000 -0.0008
## 400 0.3144 nan 0.2000 -0.0008
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## 480 0.2662 nan 0.2000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2094 nan 0.2000 0.0342
## 2 1.1450 nan 0.2000 0.0234
## 3 1.1026 nan 0.2000 0.0190
## 4 1.0650 nan 0.2000 0.0130
## 5 1.0327 nan 0.2000 0.0105
## 6 1.0118 nan 0.2000 0.0026
## 7 0.9926 nan 0.2000 0.0027
## 8 0.9713 nan 0.2000 0.0067
## 9 0.9485 nan 0.2000 0.0059
## 10 0.9322 nan 0.2000 0.0032
## 20 0.8501 nan 0.2000 -0.0022
## 40 0.7628 nan 0.2000 -0.0008
## 60 0.7034 nan 0.2000 -0.0031
## 80 0.6661 nan 0.2000 -0.0014
## 100 0.6281 nan 0.2000 -0.0008
## 120 0.6008 nan 0.2000 -0.0035
## 140 0.5717 nan 0.2000 -0.0018
## 160 0.5458 nan 0.2000 -0.0040
## 180 0.5244 nan 0.2000 -0.0014
## 200 0.5016 nan 0.2000 -0.0022
## 220 0.4832 nan 0.2000 -0.0034
## 240 0.4573 nan 0.2000 -0.0025
## 260 0.4385 nan 0.2000 -0.0018
## 280 0.4224 nan 0.2000 -0.0011
## 300 0.3995 nan 0.2000 -0.0016
## 320 0.3827 nan 0.2000 -0.0005
## 340 0.3704 nan 0.2000 -0.0021
## 360 0.3496 nan 0.2000 -0.0014
## 380 0.3341 nan 0.2000 -0.0010
## 400 0.3199 nan 0.2000 -0.0019
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1927 nan 0.2000 0.0472
## 2 1.1304 nan 0.2000 0.0232
## 3 1.0794 nan 0.2000 0.0249
## 4 1.0331 nan 0.2000 0.0177
## 5 0.9953 nan 0.2000 0.0113
## 6 0.9685 nan 0.2000 0.0056
## 7 0.9465 nan 0.2000 0.0051
## 8 0.9246 nan 0.2000 0.0055
## 9 0.9063 nan 0.2000 0.0028
## 10 0.8932 nan 0.2000 0.0021
## 20 0.8026 nan 0.2000 -0.0013
## 40 0.6931 nan 0.2000 -0.0038
## 60 0.6244 nan 0.2000 -0.0002
## 80 0.5654 nan 0.2000 -0.0029
## 100 0.5178 nan 0.2000 -0.0019
## 120 0.4737 nan 0.2000 -0.0021
## 140 0.4302 nan 0.2000 -0.0014
## 160 0.3943 nan 0.2000 -0.0030
## 180 0.3597 nan 0.2000 -0.0003
## 200 0.3338 nan 0.2000 -0.0024
## 220 0.3096 nan 0.2000 -0.0018
## 240 0.2873 nan 0.2000 -0.0014
## 260 0.2678 nan 0.2000 -0.0013
## 280 0.2476 nan 0.2000 -0.0016
## 300 0.2299 nan 0.2000 -0.0011
## 320 0.2146 nan 0.2000 -0.0015
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## 380 0.1761 nan 0.2000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1836 nan 0.2000 0.0426
## 2 1.1141 nan 0.2000 0.0245
## 3 1.0643 nan 0.2000 0.0149
## 4 1.0198 nan 0.2000 0.0172
## 5 0.9794 nan 0.2000 0.0044
## 6 0.9560 nan 0.2000 0.0070
## 7 0.9322 nan 0.2000 0.0082
## 8 0.9155 nan 0.2000 -0.0001
## 9 0.8991 nan 0.2000 0.0018
## 10 0.8879 nan 0.2000 0.0005
## 20 0.7908 nan 0.2000 -0.0040
## 40 0.6965 nan 0.2000 -0.0049
## 60 0.6288 nan 0.2000 -0.0032
## 80 0.5603 nan 0.2000 -0.0005
## 100 0.5231 nan 0.2000 -0.0035
## 120 0.4790 nan 0.2000 -0.0022
## 140 0.4392 nan 0.2000 -0.0014
## 160 0.4081 nan 0.2000 -0.0014
## 180 0.3766 nan 0.2000 -0.0006
## 200 0.3486 nan 0.2000 0.0001
## 220 0.3214 nan 0.2000 -0.0026
## 240 0.2980 nan 0.2000 -0.0015
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## 280 0.2555 nan 0.2000 -0.0020
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1982 nan 0.2000 0.0437
## 2 1.1204 nan 0.2000 0.0323
## 3 1.0654 nan 0.2000 0.0182
## 4 1.0240 nan 0.2000 0.0180
## 5 0.9955 nan 0.2000 0.0082
## 6 0.9729 nan 0.2000 0.0046
## 7 0.9520 nan 0.2000 0.0027
## 8 0.9332 nan 0.2000 0.0061
## 9 0.9168 nan 0.2000 0.0017
## 10 0.8974 nan 0.2000 0.0028
## 20 0.8081 nan 0.2000 -0.0021
## 40 0.7104 nan 0.2000 -0.0012
## 60 0.6356 nan 0.2000 -0.0036
## 80 0.5851 nan 0.2000 -0.0070
## 100 0.5313 nan 0.2000 -0.0031
## 120 0.4880 nan 0.2000 -0.0015
## 140 0.4502 nan 0.2000 -0.0025
## 160 0.4104 nan 0.2000 -0.0015
## 180 0.3730 nan 0.2000 -0.0022
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2094 nan 0.3000 0.0392
## 2 1.1574 nan 0.3000 0.0278
## 3 1.1193 nan 0.3000 0.0159
## 4 1.0891 nan 0.3000 0.0128
## 5 1.0536 nan 0.3000 0.0154
## 6 1.0145 nan 0.3000 0.0107
## 7 1.0027 nan 0.3000 -0.0003
## 8 0.9829 nan 0.3000 0.0074
## 9 0.9665 nan 0.3000 0.0061
## 10 0.9604 nan 0.3000 -0.0011
## 20 0.8915 nan 0.3000 -0.0001
## 40 0.8290 nan 0.3000 -0.0014
## 60 0.7930 nan 0.3000 -0.0038
## 80 0.7665 nan 0.3000 -0.0036
## 100 0.7442 nan 0.3000 -0.0020
## 120 0.7246 nan 0.3000 -0.0037
## 140 0.7168 nan 0.3000 -0.0061
## 160 0.7004 nan 0.3000 -0.0061
## 180 0.6806 nan 0.3000 -0.0022
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## 220 0.6628 nan 0.3000 -0.0032
## 240 0.6528 nan 0.3000 -0.0023
## 260 0.6414 nan 0.3000 -0.0007
## 280 0.6354 nan 0.3000 -0.0033
## 300 0.6227 nan 0.3000 -0.0036
## 320 0.6144 nan 0.3000 -0.0044
## 340 0.6055 nan 0.3000 -0.0023
## 360 0.5986 nan 0.3000 -0.0031
## 380 0.5916 nan 0.3000 -0.0038
## 400 0.5833 nan 0.3000 -0.0017
## 420 0.5783 nan 0.3000 -0.0015
## 440 0.5716 nan 0.3000 -0.0015
## 460 0.5640 nan 0.3000 -0.0031
## 480 0.5590 nan 0.3000 -0.0028
## 500 0.5528 nan 0.3000 -0.0015
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1996 nan 0.3000 0.0337
## 2 1.1518 nan 0.3000 0.0242
## 3 1.1092 nan 0.3000 0.0194
## 4 1.0751 nan 0.3000 0.0146
## 5 1.0514 nan 0.3000 0.0077
## 6 1.0327 nan 0.3000 0.0012
## 7 1.0036 nan 0.3000 0.0148
## 8 0.9887 nan 0.3000 0.0044
## 9 0.9728 nan 0.3000 0.0066
## 10 0.9586 nan 0.3000 0.0031
## 20 0.8847 nan 0.3000 0.0003
## 40 0.8289 nan 0.3000 -0.0007
## 60 0.7986 nan 0.3000 -0.0045
## 80 0.7750 nan 0.3000 -0.0018
## 100 0.7544 nan 0.3000 -0.0070
## 120 0.7293 nan 0.3000 -0.0027
## 140 0.7183 nan 0.3000 -0.0021
## 160 0.7015 nan 0.3000 -0.0021
## 180 0.6908 nan 0.3000 -0.0029
## 200 0.6829 nan 0.3000 -0.0034
## 220 0.6699 nan 0.3000 -0.0018
## 240 0.6538 nan 0.3000 -0.0003
## 260 0.6417 nan 0.3000 -0.0016
## 280 0.6310 nan 0.3000 -0.0016
## 300 0.6262 nan 0.3000 0.0007
## 320 0.6207 nan 0.3000 -0.0027
## 340 0.6083 nan 0.3000 -0.0007
## 360 0.6022 nan 0.3000 -0.0011
## 380 0.5947 nan 0.3000 -0.0033
## 400 0.5855 nan 0.3000 -0.0011
## 420 0.5759 nan 0.3000 -0.0015
## 440 0.5701 nan 0.3000 -0.0021
## 460 0.5608 nan 0.3000 -0.0028
## 480 0.5635 nan 0.3000 -0.0037
## 500 0.5538 nan 0.3000 -0.0037
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1927 nan 0.3000 0.0471
## 2 1.1362 nan 0.3000 0.0254
## 3 1.0970 nan 0.3000 0.0159
## 4 1.0614 nan 0.3000 0.0128
## 5 1.0367 nan 0.3000 0.0075
## 6 1.0089 nan 0.3000 0.0157
## 7 0.9943 nan 0.3000 0.0014
## 8 0.9749 nan 0.3000 0.0074
## 9 0.9651 nan 0.3000 0.0029
## 10 0.9520 nan 0.3000 0.0040
## 20 0.8885 nan 0.3000 -0.0050
## 40 0.8136 nan 0.3000 -0.0021
## 60 0.7767 nan 0.3000 -0.0036
## 80 0.7503 nan 0.3000 -0.0015
## 100 0.7432 nan 0.3000 -0.0007
## 120 0.7259 nan 0.3000 -0.0003
## 140 0.7050 nan 0.3000 -0.0037
## 160 0.6910 nan 0.3000 -0.0019
## 180 0.6798 nan 0.3000 -0.0028
## 200 0.6712 nan 0.3000 -0.0005
## 220 0.6647 nan 0.3000 -0.0029
## 240 0.6558 nan 0.3000 -0.0030
## 260 0.6512 nan 0.3000 -0.0034
## 280 0.6463 nan 0.3000 -0.0042
## 300 0.6314 nan 0.3000 -0.0044
## 320 0.6233 nan 0.3000 -0.0014
## 340 0.6170 nan 0.3000 -0.0022
## 360 0.6088 nan 0.3000 -0.0016
## 380 0.5972 nan 0.3000 -0.0009
## 400 0.5902 nan 0.3000 -0.0019
## 420 0.5893 nan 0.3000 -0.0032
## 440 0.5817 nan 0.3000 -0.0018
## 460 0.5771 nan 0.3000 -0.0046
## 480 0.5697 nan 0.3000 -0.0014
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1619 nan 0.3000 0.0593
## 2 1.0927 nan 0.3000 0.0284
## 3 1.0346 nan 0.3000 0.0245
## 4 1.0007 nan 0.3000 0.0092
## 5 0.9618 nan 0.3000 0.0158
## 6 0.9399 nan 0.3000 0.0055
## 7 0.9172 nan 0.3000 0.0037
## 8 0.9059 nan 0.3000 -0.0015
## 9 0.8930 nan 0.3000 0.0005
## 10 0.8779 nan 0.3000 -0.0010
## 20 0.7928 nan 0.3000 0.0003
## 40 0.7211 nan 0.3000 -0.0085
## 60 0.6482 nan 0.3000 -0.0040
## 80 0.6019 nan 0.3000 -0.0027
## 100 0.5671 nan 0.3000 -0.0039
## 120 0.5272 nan 0.3000 -0.0040
## 140 0.4991 nan 0.3000 -0.0039
## 160 0.4861 nan 0.3000 -0.0043
## 180 0.4469 nan 0.3000 -0.0055
## 200 0.4123 nan 0.3000 -0.0036
## 220 0.3870 nan 0.3000 -0.0023
## 240 0.3597 nan 0.3000 -0.0009
## 260 0.3316 nan 0.3000 -0.0026
## 280 0.3136 nan 0.3000 -0.0033
## 300 0.3005 nan 0.3000 -0.0017
## 320 0.2843 nan 0.3000 -0.0004
## 340 0.2661 nan 0.3000 -0.0022
## 360 0.2531 nan 0.3000 -0.0026
## 380 0.2361 nan 0.3000 -0.0025
## 400 0.2249 nan 0.3000 -0.0014
## 420 0.2124 nan 0.3000 -0.0019
## 440 0.2036 nan 0.3000 -0.0000
## 460 0.1891 nan 0.3000 -0.0013
## 480 0.1797 nan 0.3000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1770 nan 0.3000 0.0504
## 2 1.0977 nan 0.3000 0.0350
## 3 1.0429 nan 0.3000 0.0194
## 4 1.0043 nan 0.3000 0.0156
## 5 0.9687 nan 0.3000 0.0093
## 6 0.9449 nan 0.3000 0.0102
## 7 0.9242 nan 0.3000 0.0039
## 8 0.9097 nan 0.3000 0.0053
## 9 0.8993 nan 0.3000 0.0000
## 10 0.8889 nan 0.3000 0.0017
## 20 0.7966 nan 0.3000 -0.0044
## 40 0.7124 nan 0.3000 -0.0034
## 60 0.6545 nan 0.3000 -0.0045
## 80 0.6035 nan 0.3000 -0.0040
## 100 0.5531 nan 0.3000 -0.0018
## 120 0.5161 nan 0.3000 -0.0039
## 140 0.4773 nan 0.3000 -0.0019
## 160 0.4422 nan 0.3000 -0.0042
## 180 0.4128 nan 0.3000 -0.0052
## 200 0.3948 nan 0.3000 -0.0037
## 220 0.3669 nan 0.3000 -0.0021
## 240 0.3429 nan 0.3000 -0.0018
## 260 0.3192 nan 0.3000 -0.0009
## 280 0.2954 nan 0.3000 -0.0006
## 300 0.2806 nan 0.3000 -0.0014
## 320 0.2654 nan 0.3000 -0.0005
## 340 0.2519 nan 0.3000 -0.0021
## 360 0.2438 nan 0.3000 -0.0012
## 380 0.2294 nan 0.3000 -0.0013
## 400 0.2172 nan 0.3000 -0.0024
## 420 0.2091 nan 0.3000 -0.0015
## 440 0.1959 nan 0.3000 -0.0019
## 460 0.1852 nan 0.3000 -0.0008
## 480 0.1770 nan 0.3000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1756 nan 0.3000 0.0469
## 2 1.0993 nan 0.3000 0.0359
## 3 1.0379 nan 0.3000 0.0230
## 4 1.0069 nan 0.3000 0.0010
## 5 0.9772 nan 0.3000 0.0074
## 6 0.9610 nan 0.3000 0.0006
## 7 0.9370 nan 0.3000 0.0091
## 8 0.9160 nan 0.3000 0.0036
## 9 0.9079 nan 0.3000 -0.0006
## 10 0.8918 nan 0.3000 0.0035
## 20 0.8087 nan 0.3000 -0.0020
## 40 0.7124 nan 0.3000 -0.0013
## 60 0.6539 nan 0.3000 -0.0021
## 80 0.6055 nan 0.3000 -0.0018
## 100 0.5562 nan 0.3000 -0.0039
## 120 0.5254 nan 0.3000 -0.0068
## 140 0.4963 nan 0.3000 -0.0038
## 160 0.4705 nan 0.3000 -0.0056
## 180 0.4317 nan 0.3000 -0.0035
## 200 0.4013 nan 0.3000 -0.0004
## 220 0.3709 nan 0.3000 -0.0013
## 240 0.3537 nan 0.3000 -0.0012
## 260 0.3317 nan 0.3000 -0.0034
## 280 0.3135 nan 0.3000 -0.0017
## 300 0.2955 nan 0.3000 -0.0025
## 320 0.2789 nan 0.3000 -0.0028
## 340 0.2625 nan 0.3000 -0.0003
## 360 0.2493 nan 0.3000 -0.0007
## 380 0.2344 nan 0.3000 -0.0005
## 400 0.2183 nan 0.3000 -0.0017
## 420 0.2050 nan 0.3000 -0.0023
## 440 0.1966 nan 0.3000 -0.0005
## 460 0.1857 nan 0.3000 -0.0014
## 480 0.1735 nan 0.3000 -0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1746 nan 0.3000 0.0511
## 2 1.0903 nan 0.3000 0.0308
## 3 1.0274 nan 0.3000 0.0260
## 4 0.9835 nan 0.3000 0.0140
## 5 0.9424 nan 0.3000 0.0147
## 6 0.9171 nan 0.3000 0.0055
## 7 0.8897 nan 0.3000 0.0102
## 8 0.8677 nan 0.3000 0.0036
## 9 0.8553 nan 0.3000 -0.0018
## 10 0.8476 nan 0.3000 -0.0132
## 20 0.7537 nan 0.3000 -0.0053
## 40 0.6341 nan 0.3000 -0.0045
## 60 0.5513 nan 0.3000 -0.0047
## 80 0.4832 nan 0.3000 -0.0004
## 100 0.4220 nan 0.3000 -0.0019
## 120 0.3679 nan 0.3000 -0.0035
## 140 0.3293 nan 0.3000 -0.0019
## 160 0.2912 nan 0.3000 -0.0028
## 180 0.2605 nan 0.3000 -0.0015
## 200 0.2391 nan 0.3000 -0.0005
## 220 0.2155 nan 0.3000 -0.0016
## 240 0.1949 nan 0.3000 -0.0021
## 260 0.1772 nan 0.3000 -0.0015
## 280 0.1620 nan 0.3000 -0.0013
## 300 0.1502 nan 0.3000 -0.0016
## 320 0.1345 nan 0.3000 -0.0009
## 340 0.1249 nan 0.3000 -0.0008
## 360 0.1162 nan 0.3000 -0.0012
## 380 0.1048 nan 0.3000 -0.0008
## 400 0.0966 nan 0.3000 -0.0010
## 420 0.0887 nan 0.3000 -0.0002
## 440 0.0822 nan 0.3000 -0.0003
## 460 0.0769 nan 0.3000 -0.0007
## 480 0.0709 nan 0.3000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1651 nan 0.3000 0.0484
## 2 1.0938 nan 0.3000 0.0191
## 3 1.0263 nan 0.3000 0.0282
## 4 0.9755 nan 0.3000 0.0215
## 5 0.9461 nan 0.3000 0.0028
## 6 0.9109 nan 0.3000 0.0079
## 7 0.8836 nan 0.3000 0.0062
## 8 0.8678 nan 0.3000 0.0008
## 9 0.8517 nan 0.3000 -0.0005
## 10 0.8385 nan 0.3000 0.0004
## 20 0.7405 nan 0.3000 -0.0042
## 40 0.6498 nan 0.3000 -0.0039
## 60 0.5462 nan 0.3000 -0.0068
## 80 0.4676 nan 0.3000 -0.0024
## 100 0.4070 nan 0.3000 -0.0028
## 120 0.3602 nan 0.3000 -0.0021
## 140 0.3188 nan 0.3000 -0.0019
## 160 0.2890 nan 0.3000 -0.0018
## 180 0.2572 nan 0.3000 -0.0002
## 200 0.2303 nan 0.3000 -0.0017
## 220 0.2074 nan 0.3000 -0.0028
## 240 0.1872 nan 0.3000 -0.0007
## 260 0.1665 nan 0.3000 -0.0019
## 280 0.1498 nan 0.3000 -0.0014
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## 320 0.1236 nan 0.3000 -0.0010
## 340 0.1106 nan 0.3000 -0.0005
## 360 0.1026 nan 0.3000 -0.0001
## 380 0.0945 nan 0.3000 -0.0004
## 400 0.0899 nan 0.3000 -0.0011
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## 480 0.0643 nan 0.3000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1568 nan 0.3000 0.0642
## 2 1.0733 nan 0.3000 0.0402
## 3 1.0061 nan 0.3000 0.0269
## 4 0.9629 nan 0.3000 0.0165
## 5 0.9283 nan 0.3000 0.0137
## 6 0.9108 nan 0.3000 0.0009
## 7 0.8855 nan 0.3000 0.0063
## 8 0.8680 nan 0.3000 -0.0020
## 9 0.8498 nan 0.3000 0.0028
## 10 0.8369 nan 0.3000 -0.0026
## 20 0.7494 nan 0.3000 -0.0026
## 40 0.6391 nan 0.3000 -0.0065
## 60 0.5659 nan 0.3000 -0.0038
## 80 0.4871 nan 0.3000 -0.0065
## 100 0.4380 nan 0.3000 -0.0017
## 120 0.3905 nan 0.3000 -0.0044
## 140 0.3412 nan 0.3000 -0.0013
## 160 0.3020 nan 0.3000 -0.0016
## 180 0.2682 nan 0.3000 -0.0033
## 200 0.2417 nan 0.3000 -0.0011
## 220 0.2204 nan 0.3000 -0.0021
## 240 0.1977 nan 0.3000 -0.0032
## 260 0.1783 nan 0.3000 -0.0015
## 280 0.1602 nan 0.3000 -0.0004
## 300 0.1469 nan 0.3000 -0.0012
## 320 0.1337 nan 0.3000 -0.0008
## 340 0.1208 nan 0.3000 -0.0002
## 360 0.1116 nan 0.3000 -0.0010
## 380 0.1021 nan 0.3000 -0.0007
## 400 0.0951 nan 0.3000 -0.0001
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## 440 0.0821 nan 0.3000 -0.0006
## 460 0.0758 nan 0.3000 -0.0017
## 480 0.0693 nan 0.3000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1681 nan 0.5000 0.0502
## 2 1.1141 nan 0.5000 0.0222
## 3 1.0657 nan 0.5000 0.0217
## 4 1.0455 nan 0.5000 -0.0041
## 5 1.0131 nan 0.5000 0.0136
## 6 0.9674 nan 0.5000 0.0176
## 7 0.9478 nan 0.5000 0.0025
## 8 0.9361 nan 0.5000 0.0032
## 9 0.9196 nan 0.5000 0.0029
## 10 0.9055 nan 0.5000 0.0032
## 20 0.8609 nan 0.5000 -0.0026
## 40 0.7906 nan 0.5000 -0.0095
## 60 0.7676 nan 0.5000 -0.0101
## 80 0.7370 nan 0.5000 -0.0078
## 100 0.7170 nan 0.5000 -0.0097
## 120 0.6922 nan 0.5000 -0.0006
## 140 0.6812 nan 0.5000 -0.0063
## 160 0.6632 nan 0.5000 -0.0064
## 180 0.6466 nan 0.5000 -0.0036
## 200 0.6293 nan 0.5000 -0.0018
## 220 0.6236 nan 0.5000 -0.0068
## 240 0.6043 nan 0.5000 -0.0045
## 260 0.5886 nan 0.5000 -0.0048
## 280 0.5751 nan 0.5000 -0.0073
## 300 0.5559 nan 0.5000 -0.0047
## 320 0.5577 nan 0.5000 -0.0053
## 340 0.5358 nan 0.5000 -0.0052
## 360 0.5316 nan 0.5000 -0.0064
## 380 0.5239 nan 0.5000 -0.0047
## 400 0.5158 nan 0.5000 -0.0051
## 420 0.5125 nan 0.5000 -0.0029
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## 460 0.5094 nan 0.5000 0.0004
## 480 0.4948 nan 0.5000 -0.0062
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1776 nan 0.5000 0.0376
## 2 1.1078 nan 0.5000 0.0339
## 3 1.0509 nan 0.5000 0.0203
## 4 0.9983 nan 0.5000 0.0219
## 5 0.9723 nan 0.5000 0.0083
## 6 0.9575 nan 0.5000 -0.0056
## 7 0.9315 nan 0.5000 0.0103
## 8 0.9218 nan 0.5000 -0.0011
## 9 0.9075 nan 0.5000 0.0017
## 10 0.9017 nan 0.5000 -0.0002
## 20 0.8497 nan 0.5000 -0.0061
## 40 0.8027 nan 0.5000 0.0030
## 60 0.7620 nan 0.5000 -0.0064
## 80 0.7327 nan 0.5000 -0.0047
## 100 0.7158 nan 0.5000 -0.0050
## 120 0.7003 nan 0.5000 -0.0011
## 140 0.6710 nan 0.5000 -0.0056
## 160 0.6649 nan 0.5000 -0.0062
## 180 0.6522 nan 0.5000 -0.0171
## 200 0.6350 nan 0.5000 -0.0023
## 220 0.6194 nan 0.5000 -0.0065
## 240 0.6069 nan 0.5000 -0.0032
## 260 0.5984 nan 0.5000 -0.0062
## 280 0.5786 nan 0.5000 -0.0049
## 300 0.5704 nan 0.5000 -0.0057
## 320 0.5593 nan 0.5000 -0.0046
## 340 0.5487 nan 0.5000 -0.0068
## 360 0.5391 nan 0.5000 -0.0012
## 380 0.5413 nan 0.5000 -0.0084
## 400 0.5232 nan 0.5000 -0.0032
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## 460 0.5021 nan 0.5000 -0.0044
## 480 0.4880 nan 0.5000 -0.0059
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1690 nan 0.5000 0.0688
## 2 1.1043 nan 0.5000 0.0347
## 3 1.0523 nan 0.5000 0.0237
## 4 1.0002 nan 0.5000 0.0231
## 5 0.9705 nan 0.5000 0.0092
## 6 0.9636 nan 0.5000 -0.0040
## 7 0.9445 nan 0.5000 0.0033
## 8 0.9348 nan 0.5000 -0.0081
## 9 0.9279 nan 0.5000 -0.0046
## 10 0.9146 nan 0.5000 -0.0076
## 20 0.8558 nan 0.5000 -0.0121
## 40 0.7852 nan 0.5000 -0.0076
## 60 0.7534 nan 0.5000 -0.0049
## 80 0.7334 nan 0.5000 -0.0062
## 100 0.6997 nan 0.5000 -0.0036
## 120 0.6723 nan 0.5000 -0.0031
## 140 0.6488 nan 0.5000 -0.0064
## 160 0.6483 nan 0.5000 -0.0006
## 180 0.6355 nan 0.5000 -0.0022
## 200 0.6279 nan 0.5000 -0.0040
## 220 0.6160 nan 0.5000 -0.0110
## 240 0.6076 nan 0.5000 -0.0076
## 260 0.5902 nan 0.5000 -0.0080
## 280 0.5791 nan 0.5000 -0.0055
## 300 0.5694 nan 0.5000 -0.0036
## 320 0.5593 nan 0.5000 -0.0020
## 340 0.5527 nan 0.5000 -0.0044
## 360 0.5394 nan 0.5000 -0.0071
## 380 0.5287 nan 0.5000 -0.0037
## 400 0.5266 nan 0.5000 -0.0041
## 420 0.5202 nan 0.5000 -0.0034
## 440 0.5091 nan 0.5000 -0.0031
## 460 0.5025 nan 0.5000 -0.0041
## 480 0.4943 nan 0.5000 -0.0077
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1433 nan 0.5000 0.0576
## 2 1.0071 nan 0.5000 0.0532
## 3 0.9725 nan 0.5000 0.0051
## 4 0.9394 nan 0.5000 0.0056
## 5 0.8932 nan 0.5000 0.0097
## 6 0.8782 nan 0.5000 -0.0008
## 7 0.8617 nan 0.5000 0.0020
## 8 0.8421 nan 0.5000 0.0057
## 9 0.8278 nan 0.5000 -0.0066
## 10 0.8253 nan 0.5000 -0.0107
## 20 0.7640 nan 0.5000 -0.0091
## 40 3.0840 nan 0.5000 -0.0063
## 60 inf nan 0.5000 nan
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 -0.0001
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 -0.0001
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1285 nan 0.5000 0.0611
## 2 1.0443 nan 0.5000 0.0375
## 3 0.9803 nan 0.5000 0.0212
## 4 0.9516 nan 0.5000 0.0020
## 5 0.9217 nan 0.5000 0.0081
## 6 0.8961 nan 0.5000 0.0062
## 7 0.8825 nan 0.5000 -0.0035
## 8 0.8688 nan 0.5000 -0.0018
## 9 0.8586 nan 0.5000 -0.0036
## 10 0.8463 nan 0.5000 0.0000
## 20 0.7621 nan 0.5000 -0.0066
## 40 0.6833 nan 0.5000 -0.0011
## 60 0.6122 nan 0.5000 -0.0106
## 80 0.5578 nan 0.5000 -0.0132
## 100 0.5013 nan 0.5000 -0.0073
## 120 0.4452 nan 0.5000 -0.0056
## 140 0.4025 nan 0.5000 -0.0068
## 160 0.3629 nan 0.5000 -0.0043
## 180 0.3197 nan 0.5000 -0.0060
## 200 0.2834 nan 0.5000 -0.0058
## 220 0.2566 nan 0.5000 -0.0052
## 240 0.2318 nan 0.5000 -0.0047
## 260 0.2086 nan 0.5000 -0.0026
## 280 0.1846 nan 0.5000 -0.0023
## 300 0.1694 nan 0.5000 -0.0010
## 320 0.1558 nan 0.5000 -0.0009
## 340 0.1389 nan 0.5000 -0.0014
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## 380 0.1147 nan 0.5000 -0.0008
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## 460 0.0837 nan 0.5000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1264 nan 0.5000 0.0773
## 2 1.0521 nan 0.5000 0.0315
## 3 0.9976 nan 0.5000 0.0095
## 4 0.9736 nan 0.5000 -0.0023
## 5 0.9406 nan 0.5000 0.0028
## 6 0.9012 nan 0.5000 0.0119
## 7 0.8857 nan 0.5000 0.0012
## 8 0.8733 nan 0.5000 0.0003
## 9 0.8716 nan 0.5000 -0.0087
## 10 0.8580 nan 0.5000 -0.0031
## 20 0.7891 nan 0.5000 -0.0092
## 40 0.6899 nan 0.5000 -0.0079
## 60 0.6437 nan 0.5000 -0.0080
## 80 0.5544 nan 0.5000 -0.0103
## 100 0.4777 nan 0.5000 -0.0154
## 120 0.4381 nan 0.5000 -0.0058
## 140 0.3779 nan 0.5000 -0.0022
## 160 0.3300 nan 0.5000 -0.0034
## 180 0.2871 nan 0.5000 -0.0055
## 200 0.2512 nan 0.5000 -0.0012
## 220 0.2197 nan 0.5000 -0.0055
## 240 0.1986 nan 0.5000 -0.0016
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## 500 0.0667 nan 0.5000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0777 nan 0.5000 0.1007
## 2 0.9925 nan 0.5000 0.0111
## 3 0.9436 nan 0.5000 0.0089
## 4 0.8974 nan 0.5000 0.0109
## 5 0.8602 nan 0.5000 0.0038
## 6 0.8363 nan 0.5000 -0.0088
## 7 0.8090 nan 0.5000 0.0032
## 8 0.7974 nan 0.5000 -0.0046
## 9 0.7825 nan 0.5000 -0.0023
## 10 0.7765 nan 0.5000 -0.0173
## 20 0.6808 nan 0.5000 -0.0107
## 40 0.6264 nan 0.5000 -0.0039
## 60 0.6045 nan 0.5000 0.0541
## 80 1378802.6476 nan 0.5000 -0.0053
## 100 1378802.4358 nan 0.5000 -0.0141
## 120 1378802.1941 nan 0.5000 -0.0033
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0737 nan 0.5000 0.0979
## 2 0.9931 nan 0.5000 0.0230
## 3 0.9475 nan 0.5000 0.0051
## 4 0.9224 nan 0.5000 -0.0065
## 5 0.9004 nan 0.5000 -0.0057
## 6 0.8873 nan 0.5000 -0.0101
## 7 0.8633 nan 0.5000 0.0003
## 8 0.8311 nan 0.5000 0.0050
## 9 0.8208 nan 0.5000 -0.0125
## 10 0.8008 nan 0.5000 -0.0024
## 20 0.7112 nan 0.5000 -0.0046
## 40 0.5883 nan 0.5000 -0.0185
## 60 0.4657 nan 0.5000 -0.0148
## 80 0.3774 nan 0.5000 -0.0070
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0865 nan 0.5000 0.1017
## 2 1.0104 nan 0.5000 0.0216
## 3 0.9440 nan 0.5000 0.0151
## 4 0.9158 nan 0.5000 0.0027
## 5 0.8933 nan 0.5000 -0.0015
## 6 0.8688 nan 0.5000 -0.0011
## 7 0.8451 nan 0.5000 0.0028
## 8 0.8339 nan 0.5000 -0.0078
## 9 0.8131 nan 0.5000 -0.0097
## 10 0.8095 nan 0.5000 -0.0126
## 20 0.7033 nan 0.5000 -0.0117
## 40 0.5925 nan 0.5000 -0.0055
## 60 0.4974 nan 0.5000 -0.0119
## 80 0.3875 nan 0.5000 -0.0019
## 100 0.3021 nan 0.5000 -0.0025
## 120 0.2339 nan 0.5000 -0.0051
## 140 0.1936 nan 0.5000 -0.0029
## 160 0.1596 nan 0.5000 0.0005
## 180 0.1391 nan 0.5000 -0.0025
## 200 0.1193 nan 0.5000 -0.0016
## 220 0.0999 nan 0.5000 -0.0006
## 240 0.0863 nan 0.5000 -0.0006
## 260 0.0742 nan 0.5000 -0.0005
## 280 0.0652 nan 0.5000 -0.0012
## 300 0.0574 nan 0.5000 -0.0006
## 320 0.0507 nan 0.5000 -0.0003
## 340 0.0440 nan 0.5000 -0.0003
## 360 0.0378 nan 0.5000 -0.0004
## 380 0.0336 nan 0.5000 -0.0003
## 400 0.0298 nan 0.5000 -0.0007
## 420 0.0258 nan 0.5000 -0.0006
## 440 0.0230 nan 0.5000 -0.0000
## 460 0.0207 nan 0.5000 -0.0005
## 480 0.0174 nan 0.5000 -0.0003
## 500 0.0156 nan 0.5000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1190 nan 1.0000 0.0729
## 2 1.0328 nan 1.0000 0.0366
## 3 0.9880 nan 1.0000 0.0155
## 4 0.9440 nan 1.0000 0.0156
## 5 0.9357 nan 1.0000 -0.0213
## 6 0.9283 nan 1.0000 -0.0091
## 7 0.9245 nan 1.0000 -0.0161
## 8 0.9385 nan 1.0000 -0.0398
## 9 0.9202 nan 1.0000 0.0040
## 10 0.9094 nan 1.0000 -0.0092
## 20 0.9241 nan 1.0000 -0.0132
## 40 135.1166 nan 1.0000 0.0024
## 60 135.1219 nan 1.0000 -0.0020
## 80 135.0764 nan 1.0000 -0.0164
## 100 135.0942 nan 1.0000 -0.0359
## 120 135.0272 nan 1.0000 -0.0000
## 140 135.0309 nan 1.0000 -0.0096
## 160 135.0119 nan 1.0000 0.0007
## 180 inf nan 1.0000 0.0002
## 200 inf nan 1.0000 -0.0391
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
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## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1210 nan 1.0000 0.0659
## 2 1.0628 nan 1.0000 0.0081
## 3 0.9852 nan 1.0000 0.0267
## 4 0.9590 nan 1.0000 -0.0038
## 5 0.9374 nan 1.0000 0.0004
## 6 0.9267 nan 1.0000 -0.0049
## 7 0.9216 nan 1.0000 -0.0066
## 8 0.9188 nan 1.0000 -0.0140
## 9 0.9021 nan 1.0000 -0.0022
## 10 0.8720 nan 1.0000 0.0031
## 20 0.8436 nan 1.0000 -0.0012
## 40 0.8135 nan 1.0000 0.0029
## 60 0.7851 nan 1.0000 -0.0197
## 80 0.7855 nan 1.0000 -0.0162
## 100 0.7744 nan 1.0000 -0.0123
## 120 0.9004 nan 1.0000 -0.1397
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 2169605031634222834246622688202.0000 nan 1.0000 0.0003
## 400 2169605031634222834246622688202.0000 nan 1.0000 -0.0036
## 420 2169605031634222834246622688202.0000 nan 1.0000 0.0000
## 440 2169605031634222834246622688202.0000 nan 1.0000 0.0001
## 460 2169605031634222834246622688202.0000 nan 1.0000 -0.0055
## 480 2169605031634222834246622688202.0000 nan 1.0000 0.0014
## 500 2169605031634222834246622688202.0000 nan 1.0000 0.0098
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1370 nan 1.0000 0.0796
## 2 1.0679 nan 1.0000 0.0121
## 3 1.0229 nan 1.0000 0.0097
## 4 1.0075 nan 1.0000 -0.0106
## 5 0.9598 nan 1.0000 0.0174
## 6 0.9707 nan 1.0000 -0.0280
## 7 0.9487 nan 1.0000 0.0024
## 8 0.9450 nan 1.0000 -0.0227
## 9 0.9438 nan 1.0000 -0.0143
## 10 0.9340 nan 1.0000 0.0006
## 20 0.9048 nan 1.0000 -0.0029
## 40 0.8699 nan 1.0000 -0.0233
## 60 1.0284 nan 1.0000 -0.0051
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 7669239749916452864.0000 nan 1.0000 -0.0149
## 200 7669239749916452864.0000 nan 1.0000 -0.0003
## 220 7669239749916452864.0000 nan 1.0000 -0.0182
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0903 nan 1.0000 0.0895
## 2 1.0564 nan 1.0000 -0.0230
## 3 0.9713 nan 1.0000 0.0120
## 4 0.9568 nan 1.0000 -0.0159
## 5 0.9464 nan 1.0000 -0.0084
## 6 0.9089 nan 1.0000 -0.0051
## 7 0.9121 nan 1.0000 -0.0241
## 8 0.8920 nan 1.0000 -0.0042
## 9 0.8918 nan 1.0000 -0.0324
## 10 0.9123 nan 1.0000 -0.0612
## 20 1.4548 nan 1.0000 -0.0105
## 40 1.3525 nan 1.0000 -0.0066
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0648 nan 1.0000 0.0843
## 2 0.9616 nan 1.0000 0.0450
## 3 0.9257 nan 1.0000 -0.0011
## 4 0.9196 nan 1.0000 -0.0168
## 5 0.9031 nan 1.0000 -0.0067
## 6 1.2230 nan 1.0000 -0.3541
## 7 1.1689 nan 1.0000 0.0311
## 8 1.1567 nan 1.0000 -0.0182
## 9 1.1550 nan 1.0000 -0.0285
## 10 1.0201 nan 1.0000 -0.0141
## 20 1.3511 nan 1.0000 -0.0403
## 40 0.8200 nan 1.0000 -0.0023
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0419 nan 1.0000 0.1262
## 2 0.9804 nan 1.0000 -0.0060
## 3 0.9445 nan 1.0000 -0.0128
## 4 0.9224 nan 1.0000 -0.0159
## 5 0.9170 nan 1.0000 -0.0199
## 6 0.9090 nan 1.0000 -0.0124
## 7 0.8876 nan 1.0000 -0.0039
## 8 0.8749 nan 1.0000 -0.0079
## 9 0.8727 nan 1.0000 -0.0176
## 10 0.9009 nan 1.0000 -0.0045
## 20 1.4943 nan 1.0000 -0.1105
## 40 2.4397 nan 1.0000 -0.0344
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0737 nan 1.0000 0.0619
## 2 0.9953 nan 1.0000 0.0046
## 3 0.9415 nan 1.0000 0.0062
## 4 0.9495 nan 1.0000 -0.0482
## 5 0.9516 nan 1.0000 -0.0329
## 6 0.9362 nan 1.0000 -0.0336
## 7 1.0935 nan 1.0000 -0.1691
## 8 inf nan 1.0000 -inf
## 9 inf nan 1.0000 nan
## 10 inf nan 1.0000 nan
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0846 nan 1.0000 0.0576
## 2 0.9543 nan 1.0000 0.0495
## 3 0.9289 nan 1.0000 -0.0278
## 4 0.9101 nan 1.0000 -0.0186
## 5 0.9033 nan 1.0000 -0.0300
## 6 0.8816 nan 1.0000 -0.0110
## 7 0.8520 nan 1.0000 -0.0171
## 8 0.8622 nan 1.0000 -0.0423
## 9 0.8958 nan 1.0000 -0.0660
## 10 0.9135 nan 1.0000 -0.0596
## 20 2482861072219.3682 nan 1.0000 -0.0692
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0465 nan 1.0000 0.0949
## 2 1.0135 nan 1.0000 -0.0355
## 3 0.9602 nan 1.0000 -0.0131
## 4 0.9319 nan 1.0000 -0.0140
## 5 1.0531 nan 1.0000 -0.1228
## 6 1.4485 nan 1.0000 -0.3662
## 7 1.4523 nan 1.0000 -0.0522
## 8 inf nan 1.0000 -inf
## 9 inf nan 1.0000 nan
## 10 inf nan 1.0000 nan
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
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## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
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## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2921 nan 0.0010 0.0002
## 4 1.2917 nan 0.0010 0.0002
## 5 1.2914 nan 0.0010 0.0002
## 6 1.2910 nan 0.0010 0.0002
## 7 1.2906 nan 0.0010 0.0002
## 8 1.2902 nan 0.0010 0.0001
## 9 1.2898 nan 0.0010 0.0002
## 10 1.2895 nan 0.0010 0.0002
## 20 1.2858 nan 0.0010 0.0002
## 40 1.2789 nan 0.0010 0.0002
## 60 1.2720 nan 0.0010 0.0002
## 80 1.2655 nan 0.0010 0.0002
## 100 1.2591 nan 0.0010 0.0001
## 120 1.2529 nan 0.0010 0.0001
## 140 1.2471 nan 0.0010 0.0001
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## 240 1.2198 nan 0.0010 0.0001
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## 320 1.2008 nan 0.0010 0.0001
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## 380 1.1878 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2905 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2791 nan 0.0010 0.0001
## 60 1.2723 nan 0.0010 0.0001
## 80 1.2658 nan 0.0010 0.0001
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## 140 1.2476 nan 0.0010 0.0001
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## 320 1.2018 nan 0.0010 0.0001
## 340 1.1974 nan 0.0010 0.0001
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## 380 1.1887 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2914 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0001
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## 10 1.2897 nan 0.0010 0.0001
## 20 1.2861 nan 0.0010 0.0001
## 40 1.2791 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0003
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
## 9 1.2891 nan 0.0010 0.0002
## 10 1.2886 nan 0.0010 0.0002
## 20 1.2837 nan 0.0010 0.0002
## 40 1.2746 nan 0.0010 0.0002
## 60 1.2656 nan 0.0010 0.0002
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## 160 1.2253 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
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## 10 1.2886 nan 0.0010 0.0002
## 20 1.2839 nan 0.0010 0.0002
## 40 1.2749 nan 0.0010 0.0002
## 60 1.2659 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
## 8 1.2897 nan 0.0010 0.0002
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## 10 1.2888 nan 0.0010 0.0002
## 20 1.2841 nan 0.0010 0.0002
## 40 1.2749 nan 0.0010 0.0002
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## 180 1.2190 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2895 nan 0.0010 0.0002
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## 20 1.2825 nan 0.0010 0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0003
## 5 1.2906 nan 0.0010 0.0003
## 6 1.2900 nan 0.0010 0.0003
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## 180 1.2077 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0003
## 4 1.2913 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0002
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## 10 1.2881 nan 0.0010 0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2539 nan 0.1000 0.0153
## 2 1.2213 nan 0.1000 0.0143
## 3 1.1972 nan 0.1000 0.0104
## 4 1.1798 nan 0.1000 0.0072
## 5 1.1601 nan 0.1000 0.0090
## 6 1.1451 nan 0.1000 0.0067
## 7 1.1277 nan 0.1000 0.0073
## 8 1.1129 nan 0.1000 0.0067
## 9 1.0991 nan 0.1000 0.0051
## 10 1.0853 nan 0.1000 0.0043
## 20 1.0029 nan 0.1000 0.0012
## 40 0.9222 nan 0.1000 -0.0011
## 60 0.8884 nan 0.1000 -0.0011
## 80 0.8643 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2583 nan 0.1000 0.0161
## 2 1.2280 nan 0.1000 0.0154
## 3 1.2053 nan 0.1000 0.0119
## 4 1.1865 nan 0.1000 0.0066
## 5 1.1688 nan 0.1000 0.0088
## 6 1.1500 nan 0.1000 0.0083
## 7 1.1339 nan 0.1000 0.0073
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## 10 1.0951 nan 0.1000 0.0040
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## 40 0.9245 nan 0.1000 -0.0001
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## 200 0.7904 nan 0.1000 -0.0003
## 220 0.7828 nan 0.1000 -0.0010
## 240 0.7761 nan 0.1000 -0.0004
## 260 0.7676 nan 0.1000 -0.0014
## 280 0.7623 nan 0.1000 -0.0014
## 300 0.7578 nan 0.1000 -0.0013
## 320 0.7511 nan 0.1000 -0.0015
## 340 0.7435 nan 0.1000 -0.0006
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## 380 0.7333 nan 0.1000 -0.0002
## 400 0.7318 nan 0.1000 -0.0011
## 420 0.7257 nan 0.1000 -0.0009
## 440 0.7224 nan 0.1000 -0.0015
## 460 0.7176 nan 0.1000 -0.0009
## 480 0.7139 nan 0.1000 -0.0009
## 500 0.7091 nan 0.1000 -0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2566 nan 0.1000 0.0164
## 2 1.2237 nan 0.1000 0.0153
## 3 1.2005 nan 0.1000 0.0097
## 4 1.1756 nan 0.1000 0.0100
## 5 1.1574 nan 0.1000 0.0077
## 6 1.1408 nan 0.1000 0.0066
## 7 1.1252 nan 0.1000 0.0061
## 8 1.1136 nan 0.1000 0.0039
## 9 1.1008 nan 0.1000 0.0044
## 10 1.0913 nan 0.1000 0.0038
## 20 1.0033 nan 0.1000 0.0008
## 40 0.9235 nan 0.1000 -0.0002
## 60 0.8863 nan 0.1000 -0.0002
## 80 0.8603 nan 0.1000 -0.0006
## 100 0.8395 nan 0.1000 -0.0001
## 120 0.8249 nan 0.1000 -0.0020
## 140 0.8128 nan 0.1000 -0.0008
## 160 0.8044 nan 0.1000 -0.0013
## 180 0.7961 nan 0.1000 -0.0004
## 200 0.7888 nan 0.1000 -0.0015
## 220 0.7815 nan 0.1000 -0.0010
## 240 0.7744 nan 0.1000 -0.0007
## 260 0.7690 nan 0.1000 -0.0007
## 280 0.7619 nan 0.1000 -0.0009
## 300 0.7576 nan 0.1000 -0.0011
## 320 0.7510 nan 0.1000 -0.0012
## 340 0.7468 nan 0.1000 -0.0008
## 360 0.7420 nan 0.1000 -0.0001
## 380 0.7366 nan 0.1000 -0.0009
## 400 0.7329 nan 0.1000 -0.0016
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## 440 0.7226 nan 0.1000 -0.0004
## 460 0.7181 nan 0.1000 -0.0006
## 480 0.7140 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2445 nan 0.1000 0.0219
## 2 1.2040 nan 0.1000 0.0173
## 3 1.1690 nan 0.1000 0.0152
## 4 1.1425 nan 0.1000 0.0111
## 5 1.1178 nan 0.1000 0.0114
## 6 1.0985 nan 0.1000 0.0052
## 7 1.0771 nan 0.1000 0.0087
## 8 1.0596 nan 0.1000 0.0057
## 9 1.0455 nan 0.1000 0.0054
## 10 1.0298 nan 0.1000 0.0061
## 20 0.9388 nan 0.1000 0.0018
## 40 0.8641 nan 0.1000 -0.0005
## 60 0.8151 nan 0.1000 -0.0016
## 80 0.7787 nan 0.1000 -0.0011
## 100 0.7498 nan 0.1000 -0.0013
## 120 0.7211 nan 0.1000 -0.0018
## 140 0.7040 nan 0.1000 -0.0004
## 160 0.6825 nan 0.1000 -0.0005
## 180 0.6593 nan 0.1000 -0.0004
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## 220 0.6264 nan 0.1000 -0.0021
## 240 0.6114 nan 0.1000 -0.0003
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## 280 0.5801 nan 0.1000 -0.0019
## 300 0.5677 nan 0.1000 -0.0008
## 320 0.5508 nan 0.1000 -0.0012
## 340 0.5371 nan 0.1000 -0.0014
## 360 0.5246 nan 0.1000 -0.0002
## 380 0.5134 nan 0.1000 -0.0006
## 400 0.5030 nan 0.1000 -0.0016
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## 460 0.4713 nan 0.1000 -0.0011
## 480 0.4608 nan 0.1000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2476 nan 0.1000 0.0222
## 2 1.2108 nan 0.1000 0.0169
## 3 1.1741 nan 0.1000 0.0155
## 4 1.1461 nan 0.1000 0.0120
## 5 1.1244 nan 0.1000 0.0099
## 6 1.1010 nan 0.1000 0.0092
## 7 1.0807 nan 0.1000 0.0087
## 8 1.0618 nan 0.1000 0.0085
## 9 1.0456 nan 0.1000 0.0054
## 10 1.0338 nan 0.1000 0.0044
## 20 0.9454 nan 0.1000 0.0007
## 40 0.8622 nan 0.1000 -0.0006
## 60 0.8155 nan 0.1000 0.0002
## 80 0.7850 nan 0.1000 -0.0017
## 100 0.7636 nan 0.1000 -0.0023
## 120 0.7386 nan 0.1000 -0.0012
## 140 0.7175 nan 0.1000 -0.0012
## 160 0.6958 nan 0.1000 -0.0011
## 180 0.6751 nan 0.1000 -0.0031
## 200 0.6561 nan 0.1000 -0.0002
## 220 0.6411 nan 0.1000 -0.0019
## 240 0.6273 nan 0.1000 -0.0017
## 260 0.6129 nan 0.1000 -0.0007
## 280 0.5972 nan 0.1000 -0.0007
## 300 0.5843 nan 0.1000 -0.0009
## 320 0.5665 nan 0.1000 -0.0010
## 340 0.5554 nan 0.1000 -0.0012
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## 380 0.5303 nan 0.1000 -0.0009
## 400 0.5204 nan 0.1000 -0.0017
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## 460 0.4874 nan 0.1000 -0.0011
## 480 0.4753 nan 0.1000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2503 nan 0.1000 0.0194
## 2 1.2084 nan 0.1000 0.0160
## 3 1.1743 nan 0.1000 0.0154
## 4 1.1444 nan 0.1000 0.0124
## 5 1.1158 nan 0.1000 0.0098
## 6 1.0933 nan 0.1000 0.0082
## 7 1.0758 nan 0.1000 0.0068
## 8 1.0579 nan 0.1000 0.0076
## 9 1.0413 nan 0.1000 0.0074
## 10 1.0264 nan 0.1000 0.0060
## 20 0.9354 nan 0.1000 0.0022
## 40 0.8603 nan 0.1000 -0.0010
## 60 0.8180 nan 0.1000 -0.0014
## 80 0.7864 nan 0.1000 -0.0017
## 100 0.7579 nan 0.1000 -0.0023
## 120 0.7326 nan 0.1000 -0.0016
## 140 0.7087 nan 0.1000 -0.0010
## 160 0.6877 nan 0.1000 -0.0008
## 180 0.6664 nan 0.1000 -0.0012
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## 220 0.6298 nan 0.1000 -0.0016
## 240 0.6119 nan 0.1000 -0.0011
## 260 0.5978 nan 0.1000 -0.0004
## 280 0.5856 nan 0.1000 -0.0022
## 300 0.5700 nan 0.1000 -0.0006
## 320 0.5572 nan 0.1000 -0.0015
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## 360 0.5318 nan 0.1000 -0.0012
## 380 0.5182 nan 0.1000 -0.0006
## 400 0.5062 nan 0.1000 -0.0013
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## 460 0.4754 nan 0.1000 -0.0008
## 480 0.4658 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2367 nan 0.1000 0.0261
## 2 1.1973 nan 0.1000 0.0172
## 3 1.1605 nan 0.1000 0.0164
## 4 1.1266 nan 0.1000 0.0136
## 5 1.0992 nan 0.1000 0.0105
## 6 1.0761 nan 0.1000 0.0079
## 7 1.0537 nan 0.1000 0.0077
## 8 1.0343 nan 0.1000 0.0086
## 9 1.0164 nan 0.1000 0.0050
## 10 1.0027 nan 0.1000 0.0017
## 20 0.9008 nan 0.1000 -0.0035
## 40 0.8060 nan 0.1000 -0.0001
## 60 0.7541 nan 0.1000 -0.0017
## 80 0.7111 nan 0.1000 -0.0024
## 100 0.6675 nan 0.1000 -0.0003
## 120 0.6284 nan 0.1000 -0.0014
## 140 0.5950 nan 0.1000 -0.0022
## 160 0.5700 nan 0.1000 -0.0002
## 180 0.5496 nan 0.1000 -0.0011
## 200 0.5220 nan 0.1000 -0.0023
## 220 0.4939 nan 0.1000 -0.0011
## 240 0.4750 nan 0.1000 -0.0012
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## 280 0.4339 nan 0.1000 -0.0011
## 300 0.4198 nan 0.1000 -0.0013
## 320 0.4075 nan 0.1000 -0.0019
## 340 0.3919 nan 0.1000 -0.0014
## 360 0.3762 nan 0.1000 -0.0005
## 380 0.3633 nan 0.1000 -0.0017
## 400 0.3484 nan 0.1000 -0.0006
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## 460 0.3123 nan 0.1000 -0.0008
## 480 0.3022 nan 0.1000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2415 nan 0.1000 0.0200
## 2 1.1984 nan 0.1000 0.0182
## 3 1.1583 nan 0.1000 0.0184
## 4 1.1267 nan 0.1000 0.0123
## 5 1.0999 nan 0.1000 0.0131
## 6 1.0761 nan 0.1000 0.0098
## 7 1.0546 nan 0.1000 0.0071
## 8 1.0357 nan 0.1000 0.0063
## 9 1.0168 nan 0.1000 0.0072
## 10 1.0002 nan 0.1000 0.0041
## 20 0.9004 nan 0.1000 0.0021
## 40 0.8068 nan 0.1000 -0.0012
## 60 0.7519 nan 0.1000 -0.0007
## 80 0.7087 nan 0.1000 -0.0023
## 100 0.6761 nan 0.1000 -0.0023
## 120 0.6380 nan 0.1000 -0.0010
## 140 0.6052 nan 0.1000 -0.0015
## 160 0.5761 nan 0.1000 -0.0016
## 180 0.5480 nan 0.1000 -0.0007
## 200 0.5235 nan 0.1000 -0.0014
## 220 0.4975 nan 0.1000 -0.0017
## 240 0.4791 nan 0.1000 -0.0024
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## 280 0.4363 nan 0.1000 -0.0011
## 300 0.4190 nan 0.1000 -0.0009
## 320 0.4034 nan 0.1000 -0.0006
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## 380 0.3569 nan 0.1000 -0.0009
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## 460 0.3032 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2393 nan 0.1000 0.0262
## 2 1.1987 nan 0.1000 0.0182
## 3 1.1576 nan 0.1000 0.0177
## 4 1.1282 nan 0.1000 0.0121
## 5 1.0999 nan 0.1000 0.0103
## 6 1.0797 nan 0.1000 0.0076
## 7 1.0578 nan 0.1000 0.0084
## 8 1.0357 nan 0.1000 0.0085
## 9 1.0191 nan 0.1000 0.0062
## 10 1.0061 nan 0.1000 0.0028
## 20 0.9023 nan 0.1000 0.0005
## 40 0.8139 nan 0.1000 -0.0003
## 60 0.7567 nan 0.1000 0.0005
## 80 0.7101 nan 0.1000 -0.0005
## 100 0.6680 nan 0.1000 -0.0009
## 120 0.6335 nan 0.1000 -0.0018
## 140 0.6059 nan 0.1000 -0.0021
## 160 0.5795 nan 0.1000 -0.0017
## 180 0.5562 nan 0.1000 -0.0013
## 200 0.5353 nan 0.1000 -0.0010
## 220 0.5173 nan 0.1000 -0.0004
## 240 0.4967 nan 0.1000 -0.0006
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## 280 0.4539 nan 0.1000 -0.0009
## 300 0.4378 nan 0.1000 -0.0019
## 320 0.4187 nan 0.1000 -0.0010
## 340 0.4037 nan 0.1000 -0.0007
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## 380 0.3753 nan 0.1000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2256 nan 0.2000 0.0320
## 2 1.1799 nan 0.2000 0.0218
## 3 1.1523 nan 0.2000 0.0119
## 4 1.1201 nan 0.2000 0.0119
## 5 1.0923 nan 0.2000 0.0127
## 6 1.0773 nan 0.2000 0.0055
## 7 1.0571 nan 0.2000 0.0067
## 8 1.0359 nan 0.2000 0.0083
## 9 1.0240 nan 0.2000 0.0039
## 10 1.0124 nan 0.2000 0.0004
## 20 0.9344 nan 0.2000 0.0019
## 40 0.8667 nan 0.2000 -0.0008
## 60 0.8347 nan 0.2000 -0.0022
## 80 0.8118 nan 0.2000 -0.0019
## 100 0.7928 nan 0.2000 -0.0019
## 120 0.7806 nan 0.2000 -0.0018
## 140 0.7657 nan 0.2000 -0.0024
## 160 0.7532 nan 0.2000 -0.0014
## 180 0.7433 nan 0.2000 -0.0023
## 200 0.7302 nan 0.2000 -0.0016
## 220 0.7193 nan 0.2000 -0.0011
## 240 0.7118 nan 0.2000 -0.0018
## 260 0.7044 nan 0.2000 -0.0011
## 280 0.6968 nan 0.2000 -0.0018
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## 320 0.6855 nan 0.2000 -0.0028
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## 380 0.6678 nan 0.2000 -0.0020
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## 460 0.6394 nan 0.2000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2352 nan 0.2000 0.0308
## 2 1.1777 nan 0.2000 0.0201
## 3 1.1411 nan 0.2000 0.0135
## 4 1.1078 nan 0.2000 0.0116
## 5 1.0828 nan 0.2000 0.0081
## 6 1.0610 nan 0.2000 0.0093
## 7 1.0440 nan 0.2000 0.0067
## 8 1.0331 nan 0.2000 0.0023
## 9 1.0146 nan 0.2000 0.0050
## 10 1.0009 nan 0.2000 0.0040
## 20 0.9293 nan 0.2000 0.0001
## 40 0.8831 nan 0.2000 -0.0016
## 60 0.8478 nan 0.2000 -0.0029
## 80 0.8245 nan 0.2000 -0.0005
## 100 0.8087 nan 0.2000 -0.0032
## 120 0.7947 nan 0.2000 -0.0016
## 140 0.7792 nan 0.2000 -0.0019
## 160 0.7659 nan 0.2000 -0.0014
## 180 0.7556 nan 0.2000 -0.0011
## 200 0.7447 nan 0.2000 -0.0041
## 220 0.7335 nan 0.2000 -0.0012
## 240 0.7239 nan 0.2000 -0.0024
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## 280 0.7074 nan 0.2000 -0.0023
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## 320 0.6902 nan 0.2000 -0.0018
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## 380 0.6692 nan 0.2000 -0.0039
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## 460 0.6438 nan 0.2000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2206 nan 0.2000 0.0263
## 2 1.1696 nan 0.2000 0.0210
## 3 1.1356 nan 0.2000 0.0127
## 4 1.1079 nan 0.2000 0.0124
## 5 1.0825 nan 0.2000 0.0099
## 6 1.0602 nan 0.2000 0.0115
## 7 1.0411 nan 0.2000 0.0068
## 8 1.0275 nan 0.2000 0.0053
## 9 1.0071 nan 0.2000 0.0083
## 10 0.9974 nan 0.2000 0.0039
## 20 0.9262 nan 0.2000 -0.0006
## 40 0.8646 nan 0.2000 -0.0004
## 60 0.8364 nan 0.2000 -0.0016
## 80 0.8210 nan 0.2000 -0.0032
## 100 0.8045 nan 0.2000 -0.0021
## 120 0.7923 nan 0.2000 -0.0043
## 140 0.7757 nan 0.2000 -0.0012
## 160 0.7618 nan 0.2000 -0.0019
## 180 0.7526 nan 0.2000 -0.0019
## 200 0.7442 nan 0.2000 -0.0007
## 220 0.7395 nan 0.2000 -0.0018
## 240 0.7323 nan 0.2000 -0.0020
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## 320 0.6963 nan 0.2000 -0.0012
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## 460 0.6505 nan 0.2000 -0.0018
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2091 nan 0.2000 0.0372
## 2 1.1424 nan 0.2000 0.0264
## 3 1.0979 nan 0.2000 0.0198
## 4 1.0637 nan 0.2000 0.0147
## 5 1.0335 nan 0.2000 0.0041
## 6 1.0138 nan 0.2000 0.0071
## 7 0.9881 nan 0.2000 0.0072
## 8 0.9733 nan 0.2000 0.0014
## 9 0.9570 nan 0.2000 0.0029
## 10 0.9456 nan 0.2000 0.0041
## 20 0.8629 nan 0.2000 0.0001
## 40 0.7967 nan 0.2000 -0.0035
## 60 0.7374 nan 0.2000 0.0003
## 80 0.6988 nan 0.2000 -0.0023
## 100 0.6708 nan 0.2000 -0.0020
## 120 0.6396 nan 0.2000 -0.0019
## 140 0.6129 nan 0.2000 -0.0031
## 160 0.5872 nan 0.2000 -0.0022
## 180 0.5611 nan 0.2000 -0.0019
## 200 0.5308 nan 0.2000 -0.0017
## 220 0.5012 nan 0.2000 -0.0026
## 240 0.4768 nan 0.2000 -0.0027
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## 280 0.4420 nan 0.2000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2035 nan 0.2000 0.0428
## 2 1.1485 nan 0.2000 0.0188
## 3 1.1029 nan 0.2000 0.0200
## 4 1.0623 nan 0.2000 0.0149
## 5 1.0265 nan 0.2000 0.0123
## 6 1.0050 nan 0.2000 0.0060
## 7 0.9900 nan 0.2000 0.0025
## 8 0.9737 nan 0.2000 0.0030
## 9 0.9565 nan 0.2000 0.0050
## 10 0.9431 nan 0.2000 0.0049
## 20 0.8741 nan 0.2000 -0.0008
## 40 0.8013 nan 0.2000 -0.0031
## 60 0.7442 nan 0.2000 -0.0055
## 80 0.6999 nan 0.2000 -0.0029
## 100 0.6591 nan 0.2000 -0.0007
## 120 0.6303 nan 0.2000 -0.0013
## 140 0.5990 nan 0.2000 -0.0026
## 160 0.5718 nan 0.2000 -0.0037
## 180 0.5447 nan 0.2000 -0.0045
## 200 0.5211 nan 0.2000 -0.0028
## 220 0.4945 nan 0.2000 -0.0012
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## 460 0.3063 nan 0.2000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2115 nan 0.2000 0.0367
## 2 1.1430 nan 0.2000 0.0290
## 3 1.0973 nan 0.2000 0.0154
## 4 1.0569 nan 0.2000 0.0177
## 5 1.0230 nan 0.2000 0.0083
## 6 0.9945 nan 0.2000 0.0129
## 7 0.9770 nan 0.2000 0.0039
## 8 0.9632 nan 0.2000 0.0027
## 9 0.9507 nan 0.2000 0.0034
## 10 0.9378 nan 0.2000 0.0047
## 20 0.8670 nan 0.2000 -0.0034
## 40 0.7920 nan 0.2000 -0.0035
## 60 0.7325 nan 0.2000 -0.0016
## 80 0.6908 nan 0.2000 -0.0028
## 100 0.6562 nan 0.2000 -0.0013
## 120 0.6235 nan 0.2000 -0.0032
## 140 0.5890 nan 0.2000 -0.0021
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## 180 0.5288 nan 0.2000 -0.0033
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## 240 0.4668 nan 0.2000 -0.0014
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## 300 0.4207 nan 0.2000 -0.0037
## 320 0.4051 nan 0.2000 -0.0013
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## 460 0.3068 nan 0.2000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2118 nan 0.2000 0.0321
## 2 1.1242 nan 0.2000 0.0370
## 3 1.0652 nan 0.2000 0.0222
## 4 1.0222 nan 0.2000 0.0154
## 5 0.9956 nan 0.2000 0.0052
## 6 0.9737 nan 0.2000 0.0093
## 7 0.9524 nan 0.2000 0.0061
## 8 0.9359 nan 0.2000 0.0019
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## 10 0.9071 nan 0.2000 -0.0016
## 20 0.8253 nan 0.2000 -0.0042
## 40 0.7179 nan 0.2000 -0.0049
## 60 0.6550 nan 0.2000 -0.0015
## 80 0.5971 nan 0.2000 -0.0065
## 100 0.5257 nan 0.2000 -0.0018
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## 140 0.4415 nan 0.2000 -0.0012
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## 380 0.1796 nan 0.2000 -0.0008
## 400 0.1695 nan 0.2000 -0.0009
## 420 0.1609 nan 0.2000 -0.0008
## 440 0.1516 nan 0.2000 -0.0016
## 460 0.1427 nan 0.2000 -0.0007
## 480 0.1324 nan 0.2000 -0.0003
## 500 0.1234 nan 0.2000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1938 nan 0.2000 0.0489
## 2 1.1222 nan 0.2000 0.0307
## 3 1.0669 nan 0.2000 0.0145
## 4 1.0201 nan 0.2000 0.0186
## 5 0.9911 nan 0.2000 0.0131
## 6 0.9630 nan 0.2000 0.0088
## 7 0.9484 nan 0.2000 0.0020
## 8 0.9289 nan 0.2000 0.0001
## 9 0.9177 nan 0.2000 -0.0009
## 10 0.9072 nan 0.2000 0.0005
## 20 0.8172 nan 0.2000 -0.0028
## 40 0.7242 nan 0.2000 -0.0078
## 60 0.6527 nan 0.2000 -0.0025
## 80 0.5920 nan 0.2000 -0.0070
## 100 0.5489 nan 0.2000 -0.0011
## 120 0.5025 nan 0.2000 -0.0015
## 140 0.4669 nan 0.2000 -0.0012
## 160 0.4311 nan 0.2000 -0.0012
## 180 0.3981 nan 0.2000 -0.0021
## 200 0.3655 nan 0.2000 -0.0014
## 220 0.3387 nan 0.2000 -0.0028
## 240 0.3140 nan 0.2000 -0.0022
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## 280 0.2678 nan 0.2000 -0.0021
## 300 0.2473 nan 0.2000 -0.0005
## 320 0.2302 nan 0.2000 -0.0002
## 340 0.2163 nan 0.2000 -0.0012
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## 380 0.1878 nan 0.2000 -0.0010
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## 460 0.1508 nan 0.2000 -0.0001
## 480 0.1432 nan 0.2000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1867 nan 0.2000 0.0443
## 2 1.1230 nan 0.2000 0.0284
## 3 1.0711 nan 0.2000 0.0210
## 4 1.0342 nan 0.2000 0.0121
## 5 0.9998 nan 0.2000 0.0070
## 6 0.9728 nan 0.2000 0.0091
## 7 0.9559 nan 0.2000 0.0028
## 8 0.9375 nan 0.2000 0.0021
## 9 0.9201 nan 0.2000 0.0050
## 10 0.9086 nan 0.2000 0.0007
## 20 0.8181 nan 0.2000 0.0002
## 40 0.7242 nan 0.2000 -0.0040
## 60 0.6584 nan 0.2000 -0.0081
## 80 0.5933 nan 0.2000 -0.0022
## 100 0.5360 nan 0.2000 -0.0034
## 120 0.4957 nan 0.2000 -0.0025
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## 180 0.3853 nan 0.2000 -0.0012
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## 220 0.3384 nan 0.2000 -0.0010
## 240 0.3156 nan 0.2000 -0.0022
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## 280 0.2704 nan 0.2000 -0.0013
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## 320 0.2362 nan 0.2000 -0.0009
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## 380 0.1903 nan 0.2000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2363 nan 0.3000 0.0200
## 2 1.1506 nan 0.3000 0.0415
## 3 1.0909 nan 0.3000 0.0226
## 4 1.0500 nan 0.3000 0.0127
## 5 1.0319 nan 0.3000 0.0055
## 6 1.0056 nan 0.3000 0.0110
## 7 0.9954 nan 0.3000 -0.0037
## 8 0.9802 nan 0.3000 0.0041
## 9 0.9649 nan 0.3000 0.0031
## 10 0.9558 nan 0.3000 0.0007
## 20 0.8894 nan 0.3000 0.0013
## 40 0.8396 nan 0.3000 -0.0002
## 60 0.8134 nan 0.3000 -0.0031
## 80 0.7919 nan 0.3000 -0.0059
## 100 0.7764 nan 0.3000 -0.0015
## 120 0.7604 nan 0.3000 -0.0008
## 140 0.7509 nan 0.3000 -0.0028
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## 180 0.7173 nan 0.3000 -0.0045
## 200 0.7092 nan 0.3000 -0.0046
## 220 0.6969 nan 0.3000 -0.0030
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## 280 0.6786 nan 0.3000 -0.0006
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## 320 0.6641 nan 0.3000 -0.0017
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## 380 0.6385 nan 0.3000 -0.0039
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## 460 0.6045 nan 0.3000 -0.0029
## 480 0.5996 nan 0.3000 -0.0032
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2039 nan 0.3000 0.0467
## 2 1.1430 nan 0.3000 0.0321
## 3 1.0989 nan 0.3000 0.0171
## 4 1.0648 nan 0.3000 0.0115
## 5 1.0226 nan 0.3000 0.0146
## 6 1.0032 nan 0.3000 0.0058
## 7 0.9895 nan 0.3000 0.0031
## 8 0.9775 nan 0.3000 0.0040
## 9 0.9691 nan 0.3000 0.0019
## 10 0.9549 nan 0.3000 0.0008
## 20 0.8869 nan 0.3000 -0.0005
## 40 0.8478 nan 0.3000 -0.0057
## 60 0.8187 nan 0.3000 -0.0053
## 80 0.7926 nan 0.3000 -0.0032
## 100 0.7733 nan 0.3000 -0.0004
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## 180 0.7242 nan 0.3000 -0.0034
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## 220 0.6997 nan 0.3000 -0.0019
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## 280 0.6753 nan 0.3000 -0.0032
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## 380 0.6248 nan 0.3000 -0.0022
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## 460 0.6006 nan 0.3000 -0.0028
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2019 nan 0.3000 0.0447
## 2 1.1506 nan 0.3000 0.0200
## 3 1.1005 nan 0.3000 0.0182
## 4 1.0705 nan 0.3000 0.0096
## 5 1.0422 nan 0.3000 0.0102
## 6 1.0147 nan 0.3000 0.0092
## 7 1.0041 nan 0.3000 0.0043
## 8 0.9857 nan 0.3000 0.0069
## 9 0.9732 nan 0.3000 0.0012
## 10 0.9644 nan 0.3000 0.0040
## 20 0.8889 nan 0.3000 0.0005
## 40 0.8451 nan 0.3000 -0.0001
## 60 0.8101 nan 0.3000 -0.0054
## 80 0.7943 nan 0.3000 -0.0060
## 100 0.7744 nan 0.3000 -0.0027
## 120 0.7560 nan 0.3000 -0.0011
## 140 0.7405 nan 0.3000 -0.0014
## 160 0.7290 nan 0.3000 -0.0021
## 180 0.7133 nan 0.3000 -0.0022
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## 220 0.6947 nan 0.3000 -0.0017
## 240 0.6848 nan 0.3000 -0.0041
## 260 0.6737 nan 0.3000 -0.0020
## 280 0.6694 nan 0.3000 -0.0025
## 300 0.6577 nan 0.3000 -0.0029
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## 380 0.6325 nan 0.3000 -0.0015
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## 460 0.6025 nan 0.3000 -0.0032
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1703 nan 0.3000 0.0556
## 2 1.0888 nan 0.3000 0.0341
## 3 1.0505 nan 0.3000 0.0084
## 4 1.0082 nan 0.3000 0.0180
## 5 0.9842 nan 0.3000 0.0030
## 6 0.9698 nan 0.3000 0.0033
## 7 0.9526 nan 0.3000 0.0051
## 8 0.9302 nan 0.3000 0.0046
## 9 0.9223 nan 0.3000 -0.0090
## 10 0.9106 nan 0.3000 -0.0009
## 20 0.8423 nan 0.3000 -0.0072
## 40 0.7603 nan 0.3000 -0.0046
## 60 0.6979 nan 0.3000 -0.0047
## 80 0.6455 nan 0.3000 -0.0031
## 100 0.6151 nan 0.3000 -0.0079
## 120 0.5752 nan 0.3000 -0.0008
## 140 0.5366 nan 0.3000 -0.0039
## 160 0.5037 nan 0.3000 -0.0013
## 180 0.4564 nan 0.3000 -0.0035
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## 220 0.4112 nan 0.3000 -0.0019
## 240 0.3856 nan 0.3000 -0.0028
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## 280 0.3381 nan 0.3000 -0.0017
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## 320 0.3003 nan 0.3000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1642 nan 0.3000 0.0541
## 2 1.0945 nan 0.3000 0.0303
## 3 1.0501 nan 0.3000 0.0110
## 4 0.9976 nan 0.3000 0.0221
## 5 0.9703 nan 0.3000 0.0081
## 6 0.9502 nan 0.3000 0.0058
## 7 0.9310 nan 0.3000 0.0023
## 8 0.9210 nan 0.3000 -0.0028
## 9 0.9082 nan 0.3000 0.0028
## 10 0.8996 nan 0.3000 -0.0046
## 20 0.8211 nan 0.3000 -0.0087
## 40 0.7549 nan 0.3000 -0.0019
## 60 0.6970 nan 0.3000 -0.0057
## 80 0.6318 nan 0.3000 -0.0041
## 100 0.5917 nan 0.3000 -0.0055
## 120 0.5531 nan 0.3000 -0.0038
## 140 0.5122 nan 0.3000 -0.0031
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## 180 0.4556 nan 0.3000 -0.0015
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## 220 0.4006 nan 0.3000 -0.0048
## 240 0.3829 nan 0.3000 -0.0020
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## 280 0.3406 nan 0.3000 -0.0036
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1735 nan 0.3000 0.0461
## 2 1.0984 nan 0.3000 0.0317
## 3 1.0460 nan 0.3000 0.0188
## 4 1.0065 nan 0.3000 0.0125
## 5 0.9852 nan 0.3000 0.0056
## 6 0.9630 nan 0.3000 0.0066
## 7 0.9470 nan 0.3000 -0.0003
## 8 0.9256 nan 0.3000 0.0050
## 9 0.9085 nan 0.3000 0.0048
## 10 0.8954 nan 0.3000 0.0024
## 20 0.8293 nan 0.3000 0.0001
## 40 0.7545 nan 0.3000 -0.0028
## 60 0.6990 nan 0.3000 -0.0046
## 80 0.6408 nan 0.3000 -0.0060
## 100 0.6057 nan 0.3000 -0.0003
## 120 0.5672 nan 0.3000 -0.0023
## 140 0.5332 nan 0.3000 -0.0023
## 160 0.4958 nan 0.3000 0.0007
## 180 0.4757 nan 0.3000 -0.0008
## 200 0.4524 nan 0.3000 -0.0029
## 220 0.4225 nan 0.3000 -0.0029
## 240 0.4001 nan 0.3000 -0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1600 nan 0.3000 0.0511
## 2 1.0753 nan 0.3000 0.0373
## 3 1.0204 nan 0.3000 0.0179
## 4 0.9846 nan 0.3000 0.0095
## 5 0.9563 nan 0.3000 0.0021
## 6 0.9367 nan 0.3000 -0.0035
## 7 0.9095 nan 0.3000 0.0069
## 8 0.8923 nan 0.3000 0.0023
## 9 0.8720 nan 0.3000 0.0046
## 10 0.8599 nan 0.3000 -0.0021
## 20 0.7779 nan 0.3000 -0.0072
## 40 0.6703 nan 0.3000 -0.0097
## 60 0.5859 nan 0.3000 -0.0011
## 80 0.5078 nan 0.3000 -0.0021
## 100 0.4477 nan 0.3000 -0.0055
## 120 0.4031 nan 0.3000 -0.0020
## 140 0.3409 nan 0.3000 -0.0011
## 160 0.3061 nan 0.3000 -0.0036
## 180 0.2750 nan 0.3000 -0.0017
## 200 0.2490 nan 0.3000 -0.0026
## 220 0.2246 nan 0.3000 -0.0027
## 240 0.2024 nan 0.3000 -0.0017
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1559 nan 0.3000 0.0554
## 2 1.0671 nan 0.3000 0.0375
## 3 1.0153 nan 0.3000 0.0203
## 4 0.9798 nan 0.3000 0.0083
## 5 0.9516 nan 0.3000 0.0021
## 6 0.9190 nan 0.3000 -0.0029
## 7 0.9051 nan 0.3000 -0.0057
## 8 0.8838 nan 0.3000 0.0055
## 9 0.8665 nan 0.3000 -0.0010
## 10 0.8531 nan 0.3000 -0.0080
## 20 0.7544 nan 0.3000 -0.0043
## 40 0.6598 nan 0.3000 -0.0018
## 60 0.5500 nan 0.3000 -0.0067
## 80 0.4839 nan 0.3000 -0.0039
## 100 0.4237 nan 0.3000 -0.0048
## 120 0.3685 nan 0.3000 -0.0033
## 140 0.3295 nan 0.3000 -0.0015
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## 180 0.2636 nan 0.3000 -0.0008
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## 220 0.2178 nan 0.3000 -0.0032
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1598 nan 0.3000 0.0606
## 2 1.0663 nan 0.3000 0.0344
## 3 1.0117 nan 0.3000 0.0139
## 4 0.9730 nan 0.3000 0.0137
## 5 0.9476 nan 0.3000 -0.0063
## 6 0.9230 nan 0.3000 0.0059
## 7 0.9097 nan 0.3000 -0.0017
## 8 0.9006 nan 0.3000 -0.0062
## 9 0.8835 nan 0.3000 -0.0031
## 10 0.8705 nan 0.3000 -0.0037
## 20 0.7772 nan 0.3000 -0.0077
## 40 0.6546 nan 0.3000 -0.0020
## 60 0.6023 nan 0.3000 -0.0060
## 80 0.5115 nan 0.3000 0.0001
## 100 0.4509 nan 0.3000 -0.0041
## 120 0.3921 nan 0.3000 -0.0014
## 140 0.3455 nan 0.3000 -0.0026
## 160 0.3013 nan 0.3000 -0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1728 nan 0.5000 0.0484
## 2 1.0864 nan 0.5000 0.0288
## 3 1.0332 nan 0.5000 0.0239
## 4 1.0041 nan 0.5000 0.0008
## 5 0.9755 nan 0.5000 0.0147
## 6 0.9669 nan 0.5000 -0.0017
## 7 0.9528 nan 0.5000 0.0018
## 8 0.9484 nan 0.5000 -0.0029
## 9 0.9330 nan 0.5000 0.0047
## 10 0.9247 nan 0.5000 0.0016
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## 40 0.8356 nan 0.5000 -0.0111
## 60 0.8003 nan 0.5000 -0.0069
## 80 0.7686 nan 0.5000 0.0024
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## 120 0.7284 nan 0.5000 -0.0006
## 140 0.7070 nan 0.5000 -0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1426 nan 0.5000 0.0569
## 2 1.0819 nan 0.5000 0.0209
## 3 1.0268 nan 0.5000 0.0269
## 4 0.9975 nan 0.5000 0.0125
## 5 0.9750 nan 0.5000 0.0056
## 6 0.9641 nan 0.5000 0.0022
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## 8 0.9287 nan 0.5000 0.0034
## 9 0.9145 nan 0.5000 -0.0021
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## 20 0.8671 nan 0.5000 -0.0046
## 40 0.8204 nan 0.5000 -0.0045
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1704 nan 0.5000 0.0654
## 2 1.1048 nan 0.5000 0.0210
## 3 1.0408 nan 0.5000 0.0258
## 4 1.0031 nan 0.5000 0.0118
## 5 0.9878 nan 0.5000 -0.0012
## 6 0.9567 nan 0.5000 0.0072
## 7 0.9462 nan 0.5000 0.0017
## 8 0.9384 nan 0.5000 -0.0014
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## 10 0.9282 nan 0.5000 0.0026
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## 40 0.8222 nan 0.5000 -0.0061
## 60 0.8003 nan 0.5000 -0.0085
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## 140 0.7170 nan 0.5000 0.0004
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## 180 0.6904 nan 0.5000 -0.0075
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1282 nan 0.5000 0.0780
## 2 1.0363 nan 0.5000 0.0438
## 3 0.9991 nan 0.5000 0.0102
## 4 0.9615 nan 0.5000 0.0038
## 5 0.9423 nan 0.5000 0.0039
## 6 0.9441 nan 0.5000 -0.0158
## 7 0.9189 nan 0.5000 0.0094
## 8 0.9161 nan 0.5000 -0.0103
## 9 0.9073 nan 0.5000 -0.0135
## 10 0.9007 nan 0.5000 -0.0039
## 20 0.8064 nan 0.5000 0.0033
## 40 0.7175 nan 0.5000 -0.0144
## 60 0.6766 nan 0.5000 -0.0044
## 80 0.6069 nan 0.5000 -0.0061
## 100 0.5411 nan 0.5000 -0.0046
## 120 2.6905 nan 0.5000 0.0000
## 140 5.6915 nan 0.5000 -0.0110
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1215 nan 0.5000 0.0597
## 2 1.0378 nan 0.5000 0.0363
## 3 0.9914 nan 0.5000 0.0188
## 4 0.9621 nan 0.5000 0.0067
## 5 0.9429 nan 0.5000 -0.0079
## 6 0.9216 nan 0.5000 -0.0011
## 7 0.9035 nan 0.5000 0.0044
## 8 0.8815 nan 0.5000 0.0023
## 9 0.8624 nan 0.5000 -0.0006
## 10 0.8529 nan 0.5000 -0.0081
## 20 0.7825 nan 0.5000 -0.0092
## 40 0.6828 nan 0.5000 -0.0082
## 60 0.6196 nan 0.5000 -0.0122
## 80 0.5522 nan 0.5000 -0.0098
## 100 0.4918 nan 0.5000 -0.0028
## 120 0.4479 nan 0.5000 -0.0060
## 140 0.4254 nan 0.5000 -0.0051
## 160 0.4250 nan 0.5000 -0.0064
## 180 0.6236 nan 0.5000 -0.0290
## 200 0.6236 nan 0.5000 -0.0101
## 220 0.5781 nan 0.5000 -0.0042
## 240 inf nan 0.5000 -0.0045
## 260 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1239 nan 0.5000 0.0821
## 2 1.0588 nan 0.5000 0.0228
## 3 0.9929 nan 0.5000 0.0244
## 4 0.9586 nan 0.5000 0.0102
## 5 0.9352 nan 0.5000 0.0023
## 6 0.9112 nan 0.5000 0.0051
## 7 0.8870 nan 0.5000 0.0021
## 8 0.8829 nan 0.5000 -0.0145
## 9 0.8704 nan 0.5000 -0.0014
## 10 0.8623 nan 0.5000 -0.0023
## 20 0.8000 nan 0.5000 0.0019
## 40 0.7235 nan 0.5000 -0.0009
## 60 0.9606 nan 0.5000 -0.0061
## 80 0.8861 nan 0.5000 -0.0018
## 100 0.8264 nan 0.5000 -0.0025
## 120 0.7717 nan 0.5000 -0.0084
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1095 nan 0.5000 0.0674
## 2 1.0086 nan 0.5000 0.0356
## 3 0.9471 nan 0.5000 0.0221
## 4 0.9246 nan 0.5000 -0.0038
## 5 0.8965 nan 0.5000 0.0002
## 6 0.8754 nan 0.5000 -0.0076
## 7 0.8657 nan 0.5000 -0.0114
## 8 0.8558 nan 0.5000 -0.0111
## 9 0.8324 nan 0.5000 0.0026
## 10 0.8115 nan 0.5000 -0.0005
## 20 0.7164 nan 0.5000 -0.0078
## 40 0.6150 nan 0.5000 -0.0127
## 60 0.5084 nan 0.5000 -0.0048
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1012 nan 0.5000 0.0843
## 2 0.9942 nan 0.5000 0.0290
## 3 0.9442 nan 0.5000 0.0042
## 4 0.9096 nan 0.5000 0.0064
## 5 0.8957 nan 0.5000 -0.0124
## 6 0.8808 nan 0.5000 -0.0089
## 7 0.8671 nan 0.5000 -0.0099
## 8 0.8492 nan 0.5000 -0.0048
## 9 0.8356 nan 0.5000 -0.0043
## 10 0.8315 nan 0.5000 -0.0148
## 20 0.7469 nan 0.5000 -0.0205
## 40 0.6148 nan 0.5000 -0.0142
## 60 0.5149 nan 0.5000 -0.0224
## 80 0.4265 nan 0.5000 -0.0072
## 100 0.3472 nan 0.5000 -0.0149
## 120 0.2781 nan 0.5000 -0.0031
## 140 0.2430 nan 0.5000 -0.0038
## 160 0.1891 nan 0.5000 -0.0002
## 180 0.1609 nan 0.5000 -0.0024
## 200 0.1370 nan 0.5000 -0.0023
## 220 0.1195 nan 0.5000 -0.0015
## 240 0.1057 nan 0.5000 -0.0023
## 260 0.0936 nan 0.5000 -0.0003
## 280 0.0805 nan 0.5000 -0.0010
## 300 0.0706 nan 0.5000 -0.0008
## 320 0.0612 nan 0.5000 -0.0005
## 340 0.0518 nan 0.5000 -0.0003
## 360 0.0446 nan 0.5000 -0.0000
## 380 0.0385 nan 0.5000 -0.0007
## 400 0.0345 nan 0.5000 -0.0004
## 420 0.0300 nan 0.5000 -0.0003
## 440 0.0270 nan 0.5000 -0.0005
## 460 0.0242 nan 0.5000 -0.0004
## 480 0.0215 nan 0.5000 -0.0003
## 500 0.0186 nan 0.5000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0914 nan 0.5000 0.0888
## 2 0.9998 nan 0.5000 0.0381
## 3 0.9396 nan 0.5000 0.0161
## 4 0.9019 nan 0.5000 0.0049
## 5 0.8716 nan 0.5000 -0.0022
## 6 0.8583 nan 0.5000 -0.0086
## 7 0.8489 nan 0.5000 -0.0110
## 8 0.8334 nan 0.5000 -0.0091
## 9 0.8167 nan 0.5000 -0.0003
## 10 0.8100 nan 0.5000 -0.0104
## 20 0.7249 nan 0.5000 0.0006
## 40 0.5850 nan 0.5000 -0.0010
## 60 0.4820 nan 0.5000 -0.0090
## 80 0.4124 nan 0.5000 -0.0076
## 100 0.3481 nan 0.5000 -0.0047
## 120 0.2969 nan 0.5000 -0.0096
## 140 0.2424 nan 0.5000 -0.0014
## 160 0.2028 nan 0.5000 -0.0011
## 180 0.1733 nan 0.5000 -0.0060
## 200 0.1440 nan 0.5000 -0.0017
## 220 0.1261 nan 0.5000 -0.0023
## 240 0.1124 nan 0.5000 -0.0014
## 260 0.0960 nan 0.5000 -0.0000
## 280 0.0846 nan 0.5000 -0.0012
## 300 0.0748 nan 0.5000 -0.0006
## 320 0.0658 nan 0.5000 -0.0005
## 340 0.0579 nan 0.5000 -0.0013
## 360 0.0502 nan 0.5000 -0.0004
## 380 0.0437 nan 0.5000 -0.0014
## 400 0.0400 nan 0.5000 -0.0012
## 420 0.0351 nan 0.5000 -0.0003
## 440 0.0323 nan 0.5000 -0.0003
## 460 0.0286 nan 0.5000 -0.0006
## 480 0.0247 nan 0.5000 -0.0003
## 500 0.0230 nan 0.5000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1213 nan 1.0000 0.0584
## 2 1.0833 nan 1.0000 -0.0141
## 3 1.0204 nan 1.0000 0.0320
## 4 1.0036 nan 1.0000 -0.0022
## 5 0.9540 nan 1.0000 0.0236
## 6 0.9460 nan 1.0000 -0.0089
## 7 0.9370 nan 1.0000 -0.0067
## 8 0.9284 nan 1.0000 -0.0065
## 9 0.9011 nan 1.0000 0.0042
## 10 0.9157 nan 1.0000 -0.0320
## 20 0.9251 nan 1.0000 -0.0124
## 40 25.7224 nan 1.0000 -0.0263
## 60 25.7587 nan 1.0000 0.0022
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1217 nan 1.0000 0.0876
## 2 1.0682 nan 1.0000 0.0040
## 3 0.9966 nan 1.0000 0.0265
## 4 0.9860 nan 1.0000 -0.0104
## 5 0.9794 nan 1.0000 -0.0100
## 6 0.9649 nan 1.0000 -0.0115
## 7 0.9706 nan 1.0000 -0.0185
## 8 0.9564 nan 1.0000 -0.0120
## 9 0.9400 nan 1.0000 0.0055
## 10 0.9468 nan 1.0000 -0.0207
## 20 0.9346 nan 1.0000 0.0036
## 40 1.1025 nan 1.0000 -0.0221
## 60 28981244.7274 nan 1.0000 -0.0220
## 80 30522697.3681 nan 1.0000 -0.0230
## 100 30522697.2868 nan 1.0000 -0.0057
## 120 30522704.0144 nan 1.0000 -0.0003
## 140 30522703.9701 nan 1.0000 -0.0132
## 160 30522704.0809 nan 1.0000 -0.1274
## 180 30522703.9786 nan 1.0000 -0.0043
## 200 30522703.9418 nan 1.0000 -0.0145
## 220 30522703.9396 nan 1.0000 -0.0017
## 240 30522703.9099 nan 1.0000 0.0042
## 260 30522703.9026 nan 1.0000 -0.0022
## 280 30522706.0835 nan 1.0000 -2.1563
## 300 1324823907878.6494 nan 1.0000 -0.0000
## 320 1324823907878.6235 nan 1.0000 -0.0006
## 340 1324823907878.6399 nan 1.0000 -0.0145
## 360 1324823907878.6636 nan 1.0000 -0.0125
## 380 1324823907878.5723 nan 1.0000 0.0043
## 400 1324823907878.0676 nan 1.0000 -0.0263
## 420 1324823907878.0110 nan 1.0000 -0.0031
## 440 1324823907877.9277 nan 1.0000 0.0024
## 460 1324823907877.8740 nan 1.0000 0.0018
## 480 1324823907877.8247 nan 1.0000 -0.0000
## 500 1324823907877.7754 nan 1.0000 0.0130
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1902 nan 1.0000 0.0254
## 2 1.0542 nan 1.0000 0.0414
## 3 1.0036 nan 1.0000 0.0252
## 4 0.9880 nan 1.0000 -0.0059
## 5 0.9849 nan 1.0000 -0.0126
## 6 0.9816 nan 1.0000 -0.0061
## 7 0.9883 nan 1.0000 -0.0299
## 8 0.9769 nan 1.0000 -0.0083
## 9 0.9670 nan 1.0000 -0.0119
## 10 0.9553 nan 1.0000 -0.0099
## 20 0.9398 nan 1.0000 -0.0471
## 40 inf nan 1.0000 -inf
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0596 nan 1.0000 0.0986
## 2 0.9825 nan 1.0000 0.0154
## 3 0.9879 nan 1.0000 -0.0380
## 4 0.9559 nan 1.0000 -0.0298
## 5 0.9344 nan 1.0000 -0.0155
## 6 0.9320 nan 1.0000 -0.0238
## 7 0.9408 nan 1.0000 -0.0325
## 8 inf nan 1.0000 -inf
## 9 inf nan 1.0000 nan
## 10 inf nan 1.0000 -0.0731
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 inf
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0640 nan 1.0000 0.1208
## 2 0.9850 nan 1.0000 0.0256
## 3 0.9420 nan 1.0000 0.0076
## 4 0.9227 nan 1.0000 -0.0061
## 5 0.9106 nan 1.0000 -0.0324
## 6 0.9096 nan 1.0000 -0.0205
## 7 0.9110 nan 1.0000 -0.0272
## 8 0.8859 nan 1.0000 -0.0143
## 9 0.9110 nan 1.0000 -0.0505
## 10 0.9267 nan 1.0000 -0.0461
## 20 1.1683 nan 1.0000 -0.0128
## 40 1.1747 nan 1.0000 -0.0142
## 60 4933.7175 nan 1.0000 -0.0011
## 80 4933.3141 nan 1.0000 -0.0270
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0820 nan 1.0000 0.0805
## 2 1.0174 nan 1.0000 0.0026
## 3 0.9759 nan 1.0000 0.0026
## 4 0.9371 nan 1.0000 0.0077
## 5 0.9499 nan 1.0000 -0.0375
## 6 0.9180 nan 1.0000 -0.0153
## 7 0.9140 nan 1.0000 -0.0226
## 8 0.9104 nan 1.0000 -0.0309
## 9 0.8839 nan 1.0000 -0.0012
## 10 0.8656 nan 1.0000 -0.0026
## 20 0.8716 nan 1.0000 0.0186
## 40 0.9061 nan 1.0000 -0.0460
## 60 0.7553 nan 1.0000 0.0035
## 80 0.8702 nan 1.0000 -0.0418
## 100 306.8049 nan 1.0000 -0.1809
## 120 307.4369 nan 1.0000 -0.0821
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0500 nan 1.0000 0.0936
## 2 0.9645 nan 1.0000 0.0311
## 3 0.9178 nan 1.0000 0.0009
## 4 0.8963 nan 1.0000 -0.0081
## 5 0.8771 nan 1.0000 -0.0319
## 6 0.8682 nan 1.0000 -0.0387
## 7 0.8650 nan 1.0000 -0.0537
## 8 0.8355 nan 1.0000 -0.0248
## 9 0.8836 nan 1.0000 -0.0918
## 10 0.9386 nan 1.0000 -0.0668
## 20 82.7065 nan 1.0000 -0.0118
## 40 82.6499 nan 1.0000 0.0051
## 60 82.0008 nan 1.0000 -0.0016
## 80 inf nan 1.0000 -0.0022
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 -0.0103
## 360 inf nan 1.0000 nan
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## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0606 nan 1.0000 0.0574
## 2 1.0185 nan 1.0000 -0.0077
## 3 1.0273 nan 1.0000 -0.0579
## 4 1.0484 nan 1.0000 -0.0594
## 5 1.0300 nan 1.0000 -0.0557
## 6 1.0018 nan 1.0000 -0.0209
## 7 1.0244 nan 1.0000 -0.0682
## 8 1.0134 nan 1.0000 -0.0303
## 9 0.9943 nan 1.0000 -0.0406
## 10 0.9882 nan 1.0000 -0.0439
## 20 1.4253 nan 1.0000 -0.0561
## 40 138600.0285 nan 1.0000 -0.2103
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0211 nan 1.0000 0.1065
## 2 0.9532 nan 1.0000 -0.0003
## 3 0.9306 nan 1.0000 -0.0287
## 4 0.9066 nan 1.0000 -0.0358
## 5 0.8934 nan 1.0000 -0.0109
## 6 0.8631 nan 1.0000 -0.0083
## 7 0.8690 nan 1.0000 -0.0399
## 8 0.8614 nan 1.0000 -0.0286
## 9 0.8961 nan 1.0000 -0.0669
## 10 0.9001 nan 1.0000 -0.0430
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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## 360 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0001
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0001
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
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## 10 1.2897 nan 0.0010 0.0002
## 20 1.2862 nan 0.0010 0.0002
## 40 1.2793 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0001
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## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
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## 10 1.2898 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
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## 10 1.2898 nan 0.0010 0.0002
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## 40 1.2795 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
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## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2916 nan 0.0010 0.0002
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## 6 1.2907 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## 3 1.2919 nan 0.0010 0.0002
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## 6 1.2905 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
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## 3 1.2917 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0002
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## 300 1.1637 nan 0.0010 0.0001
## 320 1.1569 nan 0.0010 0.0001
## 340 1.1504 nan 0.0010 0.0002
## 360 1.1441 nan 0.0010 0.0001
## 380 1.1379 nan 0.0010 0.0001
## 400 1.1319 nan 0.0010 0.0001
## 420 1.1261 nan 0.0010 0.0001
## 440 1.1206 nan 0.0010 0.0001
## 460 1.1149 nan 0.0010 0.0001
## 480 1.1093 nan 0.0010 0.0001
## 500 1.1040 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2907 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0003
## 7 1.2896 nan 0.0010 0.0002
## 8 1.2891 nan 0.0010 0.0002
## 9 1.2887 nan 0.0010 0.0002
## 10 1.2881 nan 0.0010 0.0002
## 20 1.2828 nan 0.0010 0.0002
## 40 1.2723 nan 0.0010 0.0002
## 60 1.2625 nan 0.0010 0.0002
## 80 1.2528 nan 0.0010 0.0002
## 100 1.2435 nan 0.0010 0.0002
## 120 1.2345 nan 0.0010 0.0002
## 140 1.2260 nan 0.0010 0.0002
## 160 1.2174 nan 0.0010 0.0002
## 180 1.2091 nan 0.0010 0.0002
## 200 1.2015 nan 0.0010 0.0001
## 220 1.1937 nan 0.0010 0.0002
## 240 1.1862 nan 0.0010 0.0002
## 260 1.1786 nan 0.0010 0.0002
## 280 1.1713 nan 0.0010 0.0002
## 300 1.1645 nan 0.0010 0.0001
## 320 1.1578 nan 0.0010 0.0001
## 340 1.1513 nan 0.0010 0.0001
## 360 1.1448 nan 0.0010 0.0001
## 380 1.1385 nan 0.0010 0.0001
## 400 1.1324 nan 0.0010 0.0001
## 420 1.1266 nan 0.0010 0.0001
## 440 1.1208 nan 0.0010 0.0001
## 460 1.1153 nan 0.0010 0.0001
## 480 1.1097 nan 0.0010 0.0001
## 500 1.1042 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2575 nan 0.1000 0.0168
## 2 1.2282 nan 0.1000 0.0130
## 3 1.2064 nan 0.1000 0.0088
## 4 1.1824 nan 0.1000 0.0091
## 5 1.1643 nan 0.1000 0.0088
## 6 1.1494 nan 0.1000 0.0065
## 7 1.1332 nan 0.1000 0.0071
## 8 1.1184 nan 0.1000 0.0037
## 9 1.1058 nan 0.1000 0.0059
## 10 1.0907 nan 0.1000 0.0055
## 20 1.0107 nan 0.1000 0.0018
## 40 0.9268 nan 0.1000 0.0002
## 60 0.8827 nan 0.1000 -0.0001
## 80 0.8590 nan 0.1000 -0.0007
## 100 0.8379 nan 0.1000 0.0008
## 120 0.8233 nan 0.1000 -0.0012
## 140 0.8122 nan 0.1000 -0.0019
## 160 0.8025 nan 0.1000 -0.0013
## 180 0.7941 nan 0.1000 -0.0006
## 200 0.7848 nan 0.1000 -0.0015
## 220 0.7787 nan 0.1000 -0.0013
## 240 0.7710 nan 0.1000 -0.0012
## 260 0.7648 nan 0.1000 -0.0015
## 280 0.7585 nan 0.1000 -0.0013
## 300 0.7513 nan 0.1000 -0.0011
## 320 0.7461 nan 0.1000 -0.0009
## 340 0.7408 nan 0.1000 -0.0011
## 360 0.7345 nan 0.1000 -0.0004
## 380 0.7311 nan 0.1000 -0.0001
## 400 0.7254 nan 0.1000 -0.0010
## 420 0.7210 nan 0.1000 -0.0005
## 440 0.7161 nan 0.1000 -0.0011
## 460 0.7132 nan 0.1000 -0.0011
## 480 0.7096 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2620 nan 0.1000 0.0159
## 2 1.2349 nan 0.1000 0.0132
## 3 1.2101 nan 0.1000 0.0128
## 4 1.1853 nan 0.1000 0.0083
## 5 1.1670 nan 0.1000 0.0079
## 6 1.1509 nan 0.1000 0.0074
## 7 1.1361 nan 0.1000 0.0065
## 8 1.1243 nan 0.1000 0.0054
## 9 1.1120 nan 0.1000 0.0048
## 10 1.0993 nan 0.1000 0.0048
## 20 1.0139 nan 0.1000 0.0026
## 40 0.9324 nan 0.1000 0.0001
## 60 0.8926 nan 0.1000 -0.0003
## 80 0.8618 nan 0.1000 -0.0007
## 100 0.8411 nan 0.1000 -0.0004
## 120 0.8266 nan 0.1000 -0.0004
## 140 0.8166 nan 0.1000 -0.0004
## 160 0.8050 nan 0.1000 -0.0003
## 180 0.7971 nan 0.1000 -0.0014
## 200 0.7902 nan 0.1000 -0.0010
## 220 0.7836 nan 0.1000 -0.0013
## 240 0.7765 nan 0.1000 -0.0006
## 260 0.7726 nan 0.1000 -0.0005
## 280 0.7657 nan 0.1000 -0.0016
## 300 0.7622 nan 0.1000 -0.0012
## 320 0.7566 nan 0.1000 -0.0004
## 340 0.7524 nan 0.1000 -0.0007
## 360 0.7482 nan 0.1000 -0.0011
## 380 0.7453 nan 0.1000 -0.0005
## 400 0.7409 nan 0.1000 -0.0012
## 420 0.7361 nan 0.1000 -0.0010
## 440 0.7300 nan 0.1000 -0.0009
## 460 0.7261 nan 0.1000 -0.0011
## 480 0.7222 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2616 nan 0.1000 0.0134
## 2 1.2343 nan 0.1000 0.0133
## 3 1.2051 nan 0.1000 0.0121
## 4 1.1827 nan 0.1000 0.0099
## 5 1.1616 nan 0.1000 0.0066
## 6 1.1463 nan 0.1000 0.0061
## 7 1.1339 nan 0.1000 0.0051
## 8 1.1192 nan 0.1000 0.0044
## 9 1.1062 nan 0.1000 0.0056
## 10 1.1006 nan 0.1000 -0.0002
## 20 1.0109 nan 0.1000 0.0017
## 40 0.9280 nan 0.1000 -0.0005
## 60 0.8887 nan 0.1000 -0.0001
## 80 0.8624 nan 0.1000 -0.0010
## 100 0.8439 nan 0.1000 -0.0003
## 120 0.8279 nan 0.1000 -0.0008
## 140 0.8155 nan 0.1000 -0.0008
## 160 0.8066 nan 0.1000 -0.0013
## 180 0.7995 nan 0.1000 -0.0002
## 200 0.7911 nan 0.1000 -0.0014
## 220 0.7861 nan 0.1000 -0.0002
## 240 0.7782 nan 0.1000 -0.0004
## 260 0.7719 nan 0.1000 -0.0009
## 280 0.7666 nan 0.1000 -0.0010
## 300 0.7625 nan 0.1000 -0.0006
## 320 0.7551 nan 0.1000 -0.0006
## 340 0.7511 nan 0.1000 -0.0014
## 360 0.7480 nan 0.1000 -0.0011
## 380 0.7423 nan 0.1000 -0.0011
## 400 0.7368 nan 0.1000 -0.0006
## 420 0.7319 nan 0.1000 -0.0009
## 440 0.7276 nan 0.1000 -0.0004
## 460 0.7207 nan 0.1000 -0.0012
## 480 0.7166 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2516 nan 0.1000 0.0162
## 2 1.2091 nan 0.1000 0.0192
## 3 1.1775 nan 0.1000 0.0148
## 4 1.1494 nan 0.1000 0.0104
## 5 1.1275 nan 0.1000 0.0073
## 6 1.1053 nan 0.1000 0.0092
## 7 1.0878 nan 0.1000 0.0073
## 8 1.0703 nan 0.1000 0.0067
## 9 1.0485 nan 0.1000 0.0073
## 10 1.0349 nan 0.1000 0.0043
## 20 0.9424 nan 0.1000 0.0012
## 40 0.8539 nan 0.1000 -0.0003
## 60 0.8113 nan 0.1000 -0.0022
## 80 0.7762 nan 0.1000 -0.0010
## 100 0.7507 nan 0.1000 -0.0009
## 120 0.7211 nan 0.1000 -0.0014
## 140 0.6996 nan 0.1000 -0.0011
## 160 0.6809 nan 0.1000 -0.0007
## 180 0.6604 nan 0.1000 -0.0006
## 200 0.6377 nan 0.1000 -0.0009
## 220 0.6227 nan 0.1000 -0.0007
## 240 0.6060 nan 0.1000 -0.0018
## 260 0.5911 nan 0.1000 -0.0005
## 280 0.5751 nan 0.1000 -0.0009
## 300 0.5610 nan 0.1000 -0.0016
## 320 0.5489 nan 0.1000 -0.0009
## 340 0.5340 nan 0.1000 -0.0017
## 360 0.5210 nan 0.1000 -0.0011
## 380 0.5056 nan 0.1000 -0.0008
## 400 0.4969 nan 0.1000 -0.0013
## 420 0.4851 nan 0.1000 -0.0010
## 440 0.4752 nan 0.1000 -0.0001
## 460 0.4658 nan 0.1000 -0.0003
## 480 0.4573 nan 0.1000 -0.0009
## 500 0.4462 nan 0.1000 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2463 nan 0.1000 0.0207
## 2 1.2096 nan 0.1000 0.0140
## 3 1.1819 nan 0.1000 0.0118
## 4 1.1523 nan 0.1000 0.0095
## 5 1.1307 nan 0.1000 0.0092
## 6 1.1102 nan 0.1000 0.0093
## 7 1.0865 nan 0.1000 0.0081
## 8 1.0681 nan 0.1000 0.0074
## 9 1.0523 nan 0.1000 0.0051
## 10 1.0373 nan 0.1000 0.0048
## 20 0.9373 nan 0.1000 0.0009
## 40 0.8585 nan 0.1000 -0.0006
## 60 0.8085 nan 0.1000 -0.0003
## 80 0.7679 nan 0.1000 -0.0008
## 100 0.7438 nan 0.1000 -0.0009
## 120 0.7199 nan 0.1000 -0.0010
## 140 0.6988 nan 0.1000 -0.0008
## 160 0.6759 nan 0.1000 -0.0014
## 180 0.6606 nan 0.1000 -0.0017
## 200 0.6424 nan 0.1000 -0.0011
## 220 0.6284 nan 0.1000 -0.0005
## 240 0.6125 nan 0.1000 -0.0009
## 260 0.5904 nan 0.1000 -0.0004
## 280 0.5788 nan 0.1000 -0.0012
## 300 0.5642 nan 0.1000 -0.0012
## 320 0.5474 nan 0.1000 -0.0011
## 340 0.5347 nan 0.1000 -0.0012
## 360 0.5251 nan 0.1000 -0.0014
## 380 0.5125 nan 0.1000 -0.0006
## 400 0.5006 nan 0.1000 -0.0009
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## 440 0.4798 nan 0.1000 -0.0017
## 460 0.4673 nan 0.1000 -0.0006
## 480 0.4566 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2491 nan 0.1000 0.0217
## 2 1.2137 nan 0.1000 0.0157
## 3 1.1796 nan 0.1000 0.0141
## 4 1.1542 nan 0.1000 0.0085
## 5 1.1277 nan 0.1000 0.0111
## 6 1.1047 nan 0.1000 0.0106
## 7 1.0870 nan 0.1000 0.0082
## 8 1.0682 nan 0.1000 0.0069
## 9 1.0548 nan 0.1000 0.0039
## 10 1.0371 nan 0.1000 0.0076
## 20 0.9445 nan 0.1000 0.0019
## 40 0.8650 nan 0.1000 -0.0009
## 60 0.8156 nan 0.1000 0.0004
## 80 0.7813 nan 0.1000 -0.0014
## 100 0.7495 nan 0.1000 -0.0009
## 120 0.7249 nan 0.1000 -0.0016
## 140 0.7037 nan 0.1000 -0.0011
## 160 0.6799 nan 0.1000 -0.0012
## 180 0.6632 nan 0.1000 -0.0012
## 200 0.6461 nan 0.1000 -0.0015
## 220 0.6309 nan 0.1000 -0.0012
## 240 0.6151 nan 0.1000 -0.0013
## 260 0.6002 nan 0.1000 -0.0025
## 280 0.5867 nan 0.1000 -0.0008
## 300 0.5735 nan 0.1000 -0.0004
## 320 0.5595 nan 0.1000 -0.0004
## 340 0.5475 nan 0.1000 -0.0015
## 360 0.5339 nan 0.1000 -0.0001
## 380 0.5224 nan 0.1000 -0.0016
## 400 0.5096 nan 0.1000 -0.0005
## 420 0.4977 nan 0.1000 -0.0013
## 440 0.4866 nan 0.1000 -0.0010
## 460 0.4771 nan 0.1000 -0.0009
## 480 0.4681 nan 0.1000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2405 nan 0.1000 0.0240
## 2 1.1942 nan 0.1000 0.0181
## 3 1.1563 nan 0.1000 0.0178
## 4 1.1260 nan 0.1000 0.0132
## 5 1.0987 nan 0.1000 0.0084
## 6 1.0744 nan 0.1000 0.0089
## 7 1.0498 nan 0.1000 0.0110
## 8 1.0269 nan 0.1000 0.0084
## 9 1.0121 nan 0.1000 0.0036
## 10 0.9997 nan 0.1000 0.0029
## 20 0.8941 nan 0.1000 0.0015
## 40 0.7982 nan 0.1000 -0.0023
## 60 0.7369 nan 0.1000 -0.0008
## 80 0.6900 nan 0.1000 -0.0003
## 100 0.6515 nan 0.1000 -0.0015
## 120 0.6140 nan 0.1000 -0.0009
## 140 0.5859 nan 0.1000 -0.0008
## 160 0.5553 nan 0.1000 -0.0003
## 180 0.5287 nan 0.1000 -0.0016
## 200 0.5081 nan 0.1000 -0.0008
## 220 0.4874 nan 0.1000 -0.0007
## 240 0.4660 nan 0.1000 -0.0012
## 260 0.4478 nan 0.1000 -0.0013
## 280 0.4279 nan 0.1000 -0.0004
## 300 0.4115 nan 0.1000 -0.0010
## 320 0.3954 nan 0.1000 -0.0025
## 340 0.3820 nan 0.1000 -0.0010
## 360 0.3705 nan 0.1000 -0.0013
## 380 0.3575 nan 0.1000 -0.0008
## 400 0.3443 nan 0.1000 -0.0009
## 420 0.3317 nan 0.1000 -0.0003
## 440 0.3206 nan 0.1000 -0.0004
## 460 0.3087 nan 0.1000 -0.0006
## 480 0.2994 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2451 nan 0.1000 0.0194
## 2 1.2034 nan 0.1000 0.0177
## 3 1.1687 nan 0.1000 0.0163
## 4 1.1322 nan 0.1000 0.0157
## 5 1.1025 nan 0.1000 0.0125
## 6 1.0715 nan 0.1000 0.0115
## 7 1.0488 nan 0.1000 0.0091
## 8 1.0271 nan 0.1000 0.0083
## 9 1.0123 nan 0.1000 0.0046
## 10 0.9939 nan 0.1000 0.0062
## 20 0.8947 nan 0.1000 -0.0005
## 40 0.7967 nan 0.1000 -0.0001
## 60 0.7489 nan 0.1000 -0.0036
## 80 0.7018 nan 0.1000 -0.0005
## 100 0.6652 nan 0.1000 -0.0023
## 120 0.6296 nan 0.1000 -0.0010
## 140 0.5993 nan 0.1000 -0.0031
## 160 0.5690 nan 0.1000 -0.0007
## 180 0.5380 nan 0.1000 -0.0013
## 200 0.5105 nan 0.1000 0.0002
## 220 0.4847 nan 0.1000 -0.0011
## 240 0.4622 nan 0.1000 -0.0011
## 260 0.4427 nan 0.1000 -0.0014
## 280 0.4226 nan 0.1000 -0.0007
## 300 0.4045 nan 0.1000 -0.0012
## 320 0.3872 nan 0.1000 -0.0009
## 340 0.3720 nan 0.1000 -0.0005
## 360 0.3603 nan 0.1000 -0.0006
## 380 0.3474 nan 0.1000 -0.0006
## 400 0.3335 nan 0.1000 -0.0014
## 420 0.3195 nan 0.1000 -0.0004
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## 460 0.2964 nan 0.1000 -0.0004
## 480 0.2848 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2424 nan 0.1000 0.0252
## 2 1.1986 nan 0.1000 0.0170
## 3 1.1628 nan 0.1000 0.0162
## 4 1.1331 nan 0.1000 0.0118
## 5 1.1065 nan 0.1000 0.0075
## 6 1.0824 nan 0.1000 0.0100
## 7 1.0584 nan 0.1000 0.0099
## 8 1.0399 nan 0.1000 0.0067
## 9 1.0214 nan 0.1000 0.0066
## 10 1.0098 nan 0.1000 0.0021
## 20 0.9037 nan 0.1000 0.0006
## 40 0.7989 nan 0.1000 -0.0019
## 60 0.7410 nan 0.1000 -0.0015
## 80 0.6970 nan 0.1000 -0.0012
## 100 0.6640 nan 0.1000 -0.0023
## 120 0.6323 nan 0.1000 -0.0032
## 140 0.5977 nan 0.1000 -0.0028
## 160 0.5709 nan 0.1000 -0.0014
## 180 0.5432 nan 0.1000 -0.0015
## 200 0.5158 nan 0.1000 -0.0004
## 220 0.4916 nan 0.1000 -0.0019
## 240 0.4708 nan 0.1000 -0.0006
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## 280 0.4387 nan 0.1000 -0.0002
## 300 0.4221 nan 0.1000 -0.0006
## 320 0.4043 nan 0.1000 -0.0007
## 340 0.3875 nan 0.1000 -0.0011
## 360 0.3744 nan 0.1000 -0.0015
## 380 0.3631 nan 0.1000 -0.0007
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## 460 0.3133 nan 0.1000 -0.0007
## 480 0.3031 nan 0.1000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2240 nan 0.2000 0.0286
## 2 1.1837 nan 0.2000 0.0183
## 3 1.1567 nan 0.2000 0.0125
## 4 1.1277 nan 0.2000 0.0111
## 5 1.0994 nan 0.2000 0.0119
## 6 1.0703 nan 0.2000 0.0076
## 7 1.0520 nan 0.2000 0.0063
## 8 1.0358 nan 0.2000 0.0059
## 9 1.0259 nan 0.2000 0.0004
## 10 1.0160 nan 0.2000 0.0035
## 20 0.9325 nan 0.2000 0.0011
## 40 0.8758 nan 0.2000 -0.0004
## 60 0.8326 nan 0.2000 -0.0012
## 80 0.8142 nan 0.2000 -0.0037
## 100 0.7961 nan 0.2000 -0.0005
## 120 0.7840 nan 0.2000 -0.0012
## 140 0.7717 nan 0.2000 -0.0026
## 160 0.7637 nan 0.2000 -0.0036
## 180 0.7495 nan 0.2000 -0.0022
## 200 0.7393 nan 0.2000 -0.0007
## 220 0.7312 nan 0.2000 -0.0028
## 240 0.7208 nan 0.2000 -0.0005
## 260 0.7128 nan 0.2000 -0.0022
## 280 0.7018 nan 0.2000 -0.0022
## 300 0.6955 nan 0.2000 -0.0020
## 320 0.6886 nan 0.2000 -0.0014
## 340 0.6832 nan 0.2000 -0.0021
## 360 0.6775 nan 0.2000 -0.0026
## 380 0.6673 nan 0.2000 -0.0025
## 400 0.6591 nan 0.2000 -0.0012
## 420 0.6514 nan 0.2000 -0.0031
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## 460 0.6443 nan 0.2000 -0.0019
## 480 0.6402 nan 0.2000 -0.0023
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2293 nan 0.2000 0.0300
## 2 1.1859 nan 0.2000 0.0213
## 3 1.1538 nan 0.2000 0.0157
## 4 1.1254 nan 0.2000 0.0118
## 5 1.1011 nan 0.2000 0.0103
## 6 1.0770 nan 0.2000 0.0117
## 7 1.0583 nan 0.2000 0.0059
## 8 1.0396 nan 0.2000 0.0038
## 9 1.0183 nan 0.2000 0.0045
## 10 1.0049 nan 0.2000 0.0027
## 20 0.9322 nan 0.2000 0.0028
## 40 0.8549 nan 0.2000 -0.0025
## 60 0.8256 nan 0.2000 -0.0030
## 80 0.8093 nan 0.2000 -0.0013
## 100 0.7929 nan 0.2000 -0.0017
## 120 0.7846 nan 0.2000 -0.0010
## 140 0.7678 nan 0.2000 -0.0023
## 160 0.7563 nan 0.2000 -0.0015
## 180 0.7437 nan 0.2000 -0.0006
## 200 0.7347 nan 0.2000 -0.0013
## 220 0.7291 nan 0.2000 -0.0023
## 240 0.7186 nan 0.2000 -0.0030
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## 280 0.7033 nan 0.2000 -0.0026
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## 380 0.6641 nan 0.2000 -0.0014
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## 460 0.6378 nan 0.2000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2264 nan 0.2000 0.0290
## 2 1.1815 nan 0.2000 0.0209
## 3 1.1523 nan 0.2000 0.0125
## 4 1.1236 nan 0.2000 0.0114
## 5 1.1000 nan 0.2000 0.0087
## 6 1.0757 nan 0.2000 0.0093
## 7 1.0551 nan 0.2000 0.0084
## 8 1.0436 nan 0.2000 0.0023
## 9 1.0306 nan 0.2000 0.0037
## 10 1.0141 nan 0.2000 0.0054
## 20 0.9295 nan 0.2000 0.0005
## 40 0.8633 nan 0.2000 -0.0043
## 60 0.8218 nan 0.2000 -0.0019
## 80 0.8003 nan 0.2000 -0.0011
## 100 0.7854 nan 0.2000 -0.0023
## 120 0.7741 nan 0.2000 -0.0025
## 140 0.7628 nan 0.2000 -0.0009
## 160 0.7515 nan 0.2000 -0.0010
## 180 0.7389 nan 0.2000 -0.0014
## 200 0.7312 nan 0.2000 -0.0033
## 220 0.7173 nan 0.2000 -0.0033
## 240 0.7156 nan 0.2000 -0.0036
## 260 0.7050 nan 0.2000 -0.0005
## 280 0.6993 nan 0.2000 -0.0026
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## 340 0.6770 nan 0.2000 -0.0031
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2122 nan 0.2000 0.0424
## 2 1.1424 nan 0.2000 0.0292
## 3 1.0987 nan 0.2000 0.0162
## 4 1.0609 nan 0.2000 0.0155
## 5 1.0359 nan 0.2000 0.0069
## 6 1.0127 nan 0.2000 0.0071
## 7 0.9872 nan 0.2000 0.0056
## 8 0.9730 nan 0.2000 0.0040
## 9 0.9577 nan 0.2000 0.0045
## 10 0.9386 nan 0.2000 0.0057
## 20 0.8572 nan 0.2000 -0.0015
## 40 0.7791 nan 0.2000 -0.0012
## 60 0.7256 nan 0.2000 0.0003
## 80 0.6898 nan 0.2000 -0.0049
## 100 0.6600 nan 0.2000 -0.0017
## 120 0.6301 nan 0.2000 -0.0015
## 140 0.6030 nan 0.2000 -0.0017
## 160 0.5940 nan 0.2000 -0.0039
## 180 0.5493 nan 0.2000 -0.0037
## 200 0.5467 nan 0.2000 -0.0027
## 220 0.5161 nan 0.2000 -0.0025
## 240 0.4927 nan 0.2000 -0.0040
## 260 0.4694 nan 0.2000 -0.0016
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## 320 0.4046 nan 0.2000 -0.0008
## 340 0.3880 nan 0.2000 -0.0022
## 360 0.3756 nan 0.2000 -0.0010
## 380 249.6284 nan 0.2000 -0.0011
## 400 249.6181 nan 0.2000 0.0000
## 420 249.2166 nan 0.2000 -0.0018
## 440 248.9391 nan 0.2000 -0.0000
## 460 248.9310 nan 0.2000 -0.0033
## 480 248.9249 nan 0.2000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2122 nan 0.2000 0.0388
## 2 1.1539 nan 0.2000 0.0278
## 3 1.1043 nan 0.2000 0.0201
## 4 1.0682 nan 0.2000 0.0107
## 5 1.0392 nan 0.2000 0.0089
## 6 1.0156 nan 0.2000 0.0082
## 7 0.9910 nan 0.2000 0.0125
## 8 0.9732 nan 0.2000 0.0048
## 9 0.9489 nan 0.2000 0.0092
## 10 0.9385 nan 0.2000 0.0030
## 20 0.8622 nan 0.2000 -0.0058
## 40 0.7754 nan 0.2000 -0.0017
## 60 0.7273 nan 0.2000 -0.0023
## 80 0.6819 nan 0.2000 -0.0010
## 100 0.6457 nan 0.2000 -0.0014
## 120 0.6136 nan 0.2000 -0.0003
## 140 0.5804 nan 0.2000 -0.0023
## 160 0.5508 nan 0.2000 -0.0031
## 180 0.5279 nan 0.2000 -0.0015
## 200 0.5017 nan 0.2000 -0.0033
## 220 0.4767 nan 0.2000 -0.0024
## 240 0.4619 nan 0.2000 -0.0016
## 260 0.4393 nan 0.2000 -0.0001
## 280 0.4229 nan 0.2000 -0.0020
## 300 0.4081 nan 0.2000 -0.0042
## 320 0.3865 nan 0.2000 -0.0015
## 340 0.3731 nan 0.2000 -0.0017
## 360 0.3591 nan 0.2000 -0.0022
## 380 0.3445 nan 0.2000 -0.0024
## 400 0.3317 nan 0.2000 -0.0006
## 420 0.3172 nan 0.2000 -0.0010
## 440 0.3041 nan 0.2000 -0.0013
## 460 0.2950 nan 0.2000 -0.0023
## 480 0.2829 nan 0.2000 -0.0014
## 500 0.2721 nan 0.2000 -0.0022
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2064 nan 0.2000 0.0347
## 2 1.1466 nan 0.2000 0.0218
## 3 1.1007 nan 0.2000 0.0189
## 4 1.0653 nan 0.2000 0.0165
## 5 1.0349 nan 0.2000 0.0107
## 6 1.0135 nan 0.2000 0.0083
## 7 0.9953 nan 0.2000 0.0024
## 8 0.9766 nan 0.2000 0.0036
## 9 0.9654 nan 0.2000 -0.0020
## 10 0.9499 nan 0.2000 0.0051
## 20 0.8601 nan 0.2000 -0.0021
## 40 0.7897 nan 0.2000 -0.0046
## 60 0.7483 nan 0.2000 -0.0032
## 80 0.7040 nan 0.2000 -0.0028
## 100 0.6621 nan 0.2000 -0.0009
## 120 0.6352 nan 0.2000 -0.0042
## 140 0.6105 nan 0.2000 -0.0023
## 160 0.5836 nan 0.2000 -0.0037
## 180 0.5598 nan 0.2000 -0.0012
## 200 0.5337 nan 0.2000 -0.0010
## 220 0.5070 nan 0.2000 -0.0023
## 240 0.4828 nan 0.2000 -0.0013
## 260 0.4587 nan 0.2000 -0.0007
## 280 0.4406 nan 0.2000 -0.0021
## 300 0.4178 nan 0.2000 -0.0009
## 320 0.4041 nan 0.2000 -0.0021
## 340 0.3906 nan 0.2000 -0.0011
## 360 0.3759 nan 0.2000 -0.0007
## 380 0.3647 nan 0.2000 -0.0018
## 400 0.3542 nan 0.2000 -0.0012
## 420 0.3348 nan 0.2000 -0.0004
## 440 0.3219 nan 0.2000 -0.0010
## 460 0.3100 nan 0.2000 -0.0019
## 480 0.3006 nan 0.2000 -0.0012
## 500 0.2883 nan 0.2000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1896 nan 0.2000 0.0468
## 2 1.1206 nan 0.2000 0.0295
## 3 1.0628 nan 0.2000 0.0218
## 4 1.0220 nan 0.2000 0.0096
## 5 0.9901 nan 0.2000 0.0075
## 6 0.9613 nan 0.2000 0.0109
## 7 0.9413 nan 0.2000 0.0062
## 8 0.9255 nan 0.2000 0.0008
## 9 0.9091 nan 0.2000 0.0026
## 10 0.8931 nan 0.2000 0.0035
## 20 0.8059 nan 0.2000 -0.0037
## 40 0.7206 nan 0.2000 -0.0027
## 60 0.6331 nan 0.2000 -0.0031
## 80 0.5665 nan 0.2000 -0.0055
## 100 0.5185 nan 0.2000 -0.0059
## 120 0.4743 nan 0.2000 -0.0031
## 140 0.4321 nan 0.2000 -0.0026
## 160 0.4001 nan 0.2000 -0.0041
## 180 0.3710 nan 0.2000 -0.0021
## 200 0.3404 nan 0.2000 -0.0004
## 220 0.3158 nan 0.2000 -0.0009
## 240 0.2929 nan 0.2000 -0.0019
## 260 0.2709 nan 0.2000 -0.0005
## 280 0.2529 nan 0.2000 -0.0016
## 300 0.2387 nan 0.2000 -0.0006
## 320 0.2231 nan 0.2000 -0.0025
## 340 0.2123 nan 0.2000 -0.0005
## 360 0.1985 nan 0.2000 -0.0013
## 380 0.1866 nan 0.2000 -0.0004
## 400 0.1766 nan 0.2000 -0.0005
## 420 0.1672 nan 0.2000 -0.0009
## 440 0.1561 nan 0.2000 -0.0006
## 460 0.1478 nan 0.2000 -0.0011
## 480 0.1385 nan 0.2000 -0.0001
## 500 0.1291 nan 0.2000 -0.0010
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2074 nan 0.2000 0.0410
## 2 1.1258 nan 0.2000 0.0305
## 3 1.0630 nan 0.2000 0.0247
## 4 1.0216 nan 0.2000 0.0155
## 5 0.9924 nan 0.2000 0.0045
## 6 0.9618 nan 0.2000 0.0102
## 7 0.9390 nan 0.2000 0.0048
## 8 0.9222 nan 0.2000 0.0059
## 9 0.9065 nan 0.2000 0.0009
## 10 0.8958 nan 0.2000 -0.0020
## 20 0.7979 nan 0.2000 -0.0027
## 40 0.7134 nan 0.2000 -0.0048
## 60 0.6486 nan 0.2000 -0.0029
## 80 0.5991 nan 0.2000 -0.0021
## 100 0.5428 nan 0.2000 -0.0014
## 120 0.4898 nan 0.2000 -0.0030
## 140 0.4467 nan 0.2000 -0.0019
## 160 0.4094 nan 0.2000 -0.0025
## 180 0.3758 nan 0.2000 -0.0015
## 200 0.3448 nan 0.2000 -0.0008
## 220 0.3163 nan 0.2000 -0.0005
## 240 0.2922 nan 0.2000 -0.0013
## 260 0.2762 nan 0.2000 -0.0012
## 280 0.2598 nan 0.2000 -0.0011
## 300 0.2454 nan 0.2000 -0.0008
## 320 0.2290 nan 0.2000 -0.0011
## 340 0.2140 nan 0.2000 -0.0017
## 360 0.2007 nan 0.2000 -0.0009
## 380 0.1869 nan 0.2000 -0.0010
## 400 0.1745 nan 0.2000 -0.0014
## 420 0.1648 nan 0.2000 -0.0015
## 440 0.1555 nan 0.2000 -0.0008
## 460 0.1460 nan 0.2000 -0.0009
## 480 0.1340 nan 0.2000 -0.0012
## 500 0.1264 nan 0.2000 -0.0010
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2050 nan 0.2000 0.0351
## 2 1.1348 nan 0.2000 0.0286
## 3 1.0737 nan 0.2000 0.0254
## 4 1.0302 nan 0.2000 0.0179
## 5 0.9946 nan 0.2000 0.0155
## 6 0.9601 nan 0.2000 0.0111
## 7 0.9404 nan 0.2000 0.0033
## 8 0.9209 nan 0.2000 0.0044
## 9 0.9026 nan 0.2000 0.0020
## 10 0.8894 nan 0.2000 0.0025
## 20 0.8010 nan 0.2000 -0.0033
## 40 0.7091 nan 0.2000 -0.0046
## 60 0.6381 nan 0.2000 -0.0030
## 80 0.5778 nan 0.2000 -0.0021
## 100 0.5258 nan 0.2000 -0.0028
## 120 0.4807 nan 0.2000 -0.0025
## 140 0.4432 nan 0.2000 -0.0025
## 160 0.4164 nan 0.2000 -0.0031
## 180 0.3869 nan 0.2000 -0.0014
## 200 0.3525 nan 0.2000 -0.0017
## 220 0.3305 nan 0.2000 -0.0029
## 240 0.3058 nan 0.2000 -0.0013
## 260 0.2840 nan 0.2000 -0.0015
## 280 0.2634 nan 0.2000 -0.0023
## 300 0.2458 nan 0.2000 -0.0010
## 320 0.2262 nan 0.2000 -0.0004
## 340 0.2104 nan 0.2000 -0.0006
## 360 0.1995 nan 0.2000 -0.0015
## 380 0.1874 nan 0.2000 -0.0017
## 400 0.1736 nan 0.2000 -0.0008
## 420 0.1643 nan 0.2000 -0.0005
## 440 0.1556 nan 0.2000 -0.0005
## 460 0.1455 nan 0.2000 -0.0002
## 480 0.1370 nan 0.2000 -0.0008
## 500 0.1288 nan 0.2000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2061 nan 0.3000 0.0438
## 2 1.1591 nan 0.3000 0.0175
## 3 1.1256 nan 0.3000 0.0140
## 4 1.0875 nan 0.3000 0.0163
## 5 1.0634 nan 0.3000 0.0098
## 6 1.0385 nan 0.3000 0.0065
## 7 1.0169 nan 0.3000 0.0075
## 8 0.9937 nan 0.3000 0.0076
## 9 0.9790 nan 0.3000 0.0049
## 10 0.9659 nan 0.3000 0.0059
## 20 0.9056 nan 0.3000 -0.0027
## 40 0.8374 nan 0.3000 -0.0020
## 60 0.8160 nan 0.3000 -0.0013
## 80 0.7894 nan 0.3000 -0.0043
## 100 0.7773 nan 0.3000 -0.0032
## 120 0.7553 nan 0.3000 -0.0017
## 140 0.7379 nan 0.3000 -0.0023
## 160 0.7251 nan 0.3000 -0.0031
## 180 0.7034 nan 0.3000 -0.0018
## 200 0.6993 nan 0.3000 -0.0035
## 220 0.6860 nan 0.3000 -0.0041
## 240 0.6725 nan 0.3000 -0.0000
## 260 0.6614 nan 0.3000 -0.0016
## 280 0.6537 nan 0.3000 -0.0009
## 300 0.6473 nan 0.3000 -0.0020
## 320 0.6351 nan 0.3000 -0.0015
## 340 0.6229 nan 0.3000 -0.0029
## 360 0.6148 nan 0.3000 -0.0029
## 380 0.6046 nan 0.3000 0.0003
## 400 0.5969 nan 0.3000 -0.0034
## 420 0.5896 nan 0.3000 -0.0017
## 440 0.5841 nan 0.3000 -0.0020
## 460 0.5813 nan 0.3000 -0.0040
## 480 0.5722 nan 0.3000 -0.0011
## 500 0.5715 nan 0.3000 -0.0033
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2186 nan 0.3000 0.0378
## 2 1.1578 nan 0.3000 0.0279
## 3 1.1168 nan 0.3000 0.0130
## 4 1.0864 nan 0.3000 0.0116
## 5 1.0528 nan 0.3000 0.0066
## 6 1.0204 nan 0.3000 0.0118
## 7 1.0074 nan 0.3000 0.0036
## 8 0.9918 nan 0.3000 0.0026
## 9 0.9764 nan 0.3000 -0.0015
## 10 0.9552 nan 0.3000 0.0058
## 20 0.8746 nan 0.3000 0.0003
## 40 0.8253 nan 0.3000 -0.0025
## 60 0.8020 nan 0.3000 -0.0034
## 80 0.7862 nan 0.3000 -0.0011
## 100 0.7686 nan 0.3000 -0.0014
## 120 0.7564 nan 0.3000 -0.0020
## 140 0.7381 nan 0.3000 -0.0042
## 160 0.7215 nan 0.3000 -0.0023
## 180 0.7130 nan 0.3000 -0.0021
## 200 0.7028 nan 0.3000 -0.0039
## 220 0.6886 nan 0.3000 -0.0020
## 240 0.6866 nan 0.3000 -0.0017
## 260 0.6668 nan 0.3000 -0.0011
## 280 0.6575 nan 0.3000 -0.0027
## 300 0.6460 nan 0.3000 -0.0000
## 320 0.6400 nan 0.3000 -0.0035
## 340 0.6291 nan 0.3000 -0.0023
## 360 0.6225 nan 0.3000 -0.0040
## 380 0.6142 nan 0.3000 -0.0024
## 400 0.6094 nan 0.3000 -0.0024
## 420 0.6034 nan 0.3000 -0.0053
## 440 0.5950 nan 0.3000 -0.0034
## 460 0.5885 nan 0.3000 -0.0036
## 480 0.5744 nan 0.3000 -0.0024
## 500 0.5695 nan 0.3000 -0.0021
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2152 nan 0.3000 0.0407
## 2 1.1526 nan 0.3000 0.0260
## 3 1.1120 nan 0.3000 0.0180
## 4 1.0753 nan 0.3000 0.0166
## 5 1.0501 nan 0.3000 0.0076
## 6 1.0250 nan 0.3000 0.0080
## 7 1.0180 nan 0.3000 -0.0018
## 8 0.9974 nan 0.3000 0.0068
## 9 0.9753 nan 0.3000 0.0060
## 10 0.9618 nan 0.3000 0.0021
## 20 0.9014 nan 0.3000 -0.0014
## 40 0.8438 nan 0.3000 -0.0055
## 60 0.8152 nan 0.3000 -0.0031
## 80 0.7957 nan 0.3000 -0.0053
## 100 0.7772 nan 0.3000 -0.0048
## 120 0.7610 nan 0.3000 -0.0072
## 140 0.7465 nan 0.3000 -0.0029
## 160 0.7279 nan 0.3000 0.0001
## 180 0.7108 nan 0.3000 -0.0003
## 200 0.7059 nan 0.3000 -0.0030
## 220 0.6938 nan 0.3000 -0.0028
## 240 0.6813 nan 0.3000 -0.0015
## 260 0.6732 nan 0.3000 -0.0026
## 280 0.6641 nan 0.3000 0.0005
## 300 0.6540 nan 0.3000 -0.0013
## 320 0.6499 nan 0.3000 -0.0041
## 340 0.6440 nan 0.3000 -0.0020
## 360 0.6355 nan 0.3000 -0.0012
## 380 0.6274 nan 0.3000 -0.0023
## 400 0.6202 nan 0.3000 -0.0010
## 420 0.6164 nan 0.3000 -0.0009
## 440 0.6109 nan 0.3000 -0.0024
## 460 0.6023 nan 0.3000 -0.0016
## 480 0.5929 nan 0.3000 -0.0005
## 500 0.5835 nan 0.3000 -0.0023
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1772 nan 0.3000 0.0386
## 2 1.0950 nan 0.3000 0.0331
## 3 1.0496 nan 0.3000 0.0107
## 4 1.0008 nan 0.3000 0.0125
## 5 0.9718 nan 0.3000 0.0035
## 6 0.9408 nan 0.3000 0.0040
## 7 0.9197 nan 0.3000 0.0079
## 8 0.9059 nan 0.3000 0.0007
## 9 0.8919 nan 0.3000 0.0027
## 10 0.8804 nan 0.3000 0.0030
## 20 0.8143 nan 0.3000 -0.0002
## 40 0.7330 nan 0.3000 -0.0012
## 60 0.6718 nan 0.3000 -0.0057
## 80 0.6233 nan 0.3000 -0.0040
## 100 0.5866 nan 0.3000 -0.0101
## 120 0.5493 nan 0.3000 -0.0031
## 140 0.5161 nan 0.3000 -0.0027
## 160 0.4836 nan 0.3000 -0.0003
## 180 0.4578 nan 0.3000 -0.0074
## 200 0.4283 nan 0.3000 -0.0045
## 220 0.4023 nan 0.3000 -0.0018
## 240 0.3838 nan 0.3000 -0.0032
## 260 inf nan 0.3000 -0.3685
## 280 inf nan 0.3000 -0.0021
## 300 inf nan 0.3000 nan
## 320 inf nan 0.3000 -0.0046
## 340 inf nan 0.3000 nan
## 360 inf nan 0.3000 nan
## 380 inf nan 0.3000 nan
## 400 inf nan 0.3000 nan
## 420 inf nan 0.3000 nan
## 440 inf nan 0.3000 nan
## 460 inf nan 0.3000 nan
## 480 inf nan 0.3000 nan
## 500 inf nan 0.3000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1681 nan 0.3000 0.0547
## 2 1.1068 nan 0.3000 0.0205
## 3 1.0536 nan 0.3000 0.0246
## 4 1.0062 nan 0.3000 0.0186
## 5 0.9738 nan 0.3000 0.0123
## 6 0.9520 nan 0.3000 0.0064
## 7 0.9351 nan 0.3000 -0.0001
## 8 0.9097 nan 0.3000 0.0062
## 9 0.8901 nan 0.3000 0.0051
## 10 0.8784 nan 0.3000 0.0014
## 20 0.8116 nan 0.3000 -0.0047
## 40 0.7338 nan 0.3000 -0.0034
## 60 0.6709 nan 0.3000 -0.0024
## 80 0.6116 nan 0.3000 -0.0018
## 100 0.5634 nan 0.3000 -0.0043
## 120 0.5224 nan 0.3000 -0.0032
## 140 0.5004 nan 0.3000 0.0020
## 160 0.4602 nan 0.3000 -0.0055
## 180 0.4311 nan 0.3000 -0.0025
## 200 0.4063 nan 0.3000 -0.0014
## 220 0.3743 nan 0.3000 -0.0026
## 240 0.3458 nan 0.3000 -0.0031
## 260 0.3189 nan 0.3000 -0.0009
## 280 0.3022 nan 0.3000 -0.0036
## 300 0.2869 nan 0.3000 -0.0010
## 320 0.2646 nan 0.3000 -0.0013
## 340 0.2477 nan 0.3000 -0.0006
## 360 0.2400 nan 0.3000 -0.0015
## 380 0.2251 nan 0.3000 -0.0021
## 400 0.2147 nan 0.3000 -0.0023
## 420 0.2039 nan 0.3000 -0.0030
## 440 0.1907 nan 0.3000 -0.0026
## 460 0.1811 nan 0.3000 -0.0007
## 480 0.1727 nan 0.3000 -0.0017
## 500 0.1642 nan 0.3000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1675 nan 0.3000 0.0512
## 2 1.0930 nan 0.3000 0.0382
## 3 1.0470 nan 0.3000 0.0161
## 4 1.0107 nan 0.3000 0.0128
## 5 0.9646 nan 0.3000 0.0065
## 6 0.9445 nan 0.3000 0.0053
## 7 0.9284 nan 0.3000 0.0030
## 8 0.9119 nan 0.3000 0.0009
## 9 0.9008 nan 0.3000 -0.0030
## 10 0.8907 nan 0.3000 0.0004
## 20 0.8361 nan 0.3000 -0.0038
## 40 0.7397 nan 0.3000 -0.0033
## 60 0.6804 nan 0.3000 -0.0035
## 80 0.6342 nan 0.3000 -0.0028
## 100 0.5910 nan 0.3000 -0.0018
## 120 0.5503 nan 0.3000 -0.0014
## 140 0.5113 nan 0.3000 -0.0038
## 160 0.4766 nan 0.3000 -0.0045
## 180 0.4404 nan 0.3000 -0.0022
## 200 0.4064 nan 0.3000 -0.0012
## 220 0.3825 nan 0.3000 -0.0029
## 240 0.3674 nan 0.3000 -0.0027
## 260 0.3459 nan 0.3000 -0.0014
## 280 0.3237 nan 0.3000 -0.0033
## 300 0.3037 nan 0.3000 -0.0017
## 320 0.2877 nan 0.3000 -0.0038
## 340 0.2736 nan 0.3000 -0.0016
## 360 0.2602 nan 0.3000 -0.0012
## 380 0.2438 nan 0.3000 -0.0019
## 400 0.2342 nan 0.3000 -0.0024
## 420 0.2202 nan 0.3000 -0.0011
## 440 0.2092 nan 0.3000 -0.0019
## 460 0.1935 nan 0.3000 -0.0005
## 480 0.1864 nan 0.3000 -0.0007
## 500 0.1806 nan 0.3000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1546 nan 0.3000 0.0595
## 2 1.0731 nan 0.3000 0.0339
## 3 1.0198 nan 0.3000 0.0125
## 4 0.9777 nan 0.3000 0.0126
## 5 0.9426 nan 0.3000 0.0059
## 6 0.9283 nan 0.3000 -0.0057
## 7 0.9086 nan 0.3000 0.0027
## 8 0.8855 nan 0.3000 0.0034
## 9 0.8739 nan 0.3000 -0.0034
## 10 0.8641 nan 0.3000 -0.0014
## 20 0.8008 nan 0.3000 -0.0082
## 40 0.7021 nan 0.3000 -0.0103
## 60 0.6036 nan 0.3000 -0.0028
## 80 0.5234 nan 0.3000 -0.0025
## 100 0.4544 nan 0.3000 0.0000
## 120 0.4040 nan 0.3000 -0.0084
## 140 0.3472 nan 0.3000 -0.0015
## 160 0.3172 nan 0.3000 -0.0026
## 180 0.2789 nan 0.3000 -0.0035
## 200 0.2511 nan 0.3000 -0.0010
## 220 0.2285 nan 0.3000 -0.0052
## 240 0.2085 nan 0.3000 -0.0007
## 260 0.1906 nan 0.3000 -0.0004
## 280 0.1773 nan 0.3000 -0.0023
## 300 0.1587 nan 0.3000 -0.0002
## 320 0.1494 nan 0.3000 -0.0021
## 340 0.1362 nan 0.3000 -0.0009
## 360 0.1227 nan 0.3000 -0.0003
## 380 0.1141 nan 0.3000 -0.0011
## 400 0.1031 nan 0.3000 -0.0009
## 420 0.0931 nan 0.3000 -0.0007
## 440 0.0864 nan 0.3000 -0.0014
## 460 0.0795 nan 0.3000 -0.0006
## 480 0.0733 nan 0.3000 -0.0004
## 500 0.0679 nan 0.3000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1628 nan 0.3000 0.0601
## 2 1.0931 nan 0.3000 0.0229
## 3 1.0317 nan 0.3000 0.0201
## 4 0.9826 nan 0.3000 0.0099
## 5 0.9522 nan 0.3000 0.0041
## 6 0.9303 nan 0.3000 0.0011
## 7 0.9114 nan 0.3000 0.0020
## 8 0.8977 nan 0.3000 0.0011
## 9 0.8883 nan 0.3000 -0.0059
## 10 0.8691 nan 0.3000 0.0039
## 20 0.7863 nan 0.3000 -0.0030
## 40 0.6657 nan 0.3000 -0.0031
## 60 0.5902 nan 0.3000 -0.0066
## 80 0.5167 nan 0.3000 -0.0027
## 100 0.4681 nan 0.3000 -0.0052
## 120 0.4139 nan 0.3000 -0.0019
## 140 0.3687 nan 0.3000 -0.0077
## 160 0.3289 nan 0.3000 -0.0035
## 180 0.2938 nan 0.3000 -0.0013
## 200 0.2678 nan 0.3000 -0.0025
## 220 0.2352 nan 0.3000 -0.0015
## 240 0.2125 nan 0.3000 -0.0020
## 260 0.1871 nan 0.3000 -0.0016
## 280 0.1738 nan 0.3000 -0.0001
## 300 0.1536 nan 0.3000 0.0000
## 320 0.1417 nan 0.3000 -0.0001
## 340 0.1300 nan 0.3000 -0.0005
## 360 0.1165 nan 0.3000 -0.0014
## 380 0.1071 nan 0.3000 -0.0007
## 400 0.0966 nan 0.3000 -0.0006
## 420 0.0896 nan 0.3000 -0.0002
## 440 0.0836 nan 0.3000 -0.0009
## 460 0.0761 nan 0.3000 -0.0007
## 480 0.0706 nan 0.3000 -0.0005
## 500 0.0649 nan 0.3000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1702 nan 0.3000 0.0536
## 2 1.0699 nan 0.3000 0.0396
## 3 1.0049 nan 0.3000 0.0248
## 4 0.9619 nan 0.3000 0.0111
## 5 0.9291 nan 0.3000 0.0066
## 6 0.9037 nan 0.3000 0.0039
## 7 0.8884 nan 0.3000 -0.0011
## 8 0.8734 nan 0.3000 -0.0009
## 9 0.8587 nan 0.3000 -0.0039
## 10 0.8415 nan 0.3000 0.0027
## 20 0.7607 nan 0.3000 -0.0035
## 40 0.6471 nan 0.3000 -0.0024
## 60 0.5628 nan 0.3000 -0.0033
## 80 0.4951 nan 0.3000 -0.0021
## 100 0.4378 nan 0.3000 -0.0026
## 120 0.3950 nan 0.3000 -0.0049
## 140 0.3598 nan 0.3000 -0.0031
## 160 0.3166 nan 0.3000 -0.0018
## 180 0.2904 nan 0.3000 -0.0016
## 200 0.2616 nan 0.3000 -0.0007
## 220 0.2366 nan 0.3000 -0.0016
## 240 0.2099 nan 0.3000 -0.0006
## 260 0.1891 nan 0.3000 -0.0000
## 280 0.1695 nan 0.3000 -0.0011
## 300 0.1534 nan 0.3000 -0.0010
## 320 0.1408 nan 0.3000 -0.0013
## 340 0.1292 nan 0.3000 -0.0010
## 360 0.1204 nan 0.3000 -0.0005
## 380 0.1112 nan 0.3000 -0.0012
## 400 0.1028 nan 0.3000 -0.0008
## 420 0.0950 nan 0.3000 -0.0012
## 440 0.0849 nan 0.3000 -0.0004
## 460 0.0791 nan 0.3000 -0.0002
## 480 0.0720 nan 0.3000 -0.0004
## 500 0.0677 nan 0.3000 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1553 nan 0.5000 0.0517
## 2 1.0976 nan 0.5000 0.0218
## 3 1.0609 nan 0.5000 0.0195
## 4 1.0156 nan 0.5000 0.0178
## 5 0.9946 nan 0.5000 0.0055
## 6 0.9898 nan 0.5000 -0.0028
## 7 0.9774 nan 0.5000 0.0021
## 8 0.9583 nan 0.5000 0.0067
## 9 0.9421 nan 0.5000 0.0002
## 10 0.9314 nan 0.5000 0.0007
## 20 0.8788 nan 0.5000 -0.0039
## 40 2.1554 nan 0.5000 -0.0020
## 60 inf nan 0.5000 -inf
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 -0.0006
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
## 500 inf nan 0.5000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1663 nan 0.5000 0.0646
## 2 1.1049 nan 0.5000 0.0210
## 3 1.0535 nan 0.5000 0.0169
## 4 1.0079 nan 0.5000 0.0174
## 5 0.9926 nan 0.5000 0.0019
## 6 0.9736 nan 0.5000 0.0011
## 7 0.9400 nan 0.5000 0.0114
## 8 0.9282 nan 0.5000 -0.0012
## 9 0.9097 nan 0.5000 0.0033
## 10 0.8999 nan 0.5000 -0.0028
## 20 0.8501 nan 0.5000 0.0006
## 40 0.7852 nan 0.5000 0.0013
## 60 0.7548 nan 0.5000 -0.0053
## 80 0.7466 nan 0.5000 -0.0095
## 100 0.7250 nan 0.5000 0.0016
## 120 0.7074 nan 0.5000 -0.0027
## 140 0.6896 nan 0.5000 -0.0058
## 160 0.6665 nan 0.5000 -0.0029
## 180 0.6515 nan 0.5000 -0.0013
## 200 0.6377 nan 0.5000 -0.0028
## 220 0.6381 nan 0.5000 -0.0041
## 240 0.6224 nan 0.5000 -0.0036
## 260 0.6115 nan 0.5000 -0.0050
## 280 0.5918 nan 0.5000 -0.0033
## 300 0.5779 nan 0.5000 -0.0089
## 320 0.5673 nan 0.5000 -0.0047
## 340 0.5632 nan 0.5000 -0.0059
## 360 0.5565 nan 0.5000 -0.0071
## 380 0.5500 nan 0.5000 -0.0051
## 400 0.5332 nan 0.5000 -0.0056
## 420 0.5228 nan 0.5000 -0.0059
## 440 0.5165 nan 0.5000 -0.0073
## 460 0.5170 nan 0.5000 -0.0067
## 480 0.5102 nan 0.5000 -0.0049
## 500 0.4998 nan 0.5000 -0.0063
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1561 nan 0.5000 0.0624
## 2 1.0953 nan 0.5000 0.0199
## 3 1.0491 nan 0.5000 0.0191
## 4 1.0040 nan 0.5000 0.0201
## 5 0.9874 nan 0.5000 0.0035
## 6 0.9630 nan 0.5000 0.0080
## 7 0.9496 nan 0.5000 0.0056
## 8 0.9345 nan 0.5000 0.0044
## 9 0.9375 nan 0.5000 -0.0130
## 10 0.9317 nan 0.5000 -0.0073
## 20 0.8803 nan 0.5000 -0.0123
## 40 0.8241 nan 0.5000 -0.0094
## 60 0.7944 nan 0.5000 -0.0043
## 80 0.7590 nan 0.5000 -0.0058
## 100 0.7434 nan 0.5000 -0.0059
## 120 0.7153 nan 0.5000 -0.0010
## 140 0.7004 nan 0.5000 -0.0092
## 160 0.6776 nan 0.5000 -0.0054
## 180 0.6735 nan 0.5000 -0.0038
## 200 0.6517 nan 0.5000 0.0031
## 220 0.6449 nan 0.5000 -0.0108
## 240 0.6263 nan 0.5000 -0.0002
## 260 0.6199 nan 0.5000 -0.0032
## 280 0.6122 nan 0.5000 -0.0050
## 300 0.5929 nan 0.5000 -0.0060
## 320 0.5806 nan 0.5000 -0.0054
## 340 0.5688 nan 0.5000 -0.0017
## 360 0.5711 nan 0.5000 -0.0132
## 380 0.5681 nan 0.5000 -0.0039
## 400 0.5475 nan 0.5000 -0.0026
## 420 0.5375 nan 0.5000 -0.0040
## 440 0.5256 nan 0.5000 0.0012
## 460 0.5156 nan 0.5000 0.0008
## 480 0.5089 nan 0.5000 -0.0045
## 500 0.4951 nan 0.5000 -0.0050
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1081 nan 0.5000 0.0839
## 2 1.0281 nan 0.5000 0.0311
## 3 0.9734 nan 0.5000 0.0148
## 4 0.9368 nan 0.5000 0.0116
## 5 0.9024 nan 0.5000 0.0066
## 6 0.8926 nan 0.5000 -0.0072
## 7 0.8841 nan 0.5000 -0.0084
## 8 0.8656 nan 0.5000 0.0042
## 9 0.8567 nan 0.5000 -0.0038
## 10 0.8452 nan 0.5000 -0.0046
## 20 0.7997 nan 0.5000 -0.0122
## 40 0.6660 nan 0.5000 -0.0064
## 60 0.6027 nan 0.5000 -0.0124
## 80 0.5380 nan 0.5000 -0.0114
## 100 0.4833 nan 0.5000 -0.0130
## 120 0.4329 nan 0.5000 -0.0071
## 140 0.3935 nan 0.5000 -0.0046
## 160 0.3385 nan 0.5000 -0.0026
## 180 0.3083 nan 0.5000 -0.0027
## 200 0.2904 nan 0.5000 -0.0012
## 220 0.2553 nan 0.5000 -0.0025
## 240 0.2314 nan 0.5000 -0.0055
## 260 0.2069 nan 0.5000 -0.0061
## 280 0.1898 nan 0.5000 -0.0031
## 300 0.1700 nan 0.5000 -0.0011
## 320 0.1576 nan 0.5000 -0.0037
## 340 0.1460 nan 0.5000 -0.0020
## 360 0.1321 nan 0.5000 -0.0014
## 380 0.1190 nan 0.5000 -0.0008
## 400 0.1088 nan 0.5000 -0.0019
## 420 0.0998 nan 0.5000 -0.0005
## 440 0.0961 nan 0.5000 -0.0027
## 460 0.0905 nan 0.5000 -0.0010
## 480 0.0830 nan 0.5000 -0.0012
## 500 0.0769 nan 0.5000 -0.0010
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1270 nan 0.5000 0.0807
## 2 1.0477 nan 0.5000 0.0335
## 3 1.0000 nan 0.5000 0.0102
## 4 0.9660 nan 0.5000 0.0053
## 5 0.9460 nan 0.5000 0.0051
## 6 0.9347 nan 0.5000 -0.0152
## 7 0.9055 nan 0.5000 0.0113
## 8 0.8915 nan 0.5000 -0.0024
## 9 0.8845 nan 0.5000 -0.0118
## 10 0.8727 nan 0.5000 -0.0021
## 20 0.7930 nan 0.5000 -0.0110
## 40 0.7013 nan 0.5000 -0.0098
## 60 0.6180 nan 0.5000 -0.0174
## 80 0.5542 nan 0.5000 -0.0059
## 100 0.4770 nan 0.5000 -0.0035
## 120 0.4439 nan 0.5000 -0.0080
## 140 0.4224 nan 0.5000 -0.0229
## 160 0.3806 nan 0.5000 -0.0001
## 180 0.3417 nan 0.5000 -0.0058
## 200 0.3101 nan 0.5000 -0.0065
## 220 0.3233 nan 0.5000 -0.0077
## 240 0.2907 nan 0.5000 -0.0067
## 260 0.2737 nan 0.5000 -0.0019
## 280 3258755157680.8887 nan 0.5000 -0.0015
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
## 500 inf nan 0.5000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1371 nan 0.5000 0.0820
## 2 1.0387 nan 0.5000 0.0277
## 3 0.9721 nan 0.5000 0.0307
## 4 0.9380 nan 0.5000 0.0164
## 5 0.9067 nan 0.5000 0.0085
## 6 0.8876 nan 0.5000 -0.0029
## 7 0.8830 nan 0.5000 -0.0092
## 8 0.8643 nan 0.5000 0.0002
## 9 0.8553 nan 0.5000 -0.0065
## 10 0.8390 nan 0.5000 0.0017
## 20 0.7774 nan 0.5000 -0.0132
## 40 0.6937 nan 0.5000 -0.0035
## 60 0.6191 nan 0.5000 -0.0091
## 80 0.5568 nan 0.5000 -0.0071
## 100 0.4943 nan 0.5000 -0.0022
## 120 0.4453 nan 0.5000 -0.0053
## 140 0.4132 nan 0.5000 -0.0077
## 160 0.3716 nan 0.5000 -0.0027
## 180 0.3498 nan 0.5000 -0.0085
## 200 0.3171 nan 0.5000 -0.0028
## 220 0.2758 nan 0.5000 -0.0029
## 240 0.2487 nan 0.5000 -0.0021
## 260 0.2234 nan 0.5000 -0.0023
## 280 0.2115 nan 0.5000 -0.0010
## 300 0.1966 nan 0.5000 -0.0024
## 320 0.1833 nan 0.5000 -0.0009
## 340 0.1685 nan 0.5000 -0.0024
## 360 0.1519 nan 0.5000 -0.0015
## 380 0.1399 nan 0.5000 -0.0031
## 400 0.1283 nan 0.5000 -0.0016
## 420 0.1168 nan 0.5000 -0.0009
## 440 0.1079 nan 0.5000 -0.0017
## 460 0.1015 nan 0.5000 -0.0005
## 480 0.0961 nan 0.5000 -0.0034
## 500 0.0880 nan 0.5000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1041 nan 0.5000 0.0907
## 2 0.9974 nan 0.5000 0.0398
## 3 0.9441 nan 0.5000 0.0182
## 4 0.9097 nan 0.5000 0.0018
## 5 0.8843 nan 0.5000 -0.0084
## 6 0.8621 nan 0.5000 0.0024
## 7 0.8419 nan 0.5000 -0.0073
## 8 0.8287 nan 0.5000 -0.0047
## 9 0.8195 nan 0.5000 -0.0098
## 10 0.8071 nan 0.5000 -0.0053
## 20 0.8358 nan 0.5000 -0.0149
## 40 0.6282 nan 0.5000 -0.0030
## 60 1.4746 nan 0.5000 -0.0152
## 80 1315.3979 nan 0.5000 -0.0076
## 100 1315.3406 nan 0.5000 -0.0025
## 120 1315.3188 nan 0.5000 -0.0005
## 140 1315.2851 nan 0.5000 -0.0039
## 160 1315.5094 nan 0.5000 -0.5024
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
## 500 inf nan 0.5000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0938 nan 0.5000 0.0845
## 2 1.0205 nan 0.5000 0.0250
## 3 0.9485 nan 0.5000 0.0110
## 4 0.8998 nan 0.5000 0.0061
## 5 0.8766 nan 0.5000 -0.0193
## 6 0.8580 nan 0.5000 -0.0040
## 7 0.8486 nan 0.5000 -0.0161
## 8 0.8281 nan 0.5000 -0.0051
## 9 0.8101 nan 0.5000 -0.0031
## 10 0.7926 nan 0.5000 -0.0010
## 20 0.7179 nan 0.5000 -0.0143
## 40 0.5771 nan 0.5000 -0.0130
## 60 0.4621 nan 0.5000 -0.0108
## 80 0.3687 nan 0.5000 -0.0107
## 100 0.3008 nan 0.5000 -0.0005
## 120 0.2532 nan 0.5000 -0.0022
## 140 0.2180 nan 0.5000 -0.0008
## 160 0.1828 nan 0.5000 -0.0046
## 180 0.1570 nan 0.5000 -0.0032
## 200 0.1361 nan 0.5000 -0.0023
## 220 0.1218 nan 0.5000 -0.0013
## 240 0.1068 nan 0.5000 -0.0001
## 260 0.0930 nan 0.5000 -0.0014
## 280 0.0804 nan 0.5000 -0.0013
## 300 0.0710 nan 0.5000 -0.0013
## 320 0.0604 nan 0.5000 0.0001
## 340 0.0552 nan 0.5000 -0.0010
## 360 0.0482 nan 0.5000 -0.0003
## 380 0.0431 nan 0.5000 -0.0004
## 400 0.0397 nan 0.5000 -0.0003
## 420 0.0350 nan 0.5000 -0.0003
## 440 0.0311 nan 0.5000 -0.0003
## 460 0.0281 nan 0.5000 -0.0003
## 480 0.0252 nan 0.5000 -0.0003
## 500 0.0224 nan 0.5000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1022 nan 0.5000 0.0865
## 2 1.0093 nan 0.5000 0.0320
## 3 0.9600 nan 0.5000 0.0102
## 4 0.9195 nan 0.5000 0.0057
## 5 0.9019 nan 0.5000 -0.0028
## 6 0.8776 nan 0.5000 0.0021
## 7 0.8580 nan 0.5000 -0.0093
## 8 0.8354 nan 0.5000 -0.0072
## 9 0.8188 nan 0.5000 -0.0032
## 10 0.8118 nan 0.5000 -0.0137
## 20 0.7289 nan 0.5000 -0.0161
## 40 0.5875 nan 0.5000 -0.0074
## 60 0.4665 nan 0.5000 -0.0049
## 80 0.4080 nan 0.5000 -0.0085
## 100 0.3236 nan 0.5000 -0.0041
## 120 0.2645 nan 0.5000 -0.0035
## 140 0.2247 nan 0.5000 -0.0037
## 160 0.1966 nan 0.5000 -0.0032
## 180 0.1641 nan 0.5000 -0.0008
## 200 0.1396 nan 0.5000 -0.0029
## 220 0.1187 nan 0.5000 -0.0019
## 240 0.1028 nan 0.5000 -0.0015
## 260 0.0892 nan 0.5000 -0.0026
## 280 0.0783 nan 0.5000 -0.0009
## 300 0.0699 nan 0.5000 -0.0007
## 320 0.0624 nan 0.5000 -0.0012
## 340 0.0536 nan 0.5000 -0.0003
## 360 0.0457 nan 0.5000 -0.0009
## 380 0.0399 nan 0.5000 -0.0004
## 400 0.0355 nan 0.5000 -0.0007
## 420 0.0308 nan 0.5000 -0.0002
## 440 0.0268 nan 0.5000 0.0001
## 460 0.0228 nan 0.5000 -0.0002
## 480 0.0208 nan 0.5000 -0.0003
## 500 0.0187 nan 0.5000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1146 nan 1.0000 0.0998
## 2 1.0866 nan 1.0000 -0.0146
## 3 1.0295 nan 1.0000 0.0205
## 4 1.0139 nan 1.0000 -0.0121
## 5 0.9821 nan 1.0000 0.0018
## 6 1.0043 nan 1.0000 -0.0409
## 7 0.9919 nan 1.0000 -0.0109
## 8 0.9861 nan 1.0000 -0.0061
## 9 0.9571 nan 1.0000 0.0071
## 10 0.9558 nan 1.0000 -0.0121
## 20 1.0333 nan 1.0000 -0.0116
## 40 17211.4037 nan 1.0000 -0.0194
## 60 17212.7577 nan 1.0000 -0.0127
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1392 nan 1.0000 0.0653
## 2 1.0733 nan 1.0000 0.0118
## 3 1.0090 nan 1.0000 0.0270
## 4 0.9657 nan 1.0000 0.0104
## 5 0.9582 nan 1.0000 -0.0141
## 6 0.9747 nan 1.0000 -0.0335
## 7 0.9607 nan 1.0000 0.0018
## 8 0.9690 nan 1.0000 -0.0256
## 9 0.9496 nan 1.0000 0.0016
## 10 0.9546 nan 1.0000 -0.0196
## 20 0.8940 nan 1.0000 -0.0022
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 -0.0152
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1319 nan 1.0000 0.0746
## 2 1.0697 nan 1.0000 0.0175
## 3 1.0182 nan 1.0000 0.0145
## 4 0.9773 nan 1.0000 0.0209
## 5 0.9596 nan 1.0000 0.0009
## 6 0.9484 nan 1.0000 -0.0001
## 7 0.9378 nan 1.0000 -0.0037
## 8 0.9244 nan 1.0000 0.0009
## 9 0.9023 nan 1.0000 0.0049
## 10 0.9011 nan 1.0000 -0.0101
## 20 0.8309 nan 1.0000 -0.0045
## 40 0.7952 nan 1.0000 -0.0255
## 60 0.7967 nan 1.0000 -0.0127
## 80 0.7663 nan 1.0000 -0.0176
## 100 0.7019 nan 1.0000 -0.0032
## 120 0.7089 nan 1.0000 -0.0283
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0824 nan 1.0000 0.0672
## 2 1.0257 nan 1.0000 -0.0208
## 3 0.9913 nan 1.0000 0.0055
## 4 0.9736 nan 1.0000 -0.0185
## 5 0.9636 nan 1.0000 -0.0259
## 6 0.9436 nan 1.0000 -0.0096
## 7 0.9226 nan 1.0000 -0.0205
## 8 0.9169 nan 1.0000 -0.0288
## 9 1.0423 nan 1.0000 -0.1557
## 10 0.9702 nan 1.0000 -0.0275
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 -0.0013
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1107 nan 1.0000 0.0454
## 2 1.0154 nan 1.0000 0.0138
## 3 0.9838 nan 1.0000 -0.0141
## 4 0.9523 nan 1.0000 -0.0114
## 5 0.9294 nan 1.0000 -0.0042
## 6 0.9594 nan 1.0000 -0.0539
## 7 0.9059 nan 1.0000 0.0005
## 8 0.8834 nan 1.0000 -0.0117
## 9 0.8775 nan 1.0000 -0.0243
## 10 0.8510 nan 1.0000 -0.0016
## 20 0.8692 nan 1.0000 -0.0306
## 40 inf nan 1.0000 -inf
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0598 nan 1.0000 0.1025
## 2 1.0134 nan 1.0000 -0.0082
## 3 0.9889 nan 1.0000 -0.0049
## 4 0.9654 nan 1.0000 -0.0049
## 5 0.9547 nan 1.0000 -0.0271
## 6 0.9297 nan 1.0000 -0.0150
## 7 0.9208 nan 1.0000 -0.0111
## 8 0.9059 nan 1.0000 -0.0115
## 9 0.8868 nan 1.0000 -0.0112
## 10 0.8825 nan 1.0000 -0.0230
## 20 0.8607 nan 1.0000 -0.0259
## 40 2.9888 nan 1.0000 -0.0350
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0490 nan 1.0000 0.0720
## 2 0.9504 nan 1.0000 0.0314
## 3 0.9201 nan 1.0000 -0.0097
## 4 0.9283 nan 1.0000 -0.0702
## 5 1.1002 nan 1.0000 -0.2281
## 6 1.0620 nan 1.0000 -0.0145
## 7 1.0242 nan 1.0000 -0.0116
## 8 1.0150 nan 1.0000 -0.0324
## 9 1.0017 nan 1.0000 -0.0326
## 10 1.0066 nan 1.0000 -0.0269
## 20 1.2278 nan 1.0000 -0.0360
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0376 nan 1.0000 0.0806
## 2 1.0113 nan 1.0000 -0.0427
## 3 0.9792 nan 1.0000 -0.0347
## 4 0.9827 nan 1.0000 -0.0459
## 5 0.9668 nan 1.0000 -0.0381
## 6 0.9147 nan 1.0000 0.0046
## 7 0.9202 nan 1.0000 -0.0539
## 8 0.9254 nan 1.0000 -0.0654
## 9 0.9485 nan 1.0000 -0.0672
## 10 0.9197 nan 1.0000 -0.0153
## 20 1.2740 nan 1.0000 -0.1575
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0684 nan 1.0000 0.0810
## 2 0.9634 nan 1.0000 0.0429
## 3 0.9231 nan 1.0000 -0.0151
## 4 0.9154 nan 1.0000 -0.0335
## 5 0.9295 nan 1.0000 -0.0868
## 6 0.8956 nan 1.0000 -0.0126
## 7 0.8799 nan 1.0000 -0.0082
## 8 0.8826 nan 1.0000 -0.0500
## 9 0.8675 nan 1.0000 -0.0390
## 10 0.8464 nan 1.0000 -0.0119
## 20 1.2892 nan 1.0000 -0.0178
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
## 2 1.2945 nan 0.0000 0.0000
## 3 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2942 nan 0.0010 0.0002
## 2 1.2938 nan 0.0010 0.0001
## 3 1.2935 nan 0.0010 0.0002
## 4 1.2931 nan 0.0010 0.0002
## 5 1.2928 nan 0.0010 0.0002
## 6 1.2925 nan 0.0010 0.0002
## 7 1.2921 nan 0.0010 0.0002
## 8 1.2918 nan 0.0010 0.0001
## 9 1.2914 nan 0.0010 0.0002
## 10 1.2910 nan 0.0010 0.0002
## 20 1.2875 nan 0.0010 0.0002
## 40 1.2806 nan 0.0010 0.0002
## 60 1.2740 nan 0.0010 0.0002
## 80 1.2675 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2942 nan 0.0010 0.0002
## 2 1.2938 nan 0.0010 0.0002
## 3 1.2935 nan 0.0010 0.0002
## 4 1.2931 nan 0.0010 0.0002
## 5 1.2928 nan 0.0010 0.0001
## 6 1.2924 nan 0.0010 0.0002
## 7 1.2920 nan 0.0010 0.0002
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## 10 1.2910 nan 0.0010 0.0002
## 20 1.2875 nan 0.0010 0.0002
## 40 1.2806 nan 0.0010 0.0002
## 60 1.2739 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2942 nan 0.0010 0.0002
## 2 1.2939 nan 0.0010 0.0001
## 3 1.2935 nan 0.0010 0.0001
## 4 1.2931 nan 0.0010 0.0002
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## 6 1.2923 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
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## 3 1.2931 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
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## 3 1.2930 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
## 2 1.2936 nan 0.0010 0.0002
## 3 1.2931 nan 0.0010 0.0002
## 4 1.2927 nan 0.0010 0.0002
## 5 1.2923 nan 0.0010 0.0002
## 6 1.2918 nan 0.0010 0.0002
## 7 1.2913 nan 0.0010 0.0002
## 8 1.2909 nan 0.0010 0.0002
## 9 1.2905 nan 0.0010 0.0002
## 10 1.2900 nan 0.0010 0.0002
## 20 1.2854 nan 0.0010 0.0002
## 40 1.2766 nan 0.0010 0.0002
## 60 1.2679 nan 0.0010 0.0002
## 80 1.2593 nan 0.0010 0.0001
## 100 1.2512 nan 0.0010 0.0001
## 120 1.2432 nan 0.0010 0.0002
## 140 1.2355 nan 0.0010 0.0001
## 160 1.2280 nan 0.0010 0.0002
## 180 1.2208 nan 0.0010 0.0002
## 200 1.2137 nan 0.0010 0.0002
## 220 1.2069 nan 0.0010 0.0002
## 240 1.2005 nan 0.0010 0.0001
## 260 1.1940 nan 0.0010 0.0001
## 280 1.1880 nan 0.0010 0.0001
## 300 1.1820 nan 0.0010 0.0001
## 320 1.1762 nan 0.0010 0.0001
## 340 1.1704 nan 0.0010 0.0001
## 360 1.1647 nan 0.0010 0.0001
## 380 1.1593 nan 0.0010 0.0001
## 400 1.1541 nan 0.0010 0.0001
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## 440 1.1439 nan 0.0010 0.0001
## 460 1.1390 nan 0.0010 0.0001
## 480 1.1342 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
## 2 1.2934 nan 0.0010 0.0003
## 3 1.2929 nan 0.0010 0.0002
## 4 1.2923 nan 0.0010 0.0003
## 5 1.2918 nan 0.0010 0.0002
## 6 1.2913 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2902 nan 0.0010 0.0002
## 9 1.2897 nan 0.0010 0.0002
## 10 1.2892 nan 0.0010 0.0002
## 20 1.2838 nan 0.0010 0.0002
## 40 1.2738 nan 0.0010 0.0002
## 60 1.2633 nan 0.0010 0.0002
## 80 1.2537 nan 0.0010 0.0002
## 100 1.2443 nan 0.0010 0.0002
## 120 1.2354 nan 0.0010 0.0002
## 140 1.2266 nan 0.0010 0.0002
## 160 1.2176 nan 0.0010 0.0002
## 180 1.2095 nan 0.0010 0.0002
## 200 1.2014 nan 0.0010 0.0002
## 220 1.1934 nan 0.0010 0.0002
## 240 1.1855 nan 0.0010 0.0002
## 260 1.1782 nan 0.0010 0.0001
## 280 1.1710 nan 0.0010 0.0001
## 300 1.1642 nan 0.0010 0.0001
## 320 1.1573 nan 0.0010 0.0001
## 340 1.1508 nan 0.0010 0.0001
## 360 1.1444 nan 0.0010 0.0002
## 380 1.1385 nan 0.0010 0.0001
## 400 1.1325 nan 0.0010 0.0001
## 420 1.1267 nan 0.0010 0.0001
## 440 1.1206 nan 0.0010 0.0001
## 460 1.1148 nan 0.0010 0.0001
## 480 1.1094 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
## 2 1.2935 nan 0.0010 0.0003
## 3 1.2930 nan 0.0010 0.0002
## 4 1.2925 nan 0.0010 0.0002
## 5 1.2920 nan 0.0010 0.0002
## 6 1.2914 nan 0.0010 0.0002
## 7 1.2909 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2899 nan 0.0010 0.0002
## 10 1.2893 nan 0.0010 0.0002
## 20 1.2841 nan 0.0010 0.0002
## 40 1.2738 nan 0.0010 0.0003
## 60 1.2636 nan 0.0010 0.0002
## 80 1.2538 nan 0.0010 0.0002
## 100 1.2445 nan 0.0010 0.0002
## 120 1.2356 nan 0.0010 0.0002
## 140 1.2270 nan 0.0010 0.0002
## 160 1.2185 nan 0.0010 0.0002
## 180 1.2101 nan 0.0010 0.0002
## 200 1.2020 nan 0.0010 0.0002
## 220 1.1942 nan 0.0010 0.0002
## 240 1.1864 nan 0.0010 0.0002
## 260 1.1791 nan 0.0010 0.0001
## 280 1.1719 nan 0.0010 0.0002
## 300 1.1649 nan 0.0010 0.0001
## 320 1.1583 nan 0.0010 0.0001
## 340 1.1519 nan 0.0010 0.0001
## 360 1.1453 nan 0.0010 0.0001
## 380 1.1391 nan 0.0010 0.0001
## 400 1.1332 nan 0.0010 0.0001
## 420 1.1272 nan 0.0010 0.0001
## 440 1.1213 nan 0.0010 0.0001
## 460 1.1156 nan 0.0010 0.0001
## 480 1.1102 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
## 2 1.2935 nan 0.0010 0.0003
## 3 1.2930 nan 0.0010 0.0002
## 4 1.2925 nan 0.0010 0.0002
## 5 1.2919 nan 0.0010 0.0002
## 6 1.2914 nan 0.0010 0.0002
## 7 1.2909 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2898 nan 0.0010 0.0002
## 10 1.2892 nan 0.0010 0.0003
## 20 1.2840 nan 0.0010 0.0002
## 40 1.2736 nan 0.0010 0.0002
## 60 1.2639 nan 0.0010 0.0002
## 80 1.2541 nan 0.0010 0.0002
## 100 1.2448 nan 0.0010 0.0002
## 120 1.2357 nan 0.0010 0.0002
## 140 1.2270 nan 0.0010 0.0002
## 160 1.2185 nan 0.0010 0.0001
## 180 1.2100 nan 0.0010 0.0002
## 200 1.2021 nan 0.0010 0.0001
## 220 1.1947 nan 0.0010 0.0002
## 240 1.1868 nan 0.0010 0.0002
## 260 1.1793 nan 0.0010 0.0001
## 280 1.1724 nan 0.0010 0.0001
## 300 1.1655 nan 0.0010 0.0001
## 320 1.1587 nan 0.0010 0.0001
## 340 1.1524 nan 0.0010 0.0001
## 360 1.1461 nan 0.0010 0.0001
## 380 1.1399 nan 0.0010 0.0001
## 400 1.1337 nan 0.0010 0.0001
## 420 1.1277 nan 0.0010 0.0001
## 440 1.1220 nan 0.0010 0.0001
## 460 1.1163 nan 0.0010 0.0001
## 480 1.1108 nan 0.0010 0.0001
## 500 1.1054 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2648 nan 0.1000 0.0130
## 2 1.2325 nan 0.1000 0.0142
## 3 1.2053 nan 0.1000 0.0116
## 4 1.1844 nan 0.1000 0.0087
## 5 1.1656 nan 0.1000 0.0077
## 6 1.1480 nan 0.1000 0.0071
## 7 1.1304 nan 0.1000 0.0072
## 8 1.1173 nan 0.1000 0.0053
## 9 1.1032 nan 0.1000 0.0061
## 10 1.0892 nan 0.1000 0.0058
## 20 1.0050 nan 0.1000 0.0008
## 40 0.9212 nan 0.1000 0.0010
## 60 0.8802 nan 0.1000 -0.0006
## 80 0.8544 nan 0.1000 -0.0013
## 100 0.8381 nan 0.1000 -0.0004
## 120 0.8217 nan 0.1000 -0.0003
## 140 0.8121 nan 0.1000 -0.0009
## 160 0.8018 nan 0.1000 -0.0003
## 180 0.7924 nan 0.1000 -0.0009
## 200 0.7817 nan 0.1000 -0.0004
## 220 0.7741 nan 0.1000 -0.0014
## 240 0.7669 nan 0.1000 -0.0004
## 260 0.7615 nan 0.1000 -0.0017
## 280 0.7516 nan 0.1000 -0.0004
## 300 0.7460 nan 0.1000 -0.0020
## 320 0.7382 nan 0.1000 -0.0006
## 340 0.7318 nan 0.1000 -0.0014
## 360 0.7256 nan 0.1000 -0.0006
## 380 0.7190 nan 0.1000 -0.0009
## 400 0.7126 nan 0.1000 -0.0005
## 420 0.7071 nan 0.1000 -0.0011
## 440 0.7023 nan 0.1000 -0.0017
## 460 0.6958 nan 0.1000 -0.0010
## 480 0.6907 nan 0.1000 -0.0008
## 500 0.6861 nan 0.1000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2585 nan 0.1000 0.0161
## 2 1.2275 nan 0.1000 0.0142
## 3 1.2069 nan 0.1000 0.0060
## 4 1.1812 nan 0.1000 0.0103
## 5 1.1609 nan 0.1000 0.0096
## 6 1.1474 nan 0.1000 0.0054
## 7 1.1327 nan 0.1000 0.0072
## 8 1.1190 nan 0.1000 0.0057
## 9 1.1048 nan 0.1000 0.0055
## 10 1.0918 nan 0.1000 0.0053
## 20 1.0085 nan 0.1000 0.0011
## 40 0.9182 nan 0.1000 -0.0000
## 60 0.8785 nan 0.1000 -0.0007
## 80 0.8538 nan 0.1000 0.0004
## 100 0.8362 nan 0.1000 -0.0008
## 120 0.8220 nan 0.1000 -0.0003
## 140 0.8112 nan 0.1000 -0.0011
## 160 0.7988 nan 0.1000 -0.0007
## 180 0.7877 nan 0.1000 -0.0008
## 200 0.7776 nan 0.1000 -0.0009
## 220 0.7698 nan 0.1000 -0.0015
## 240 0.7644 nan 0.1000 -0.0012
## 260 0.7566 nan 0.1000 -0.0010
## 280 0.7513 nan 0.1000 -0.0006
## 300 0.7439 nan 0.1000 -0.0010
## 320 0.7380 nan 0.1000 -0.0012
## 340 0.7308 nan 0.1000 -0.0009
## 360 0.7240 nan 0.1000 -0.0006
## 380 0.7194 nan 0.1000 -0.0010
## 400 0.7167 nan 0.1000 -0.0010
## 420 0.7094 nan 0.1000 -0.0008
## 440 0.7044 nan 0.1000 -0.0005
## 460 0.6981 nan 0.1000 -0.0002
## 480 0.6935 nan 0.1000 -0.0002
## 500 0.6901 nan 0.1000 -0.0013
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2594 nan 0.1000 0.0162
## 2 1.2266 nan 0.1000 0.0122
## 3 1.2010 nan 0.1000 0.0104
## 4 1.1833 nan 0.1000 0.0079
## 5 1.1657 nan 0.1000 0.0078
## 6 1.1515 nan 0.1000 0.0075
## 7 1.1343 nan 0.1000 0.0070
## 8 1.1189 nan 0.1000 0.0063
## 9 1.1023 nan 0.1000 0.0050
## 10 1.0916 nan 0.1000 0.0051
## 20 1.0015 nan 0.1000 0.0025
## 40 0.9175 nan 0.1000 0.0005
## 60 0.8792 nan 0.1000 -0.0013
## 80 0.8554 nan 0.1000 -0.0002
## 100 0.8378 nan 0.1000 -0.0012
## 120 0.8237 nan 0.1000 -0.0011
## 140 0.8063 nan 0.1000 -0.0005
## 160 0.7962 nan 0.1000 -0.0007
## 180 0.7864 nan 0.1000 -0.0007
## 200 0.7781 nan 0.1000 -0.0001
## 220 0.7682 nan 0.1000 -0.0012
## 240 0.7615 nan 0.1000 -0.0025
## 260 0.7530 nan 0.1000 -0.0005
## 280 0.7464 nan 0.1000 -0.0007
## 300 0.7401 nan 0.1000 -0.0016
## 320 0.7330 nan 0.1000 -0.0002
## 340 0.7281 nan 0.1000 -0.0012
## 360 0.7202 nan 0.1000 -0.0016
## 380 0.7150 nan 0.1000 -0.0001
## 400 0.7103 nan 0.1000 -0.0012
## 420 0.7058 nan 0.1000 -0.0007
## 440 0.7001 nan 0.1000 -0.0013
## 460 0.6943 nan 0.1000 -0.0004
## 480 0.6916 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2437 nan 0.1000 0.0202
## 2 1.2089 nan 0.1000 0.0164
## 3 1.1762 nan 0.1000 0.0110
## 4 1.1487 nan 0.1000 0.0140
## 5 1.1254 nan 0.1000 0.0095
## 6 1.1043 nan 0.1000 0.0078
## 7 1.0885 nan 0.1000 0.0058
## 8 1.0692 nan 0.1000 0.0066
## 9 1.0525 nan 0.1000 0.0067
## 10 1.0395 nan 0.1000 0.0048
## 20 0.9447 nan 0.1000 0.0013
## 40 0.8563 nan 0.1000 -0.0017
## 60 0.8022 nan 0.1000 0.0007
## 80 0.7639 nan 0.1000 -0.0007
## 100 0.7390 nan 0.1000 -0.0019
## 120 0.7133 nan 0.1000 -0.0015
## 140 0.6899 nan 0.1000 -0.0007
## 160 0.6708 nan 0.1000 -0.0010
## 180 0.6541 nan 0.1000 -0.0003
## 200 0.6372 nan 0.1000 -0.0011
## 220 0.6173 nan 0.1000 -0.0020
## 240 0.5996 nan 0.1000 -0.0014
## 260 0.5835 nan 0.1000 -0.0007
## 280 0.5704 nan 0.1000 -0.0014
## 300 0.5554 nan 0.1000 -0.0016
## 320 0.5405 nan 0.1000 -0.0004
## 340 0.5276 nan 0.1000 -0.0003
## 360 0.5143 nan 0.1000 -0.0011
## 380 0.5024 nan 0.1000 -0.0009
## 400 0.4899 nan 0.1000 -0.0003
## 420 0.4784 nan 0.1000 -0.0003
## 440 0.4686 nan 0.1000 -0.0016
## 460 0.4580 nan 0.1000 -0.0003
## 480 0.4473 nan 0.1000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2513 nan 0.1000 0.0206
## 2 1.2154 nan 0.1000 0.0176
## 3 1.1826 nan 0.1000 0.0148
## 4 1.1563 nan 0.1000 0.0101
## 5 1.1301 nan 0.1000 0.0106
## 6 1.1073 nan 0.1000 0.0103
## 7 1.0862 nan 0.1000 0.0071
## 8 1.0651 nan 0.1000 0.0067
## 9 1.0521 nan 0.1000 0.0030
## 10 1.0386 nan 0.1000 0.0035
## 20 0.9400 nan 0.1000 -0.0008
## 40 0.8583 nan 0.1000 0.0008
## 60 0.8111 nan 0.1000 -0.0000
## 80 0.7702 nan 0.1000 -0.0010
## 100 0.7448 nan 0.1000 -0.0009
## 120 0.7222 nan 0.1000 -0.0014
## 140 0.7004 nan 0.1000 -0.0017
## 160 0.6788 nan 0.1000 -0.0005
## 180 0.6527 nan 0.1000 -0.0003
## 200 0.6355 nan 0.1000 -0.0012
## 220 0.6174 nan 0.1000 -0.0007
## 240 0.6007 nan 0.1000 -0.0012
## 260 0.5867 nan 0.1000 -0.0015
## 280 0.5742 nan 0.1000 -0.0010
## 300 0.5594 nan 0.1000 -0.0010
## 320 0.5412 nan 0.1000 -0.0013
## 340 0.5295 nan 0.1000 -0.0012
## 360 0.5161 nan 0.1000 -0.0011
## 380 0.5048 nan 0.1000 -0.0012
## 400 0.4919 nan 0.1000 -0.0014
## 420 0.4781 nan 0.1000 -0.0003
## 440 0.4675 nan 0.1000 -0.0015
## 460 0.4559 nan 0.1000 -0.0006
## 480 0.4464 nan 0.1000 -0.0008
## 500 0.4344 nan 0.1000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2494 nan 0.1000 0.0198
## 2 1.2105 nan 0.1000 0.0178
## 3 1.1790 nan 0.1000 0.0142
## 4 1.1499 nan 0.1000 0.0102
## 5 1.1225 nan 0.1000 0.0113
## 6 1.0987 nan 0.1000 0.0082
## 7 1.0808 nan 0.1000 0.0071
## 8 1.0641 nan 0.1000 0.0045
## 9 1.0473 nan 0.1000 0.0058
## 10 1.0342 nan 0.1000 0.0053
## 20 0.9428 nan 0.1000 -0.0000
## 40 0.8531 nan 0.1000 -0.0004
## 60 0.8080 nan 0.1000 -0.0016
## 80 0.7742 nan 0.1000 -0.0002
## 100 0.7412 nan 0.1000 -0.0008
## 120 0.7203 nan 0.1000 -0.0010
## 140 0.6929 nan 0.1000 -0.0018
## 160 0.6746 nan 0.1000 -0.0018
## 180 0.6538 nan 0.1000 -0.0009
## 200 0.6352 nan 0.1000 -0.0007
## 220 0.6187 nan 0.1000 -0.0018
## 240 0.6045 nan 0.1000 -0.0006
## 260 0.5871 nan 0.1000 -0.0007
## 280 0.5723 nan 0.1000 -0.0007
## 300 0.5593 nan 0.1000 -0.0015
## 320 0.5461 nan 0.1000 -0.0004
## 340 0.5321 nan 0.1000 -0.0002
## 360 0.5174 nan 0.1000 -0.0009
## 380 0.5058 nan 0.1000 -0.0010
## 400 0.4895 nan 0.1000 -0.0009
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## 460 0.4580 nan 0.1000 -0.0014
## 480 0.4488 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2471 nan 0.1000 0.0234
## 2 1.1995 nan 0.1000 0.0189
## 3 1.1636 nan 0.1000 0.0129
## 4 1.1309 nan 0.1000 0.0141
## 5 1.1056 nan 0.1000 0.0073
## 6 1.0801 nan 0.1000 0.0093
## 7 1.0638 nan 0.1000 0.0040
## 8 1.0427 nan 0.1000 0.0079
## 9 1.0266 nan 0.1000 0.0065
## 10 1.0096 nan 0.1000 0.0070
## 20 0.9056 nan 0.1000 0.0003
## 40 0.8038 nan 0.1000 0.0004
## 60 0.7459 nan 0.1000 -0.0009
## 80 0.7010 nan 0.1000 -0.0016
## 100 0.6538 nan 0.1000 -0.0020
## 120 0.6168 nan 0.1000 -0.0009
## 140 0.5863 nan 0.1000 -0.0010
## 160 0.5526 nan 0.1000 -0.0009
## 180 0.5302 nan 0.1000 -0.0006
## 200 0.5060 nan 0.1000 -0.0001
## 220 0.4856 nan 0.1000 -0.0004
## 240 0.4605 nan 0.1000 -0.0018
## 260 0.4393 nan 0.1000 -0.0009
## 280 0.4206 nan 0.1000 -0.0001
## 300 0.4068 nan 0.1000 -0.0009
## 320 0.3886 nan 0.1000 -0.0004
## 340 0.3737 nan 0.1000 -0.0006
## 360 0.3567 nan 0.1000 -0.0008
## 380 0.3457 nan 0.1000 -0.0010
## 400 0.3328 nan 0.1000 -0.0009
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## 460 0.2993 nan 0.1000 -0.0004
## 480 0.2884 nan 0.1000 -0.0012
## 500 0.2763 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2405 nan 0.1000 0.0231
## 2 1.2001 nan 0.1000 0.0137
## 3 1.1616 nan 0.1000 0.0140
## 4 1.1273 nan 0.1000 0.0121
## 5 1.0989 nan 0.1000 0.0086
## 6 1.0786 nan 0.1000 0.0067
## 7 1.0536 nan 0.1000 0.0084
## 8 1.0355 nan 0.1000 0.0075
## 9 1.0209 nan 0.1000 0.0037
## 10 1.0092 nan 0.1000 0.0037
## 20 0.9028 nan 0.1000 0.0019
## 40 0.8041 nan 0.1000 0.0002
## 60 0.7487 nan 0.1000 -0.0020
## 80 0.7126 nan 0.1000 -0.0005
## 100 0.6699 nan 0.1000 -0.0018
## 120 0.6391 nan 0.1000 -0.0018
## 140 0.6065 nan 0.1000 -0.0009
## 160 0.5737 nan 0.1000 -0.0015
## 180 0.5481 nan 0.1000 -0.0022
## 200 0.5262 nan 0.1000 -0.0029
## 220 0.5004 nan 0.1000 -0.0009
## 240 0.4735 nan 0.1000 -0.0032
## 260 0.4528 nan 0.1000 -0.0006
## 280 0.4333 nan 0.1000 -0.0011
## 300 0.4137 nan 0.1000 -0.0005
## 320 0.3968 nan 0.1000 0.0000
## 340 0.3806 nan 0.1000 -0.0008
## 360 0.3663 nan 0.1000 -0.0009
## 380 0.3522 nan 0.1000 -0.0008
## 400 0.3380 nan 0.1000 -0.0005
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## 460 0.2996 nan 0.1000 -0.0005
## 480 0.2866 nan 0.1000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2460 nan 0.1000 0.0195
## 2 1.1974 nan 0.1000 0.0193
## 3 1.1624 nan 0.1000 0.0169
## 4 1.1299 nan 0.1000 0.0111
## 5 1.1017 nan 0.1000 0.0117
## 6 1.0821 nan 0.1000 0.0077
## 7 1.0544 nan 0.1000 0.0091
## 8 1.0359 nan 0.1000 0.0049
## 9 1.0189 nan 0.1000 0.0064
## 10 1.0043 nan 0.1000 0.0057
## 20 0.8904 nan 0.1000 0.0003
## 40 0.8021 nan 0.1000 -0.0001
## 60 0.7442 nan 0.1000 -0.0022
## 80 0.7032 nan 0.1000 -0.0001
## 100 0.6649 nan 0.1000 -0.0021
## 120 0.6306 nan 0.1000 -0.0018
## 140 0.5949 nan 0.1000 -0.0006
## 160 0.5620 nan 0.1000 -0.0013
## 180 0.5340 nan 0.1000 -0.0014
## 200 0.5121 nan 0.1000 -0.0011
## 220 0.4896 nan 0.1000 -0.0018
## 240 0.4676 nan 0.1000 -0.0015
## 260 0.4499 nan 0.1000 -0.0007
## 280 0.4274 nan 0.1000 -0.0011
## 300 0.4117 nan 0.1000 -0.0002
## 320 0.3912 nan 0.1000 -0.0006
## 340 0.3774 nan 0.1000 -0.0007
## 360 0.3620 nan 0.1000 -0.0005
## 380 0.3492 nan 0.1000 -0.0010
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## 460 0.2975 nan 0.1000 -0.0002
## 480 0.2853 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2282 nan 0.2000 0.0296
## 2 1.1824 nan 0.2000 0.0197
## 3 1.1476 nan 0.2000 0.0119
## 4 1.1131 nan 0.2000 0.0129
## 5 1.0841 nan 0.2000 0.0132
## 6 1.0622 nan 0.2000 0.0054
## 7 1.0443 nan 0.2000 0.0066
## 8 1.0242 nan 0.2000 0.0076
## 9 1.0133 nan 0.2000 0.0032
## 10 1.0003 nan 0.2000 0.0024
## 20 0.9191 nan 0.2000 0.0011
## 40 0.8581 nan 0.2000 -0.0003
## 60 0.8211 nan 0.2000 -0.0011
## 80 0.7995 nan 0.2000 -0.0018
## 100 0.7756 nan 0.2000 -0.0023
## 120 0.7607 nan 0.2000 -0.0010
## 140 0.7458 nan 0.2000 -0.0013
## 160 0.7344 nan 0.2000 -0.0029
## 180 0.7262 nan 0.2000 -0.0021
## 200 0.7159 nan 0.2000 -0.0031
## 220 0.7043 nan 0.2000 -0.0025
## 240 0.6926 nan 0.2000 -0.0020
## 260 0.6847 nan 0.2000 -0.0009
## 280 0.6792 nan 0.2000 -0.0022
## 300 0.6710 nan 0.2000 -0.0015
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## 340 0.6583 nan 0.2000 -0.0021
## 360 0.6551 nan 0.2000 -0.0007
## 380 0.6468 nan 0.2000 -0.0019
## 400 0.6412 nan 0.2000 -0.0039
## 420 0.6295 nan 0.2000 -0.0015
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## 460 0.6225 nan 0.2000 -0.0009
## 480 0.6154 nan 0.2000 -0.0036
## 500 0.6141 nan 0.2000 -0.0030
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2260 nan 0.2000 0.0341
## 2 1.1778 nan 0.2000 0.0220
## 3 1.1465 nan 0.2000 0.0147
## 4 1.1171 nan 0.2000 0.0127
## 5 1.0915 nan 0.2000 0.0110
## 6 1.0708 nan 0.2000 0.0051
## 7 1.0436 nan 0.2000 0.0090
## 8 1.0267 nan 0.2000 0.0065
## 9 1.0162 nan 0.2000 0.0028
## 10 0.9990 nan 0.2000 0.0049
## 20 0.9166 nan 0.2000 -0.0010
## 40 0.8479 nan 0.2000 -0.0007
## 60 0.8179 nan 0.2000 -0.0003
## 80 0.7933 nan 0.2000 -0.0016
## 100 0.7741 nan 0.2000 -0.0023
## 120 0.7612 nan 0.2000 -0.0003
## 140 0.7505 nan 0.2000 -0.0011
## 160 0.7390 nan 0.2000 -0.0012
## 180 0.7260 nan 0.2000 -0.0004
## 200 0.7202 nan 0.2000 -0.0009
## 220 0.7087 nan 0.2000 -0.0037
## 240 0.6993 nan 0.2000 -0.0024
## 260 0.6921 nan 0.2000 -0.0030
## 280 0.6845 nan 0.2000 -0.0013
## 300 0.6737 nan 0.2000 -0.0025
## 320 0.6680 nan 0.2000 -0.0018
## 340 0.6606 nan 0.2000 -0.0015
## 360 0.6561 nan 0.2000 -0.0022
## 380 0.6494 nan 0.2000 -0.0003
## 400 0.6445 nan 0.2000 -0.0004
## 420 0.6403 nan 0.2000 -0.0036
## 440 0.6337 nan 0.2000 -0.0018
## 460 0.6308 nan 0.2000 -0.0028
## 480 0.6244 nan 0.2000 -0.0009
## 500 0.6192 nan 0.2000 -0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2436 nan 0.2000 0.0214
## 2 1.1860 nan 0.2000 0.0241
## 3 1.1488 nan 0.2000 0.0104
## 4 1.1168 nan 0.2000 0.0130
## 5 1.0917 nan 0.2000 0.0113
## 6 1.0681 nan 0.2000 0.0101
## 7 1.0594 nan 0.2000 0.0005
## 8 1.0434 nan 0.2000 0.0060
## 9 1.0210 nan 0.2000 0.0048
## 10 1.0074 nan 0.2000 0.0057
## 20 0.9327 nan 0.2000 0.0008
## 40 0.8595 nan 0.2000 0.0000
## 60 0.8259 nan 0.2000 -0.0017
## 80 0.8046 nan 0.2000 -0.0025
## 100 0.7876 nan 0.2000 -0.0011
## 120 0.7668 nan 0.2000 -0.0017
## 140 0.7552 nan 0.2000 -0.0017
## 160 0.7413 nan 0.2000 -0.0021
## 180 0.7297 nan 0.2000 -0.0005
## 200 0.7211 nan 0.2000 -0.0049
## 220 0.7122 nan 0.2000 -0.0013
## 240 0.7004 nan 0.2000 -0.0031
## 260 0.6924 nan 0.2000 -0.0017
## 280 0.6819 nan 0.2000 -0.0024
## 300 0.6715 nan 0.2000 -0.0017
## 320 0.6643 nan 0.2000 -0.0034
## 340 0.6559 nan 0.2000 -0.0017
## 360 0.6494 nan 0.2000 -0.0010
## 380 0.6461 nan 0.2000 -0.0023
## 400 0.6359 nan 0.2000 -0.0031
## 420 0.6311 nan 0.2000 -0.0018
## 440 0.6222 nan 0.2000 -0.0001
## 460 0.6187 nan 0.2000 -0.0024
## 480 0.6093 nan 0.2000 -0.0016
## 500 0.6060 nan 0.2000 -0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2072 nan 0.2000 0.0402
## 2 1.1427 nan 0.2000 0.0281
## 3 1.0919 nan 0.2000 0.0196
## 4 1.0564 nan 0.2000 0.0133
## 5 1.0291 nan 0.2000 0.0073
## 6 1.0002 nan 0.2000 0.0099
## 7 0.9796 nan 0.2000 0.0033
## 8 0.9633 nan 0.2000 0.0053
## 9 0.9520 nan 0.2000 -0.0004
## 10 0.9387 nan 0.2000 0.0031
## 20 0.8547 nan 0.2000 -0.0019
## 40 0.7764 nan 0.2000 -0.0015
## 60 0.7304 nan 0.2000 -0.0030
## 80 0.6921 nan 0.2000 -0.0045
## 100 0.6442 nan 0.2000 -0.0019
## 120 0.6104 nan 0.2000 -0.0021
## 140 0.5841 nan 0.2000 -0.0005
## 160 0.5450 nan 0.2000 -0.0039
## 180 0.5140 nan 0.2000 -0.0011
## 200 0.4945 nan 0.2000 -0.0031
## 220 0.4698 nan 0.2000 -0.0025
## 240 0.4509 nan 0.2000 -0.0009
## 260 0.4322 nan 0.2000 -0.0011
## 280 0.4134 nan 0.2000 -0.0027
## 300 0.3907 nan 0.2000 -0.0020
## 320 0.3735 nan 0.2000 -0.0011
## 340 0.3574 nan 0.2000 -0.0013
## 360 0.3421 nan 0.2000 -0.0010
## 380 0.3254 nan 0.2000 -0.0019
## 400 0.3097 nan 0.2000 -0.0009
## 420 0.2973 nan 0.2000 -0.0001
## 440 0.2870 nan 0.2000 -0.0019
## 460 0.2768 nan 0.2000 -0.0019
## 480 0.2643 nan 0.2000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2120 nan 0.2000 0.0315
## 2 1.1532 nan 0.2000 0.0289
## 3 1.1045 nan 0.2000 0.0190
## 4 1.0749 nan 0.2000 0.0140
## 5 1.0447 nan 0.2000 0.0106
## 6 1.0202 nan 0.2000 0.0078
## 7 0.9999 nan 0.2000 0.0069
## 8 0.9800 nan 0.2000 0.0049
## 9 0.9623 nan 0.2000 0.0053
## 10 0.9490 nan 0.2000 0.0026
## 20 0.8653 nan 0.2000 -0.0026
## 40 0.7769 nan 0.2000 -0.0041
## 60 0.7214 nan 0.2000 -0.0026
## 80 0.6720 nan 0.2000 -0.0026
## 100 0.6335 nan 0.2000 -0.0024
## 120 0.5987 nan 0.2000 -0.0020
## 140 0.5642 nan 0.2000 -0.0006
## 160 0.5405 nan 0.2000 -0.0007
## 180 0.5156 nan 0.2000 -0.0008
## 200 0.4875 nan 0.2000 -0.0033
## 220 0.4666 nan 0.2000 -0.0013
## 240 0.4431 nan 0.2000 -0.0019
## 260 0.4225 nan 0.2000 -0.0023
## 280 0.4007 nan 0.2000 -0.0008
## 300 0.3877 nan 0.2000 -0.0016
## 320 0.3694 nan 0.2000 -0.0003
## 340 0.3578 nan 0.2000 -0.0008
## 360 0.3434 nan 0.2000 -0.0014
## 380 0.3351 nan 0.2000 -0.0025
## 400 0.3214 nan 0.2000 -0.0012
## 420 0.3109 nan 0.2000 -0.0016
## 440 0.3020 nan 0.2000 -0.0034
## 460 0.2882 nan 0.2000 -0.0015
## 480 0.2752 nan 0.2000 -0.0012
## 500 0.2645 nan 0.2000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2229 nan 0.2000 0.0269
## 2 1.1601 nan 0.2000 0.0281
## 3 1.1140 nan 0.2000 0.0152
## 4 1.0750 nan 0.2000 0.0143
## 5 1.0386 nan 0.2000 0.0123
## 6 1.0164 nan 0.2000 0.0034
## 7 0.9970 nan 0.2000 0.0063
## 8 0.9768 nan 0.2000 0.0066
## 9 0.9687 nan 0.2000 -0.0021
## 10 0.9510 nan 0.2000 0.0042
## 20 0.8574 nan 0.2000 0.0001
## 40 0.7717 nan 0.2000 -0.0016
## 60 0.7239 nan 0.2000 -0.0030
## 80 0.6873 nan 0.2000 -0.0036
## 100 0.6569 nan 0.2000 -0.0044
## 120 0.6238 nan 0.2000 -0.0049
## 140 0.5842 nan 0.2000 -0.0030
## 160 0.5587 nan 0.2000 -0.0046
## 180 0.5365 nan 0.2000 -0.0032
## 200 0.5092 nan 0.2000 -0.0026
## 220 0.4842 nan 0.2000 -0.0010
## 240 0.4583 nan 0.2000 -0.0010
## 260 0.4418 nan 0.2000 -0.0024
## 280 0.4227 nan 0.2000 -0.0023
## 300 0.4027 nan 0.2000 -0.0009
## 320 0.3862 nan 0.2000 -0.0012
## 340 0.3729 nan 0.2000 -0.0010
## 360 0.3585 nan 0.2000 -0.0014
## 380 0.3473 nan 0.2000 -0.0018
## 400 0.3318 nan 0.2000 -0.0006
## 420 0.3201 nan 0.2000 -0.0006
## 440 0.3082 nan 0.2000 -0.0018
## 460 0.2921 nan 0.2000 -0.0008
## 480 0.2812 nan 0.2000 -0.0014
## 500 0.2759 nan 0.2000 -0.0021
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2052 nan 0.2000 0.0358
## 2 1.1289 nan 0.2000 0.0333
## 3 1.0769 nan 0.2000 0.0253
## 4 1.0380 nan 0.2000 0.0116
## 5 1.0037 nan 0.2000 0.0131
## 6 0.9729 nan 0.2000 0.0090
## 7 0.9528 nan 0.2000 0.0055
## 8 0.9328 nan 0.2000 0.0012
## 9 0.9171 nan 0.2000 0.0022
## 10 0.8982 nan 0.2000 0.0048
## 20 0.8062 nan 0.2000 -0.0026
## 40 0.7109 nan 0.2000 -0.0015
## 60 0.6343 nan 0.2000 -0.0049
## 80 0.5840 nan 0.2000 -0.0029
## 100 0.5290 nan 0.2000 -0.0029
## 120 0.4834 nan 0.2000 -0.0025
## 140 0.4381 nan 0.2000 -0.0037
## 160 0.3944 nan 0.2000 -0.0017
## 180 0.3631 nan 0.2000 -0.0011
## 200 0.3282 nan 0.2000 -0.0021
## 220 0.3011 nan 0.2000 -0.0014
## 240 0.2835 nan 0.2000 -0.0023
## 260 0.2629 nan 0.2000 -0.0010
## 280 0.2471 nan 0.2000 -0.0006
## 300 0.2314 nan 0.2000 -0.0023
## 320 0.2172 nan 0.2000 -0.0002
## 340 0.2036 nan 0.2000 -0.0008
## 360 0.1924 nan 0.2000 -0.0009
## 380 0.1776 nan 0.2000 -0.0001
## 400 0.1701 nan 0.2000 -0.0002
## 420 0.1588 nan 0.2000 -0.0012
## 440 0.1516 nan 0.2000 -0.0015
## 460 0.1423 nan 0.2000 -0.0003
## 480 0.1336 nan 0.2000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2071 nan 0.2000 0.0436
## 2 1.1261 nan 0.2000 0.0362
## 3 1.0734 nan 0.2000 0.0216
## 4 1.0359 nan 0.2000 0.0104
## 5 1.0044 nan 0.2000 0.0113
## 6 0.9803 nan 0.2000 0.0048
## 7 0.9534 nan 0.2000 0.0077
## 8 0.9361 nan 0.2000 0.0034
## 9 0.9221 nan 0.2000 0.0019
## 10 0.9044 nan 0.2000 0.0082
## 20 0.8159 nan 0.2000 -0.0014
## 40 0.7123 nan 0.2000 0.0001
## 60 0.6457 nan 0.2000 -0.0032
## 80 0.5779 nan 0.2000 -0.0025
## 100 0.5316 nan 0.2000 -0.0009
## 120 0.4940 nan 0.2000 -0.0066
## 140 0.4491 nan 0.2000 -0.0052
## 160 0.4129 nan 0.2000 -0.0024
## 180 0.3868 nan 0.2000 -0.0027
## 200 0.3547 nan 0.2000 -0.0014
## 220 0.3276 nan 0.2000 -0.0010
## 240 0.3068 nan 0.2000 -0.0008
## 260 0.2870 nan 0.2000 -0.0020
## 280 0.2615 nan 0.2000 -0.0011
## 300 0.2411 nan 0.2000 -0.0013
## 320 0.2250 nan 0.2000 -0.0017
## 340 0.2091 nan 0.2000 -0.0018
## 360 0.1940 nan 0.2000 -0.0016
## 380 0.1837 nan 0.2000 -0.0006
## 400 0.1727 nan 0.2000 -0.0005
## 420 0.1619 nan 0.2000 -0.0007
## 440 0.1524 nan 0.2000 -0.0005
## 460 0.1418 nan 0.2000 -0.0003
## 480 0.1346 nan 0.2000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1885 nan 0.2000 0.0441
## 2 1.1268 nan 0.2000 0.0232
## 3 1.0714 nan 0.2000 0.0154
## 4 1.0342 nan 0.2000 0.0141
## 5 1.0032 nan 0.2000 0.0099
## 6 0.9759 nan 0.2000 0.0079
## 7 0.9539 nan 0.2000 0.0055
## 8 0.9333 nan 0.2000 0.0083
## 9 0.9178 nan 0.2000 0.0026
## 10 0.8998 nan 0.2000 0.0026
## 20 0.8147 nan 0.2000 -0.0009
## 40 0.7101 nan 0.2000 -0.0038
## 60 0.6326 nan 0.2000 -0.0021
## 80 0.5779 nan 0.2000 -0.0003
## 100 0.5199 nan 0.2000 -0.0024
## 120 0.4790 nan 0.2000 -0.0030
## 140 0.4413 nan 0.2000 -0.0024
## 160 0.4034 nan 0.2000 -0.0020
## 180 0.3666 nan 0.2000 -0.0019
## 200 0.3379 nan 0.2000 -0.0047
## 220 0.3135 nan 0.2000 -0.0009
## 240 0.2863 nan 0.2000 -0.0025
## 260 0.2652 nan 0.2000 -0.0017
## 280 0.2480 nan 0.2000 -0.0019
## 300 0.2322 nan 0.2000 -0.0013
## 320 0.2185 nan 0.2000 -0.0003
## 340 0.2052 nan 0.2000 -0.0020
## 360 0.1934 nan 0.2000 -0.0011
## 380 0.1804 nan 0.2000 -0.0009
## 400 0.1685 nan 0.2000 -0.0016
## 420 0.1588 nan 0.2000 -0.0011
## 440 0.1493 nan 0.2000 -0.0005
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## 480 0.1327 nan 0.2000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1968 nan 0.3000 0.0409
## 2 1.1473 nan 0.3000 0.0246
## 3 1.1050 nan 0.3000 0.0147
## 4 1.0681 nan 0.3000 0.0143
## 5 1.0435 nan 0.3000 0.0058
## 6 1.0172 nan 0.3000 0.0108
## 7 0.9943 nan 0.3000 0.0082
## 8 0.9751 nan 0.3000 0.0065
## 9 0.9606 nan 0.3000 0.0029
## 10 0.9494 nan 0.3000 0.0040
## 20 0.8870 nan 0.3000 -0.0046
## 40 0.8252 nan 0.3000 0.0010
## 60 0.7887 nan 0.3000 -0.0017
## 80 0.7597 nan 0.3000 -0.0030
## 100 0.7424 nan 0.3000 -0.0030
## 120 0.7264 nan 0.3000 -0.0030
## 140 0.7162 nan 0.3000 -0.0006
## 160 0.6991 nan 0.3000 -0.0019
## 180 0.6855 nan 0.3000 -0.0008
## 200 0.6746 nan 0.3000 -0.0038
## 220 0.6609 nan 0.3000 -0.0042
## 240 0.6516 nan 0.3000 -0.0045
## 260 0.6384 nan 0.3000 -0.0002
## 280 0.6323 nan 0.3000 -0.0007
## 300 0.6257 nan 0.3000 -0.0014
## 320 0.6172 nan 0.3000 -0.0052
## 340 0.6090 nan 0.3000 -0.0005
## 360 0.6044 nan 0.3000 -0.0032
## 380 0.5937 nan 0.3000 -0.0031
## 400 0.5862 nan 0.3000 -0.0032
## 420 0.5779 nan 0.3000 -0.0019
## 440 0.5708 nan 0.3000 -0.0042
## 460 0.5669 nan 0.3000 -0.0028
## 480 0.5589 nan 0.3000 -0.0033
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2058 nan 0.3000 0.0417
## 2 1.1479 nan 0.3000 0.0312
## 3 1.1014 nan 0.3000 0.0180
## 4 1.0763 nan 0.3000 0.0056
## 5 1.0440 nan 0.3000 0.0133
## 6 1.0122 nan 0.3000 0.0112
## 7 0.9956 nan 0.3000 0.0013
## 8 0.9810 nan 0.3000 0.0000
## 9 0.9656 nan 0.3000 0.0057
## 10 0.9537 nan 0.3000 0.0039
## 20 0.8833 nan 0.3000 -0.0023
## 40 0.8439 nan 0.3000 -0.0064
## 60 0.7999 nan 0.3000 -0.0024
## 80 0.7716 nan 0.3000 -0.0027
## 100 0.7544 nan 0.3000 -0.0003
## 120 0.7387 nan 0.3000 -0.0025
## 140 0.7311 nan 0.3000 -0.0055
## 160 0.7093 nan 0.3000 -0.0018
## 180 0.6921 nan 0.3000 -0.0005
## 200 0.6834 nan 0.3000 -0.0011
## 220 0.6740 nan 0.3000 -0.0039
## 240 0.6638 nan 0.3000 -0.0010
## 260 0.6550 nan 0.3000 -0.0061
## 280 0.6423 nan 0.3000 -0.0018
## 300 0.6315 nan 0.3000 -0.0041
## 320 0.6298 nan 0.3000 -0.0025
## 340 0.6211 nan 0.3000 -0.0018
## 360 0.6158 nan 0.3000 -0.0040
## 380 0.6094 nan 0.3000 -0.0076
## 400 0.5980 nan 0.3000 -0.0013
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## 440 0.5861 nan 0.3000 -0.0041
## 460 0.5849 nan 0.3000 -0.0039
## 480 0.5809 nan 0.3000 -0.0024
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2040 nan 0.3000 0.0449
## 2 1.1471 nan 0.3000 0.0257
## 3 1.1034 nan 0.3000 0.0156
## 4 1.0656 nan 0.3000 0.0100
## 5 1.0271 nan 0.3000 0.0134
## 6 1.0128 nan 0.3000 0.0009
## 7 0.9940 nan 0.3000 0.0012
## 8 0.9778 nan 0.3000 -0.0000
## 9 0.9590 nan 0.3000 0.0066
## 10 0.9470 nan 0.3000 0.0034
## 20 0.8921 nan 0.3000 -0.0034
## 40 0.8279 nan 0.3000 -0.0003
## 60 0.7985 nan 0.3000 -0.0010
## 80 0.7793 nan 0.3000 -0.0013
## 100 0.7655 nan 0.3000 -0.0003
## 120 0.7474 nan 0.3000 -0.0015
## 140 0.7281 nan 0.3000 -0.0068
## 160 0.7035 nan 0.3000 -0.0027
## 180 0.6919 nan 0.3000 -0.0026
## 200 0.6806 nan 0.3000 -0.0049
## 220 0.6709 nan 0.3000 -0.0020
## 240 0.6603 nan 0.3000 -0.0027
## 260 0.6555 nan 0.3000 -0.0031
## 280 0.6421 nan 0.3000 -0.0052
## 300 0.6377 nan 0.3000 -0.0048
## 320 0.6309 nan 0.3000 -0.0012
## 340 0.6195 nan 0.3000 -0.0024
## 360 0.6164 nan 0.3000 -0.0063
## 380 0.6085 nan 0.3000 -0.0029
## 400 0.5993 nan 0.3000 -0.0038
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## 440 0.5892 nan 0.3000 -0.0021
## 460 0.5795 nan 0.3000 -0.0047
## 480 0.5719 nan 0.3000 -0.0033
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1860 nan 0.3000 0.0428
## 2 1.1066 nan 0.3000 0.0318
## 3 1.0503 nan 0.3000 0.0238
## 4 1.0096 nan 0.3000 0.0114
## 5 0.9795 nan 0.3000 0.0112
## 6 0.9464 nan 0.3000 0.0096
## 7 0.9230 nan 0.3000 0.0062
## 8 0.9108 nan 0.3000 0.0007
## 9 0.8943 nan 0.3000 0.0028
## 10 0.8822 nan 0.3000 -0.0025
## 20 0.8036 nan 0.3000 -0.0012
## 40 0.7225 nan 0.3000 -0.0039
## 60 0.6673 nan 0.3000 -0.0033
## 80 0.6140 nan 0.3000 -0.0029
## 100 0.5674 nan 0.3000 -0.0086
## 120 0.5204 nan 0.3000 -0.0051
## 140 0.4811 nan 0.3000 -0.0036
## 160 0.4475 nan 0.3000 -0.0033
## 180 0.4142 nan 0.3000 -0.0037
## 200 0.3902 nan 0.3000 -0.0041
## 220 0.3650 nan 0.3000 -0.0007
## 240 0.3471 nan 0.3000 -0.0035
## 260 0.3224 nan 0.3000 -0.0019
## 280 0.3023 nan 0.3000 -0.0022
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## 320 0.2721 nan 0.3000 -0.0019
## 340 0.2575 nan 0.3000 -0.0030
## 360 0.2446 nan 0.3000 -0.0030
## 380 0.2313 nan 0.3000 -0.0023
## 400 0.2192 nan 0.3000 -0.0014
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## 440 0.1965 nan 0.3000 -0.0009
## 460 0.1897 nan 0.3000 -0.0018
## 480 0.1798 nan 0.3000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1887 nan 0.3000 0.0514
## 2 1.1216 nan 0.3000 0.0290
## 3 1.0698 nan 0.3000 0.0155
## 4 1.0274 nan 0.3000 0.0183
## 5 0.9922 nan 0.3000 0.0124
## 6 0.9730 nan 0.3000 -0.0025
## 7 0.9525 nan 0.3000 0.0059
## 8 0.9292 nan 0.3000 0.0097
## 9 0.9151 nan 0.3000 0.0020
## 10 0.8972 nan 0.3000 0.0043
## 20 0.8174 nan 0.3000 0.0005
## 40 0.7458 nan 0.3000 -0.0022
## 60 0.6753 nan 0.3000 -0.0037
## 80 0.6175 nan 0.3000 -0.0011
## 100 0.5818 nan 0.3000 -0.0036
## 120 0.5439 nan 0.3000 -0.0072
## 140 0.5130 nan 0.3000 -0.0027
## 160 0.4817 nan 0.3000 -0.0059
## 180 0.4495 nan 0.3000 -0.0038
## 200 0.4236 nan 0.3000 -0.0012
## 220 0.4044 nan 0.3000 -0.0043
## 240 0.3857 nan 0.3000 -0.0002
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## 280 0.3304 nan 0.3000 -0.0014
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## 360 0.2497 nan 0.3000 -0.0014
## 380 0.2322 nan 0.3000 -0.0011
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## 440 0.1969 nan 0.3000 -0.0013
## 460 0.1840 nan 0.3000 -0.0019
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1742 nan 0.3000 0.0513
## 2 1.1012 nan 0.3000 0.0303
## 3 1.0455 nan 0.3000 0.0225
## 4 1.0062 nan 0.3000 0.0164
## 5 0.9746 nan 0.3000 0.0089
## 6 0.9666 nan 0.3000 -0.0059
## 7 0.9454 nan 0.3000 0.0051
## 8 0.9211 nan 0.3000 0.0014
## 9 0.9097 nan 0.3000 0.0005
## 10 0.8998 nan 0.3000 -0.0013
## 20 0.8145 nan 0.3000 0.0044
## 40 0.7350 nan 0.3000 -0.0014
## 60 0.6639 nan 0.3000 -0.0069
## 80 0.6251 nan 0.3000 -0.0024
## 100 0.5675 nan 0.3000 -0.0029
## 120 0.5257 nan 0.3000 -0.0011
## 140 0.4930 nan 0.3000 -0.0027
## 160 0.4660 nan 0.3000 -0.0043
## 180 0.4356 nan 0.3000 -0.0031
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## 220 0.3896 nan 0.3000 -0.0008
## 240 0.3654 nan 0.3000 -0.0010
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## 460 0.1935 nan 0.3000 -0.0020
## 480 0.1862 nan 0.3000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1659 nan 0.3000 0.0424
## 2 1.0755 nan 0.3000 0.0405
## 3 1.0066 nan 0.3000 0.0276
## 4 0.9715 nan 0.3000 0.0103
## 5 0.9369 nan 0.3000 0.0124
## 6 0.9176 nan 0.3000 0.0029
## 7 0.8972 nan 0.3000 0.0045
## 8 0.8838 nan 0.3000 0.0003
## 9 0.8739 nan 0.3000 -0.0043
## 10 0.8609 nan 0.3000 0.0004
## 20 0.7637 nan 0.3000 0.0024
## 40 0.6389 nan 0.3000 -0.0077
## 60 0.5524 nan 0.3000 -0.0038
## 80 0.7896 nan 0.3000 -0.0036
## 100 0.7534 nan 0.3000 -0.0019
## 120 0.7089 nan 0.3000 -0.0044
## 140 0.6808 nan 0.3000 -0.0015
## 160 0.6385 nan 0.3000 -0.0022
## 180 0.6077 nan 0.3000 -0.0027
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## 220 0.5592 nan 0.3000 -0.0013
## 240 0.5430 nan 0.3000 -0.0008
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## 320 0.4756 nan 0.3000 -0.0028
## 340 0.4609 nan 0.3000 -0.0019
## 360 0.4518 nan 0.3000 -0.0009
## 380 0.4412 nan 0.3000 -0.0003
## 400 0.4329 nan 0.3000 -0.0001
## 420 0.4253 nan 0.3000 -0.0011
## 440 0.4172 nan 0.3000 -0.0011
## 460 0.4087 nan 0.3000 -0.0009
## 480 0.4033 nan 0.3000 -0.0013
## 500 0.3961 nan 0.3000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1585 nan 0.3000 0.0680
## 2 1.0757 nan 0.3000 0.0408
## 3 1.0192 nan 0.3000 0.0079
## 4 0.9794 nan 0.3000 0.0111
## 5 0.9444 nan 0.3000 0.0095
## 6 0.9245 nan 0.3000 0.0001
## 7 0.8967 nan 0.3000 -0.0003
## 8 0.8801 nan 0.3000 0.0019
## 9 0.8639 nan 0.3000 0.0010
## 10 0.8491 nan 0.3000 -0.0029
## 20 0.7700 nan 0.3000 -0.0088
## 40 0.6317 nan 0.3000 -0.0035
## 60 0.5545 nan 0.3000 -0.0053
## 80 0.4886 nan 0.3000 -0.0039
## 100 0.4333 nan 0.3000 -0.0020
## 120 0.3907 nan 0.3000 -0.0032
## 140 0.3464 nan 0.3000 -0.0019
## 160 0.3108 nan 0.3000 -0.0024
## 180 0.2801 nan 0.3000 -0.0016
## 200 0.2503 nan 0.3000 -0.0035
## 220 0.2232 nan 0.3000 -0.0023
## 240 0.2058 nan 0.3000 -0.0018
## 260 0.1851 nan 0.3000 -0.0016
## 280 0.1640 nan 0.3000 -0.0016
## 300 0.1446 nan 0.3000 -0.0007
## 320 0.1307 nan 0.3000 -0.0008
## 340 0.1203 nan 0.3000 -0.0006
## 360 0.1096 nan 0.3000 -0.0010
## 380 0.1003 nan 0.3000 -0.0003
## 400 0.0930 nan 0.3000 -0.0006
## 420 0.0854 nan 0.3000 -0.0005
## 440 0.0790 nan 0.3000 -0.0003
## 460 0.0719 nan 0.3000 -0.0005
## 480 0.0661 nan 0.3000 -0.0007
## 500 0.0616 nan 0.3000 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1535 nan 0.3000 0.0562
## 2 1.0718 nan 0.3000 0.0318
## 3 1.0140 nan 0.3000 0.0160
## 4 0.9742 nan 0.3000 0.0157
## 5 0.9427 nan 0.3000 0.0032
## 6 0.9256 nan 0.3000 -0.0058
## 7 0.9014 nan 0.3000 0.0043
## 8 0.8936 nan 0.3000 -0.0067
## 9 0.8841 nan 0.3000 -0.0033
## 10 0.8731 nan 0.3000 -0.0047
## 20 0.7717 nan 0.3000 -0.0067
## 40 0.6367 nan 0.3000 -0.0021
## 60 0.5568 nan 0.3000 -0.0061
## 80 0.4835 nan 0.3000 -0.0046
## 100 0.4277 nan 0.3000 -0.0024
## 120 0.3776 nan 0.3000 -0.0030
## 140 0.3381 nan 0.3000 -0.0030
## 160 0.2960 nan 0.3000 -0.0024
## 180 0.2593 nan 0.3000 -0.0005
## 200 0.2347 nan 0.3000 -0.0005
## 220 0.2124 nan 0.3000 -0.0007
## 240 0.1892 nan 0.3000 -0.0008
## 260 0.1693 nan 0.3000 -0.0014
## 280 0.1532 nan 0.3000 -0.0009
## 300 0.1414 nan 0.3000 -0.0014
## 320 0.1281 nan 0.3000 -0.0006
## 340 0.1180 nan 0.3000 -0.0013
## 360 0.1087 nan 0.3000 -0.0008
## 380 0.0981 nan 0.3000 -0.0011
## 400 0.0895 nan 0.3000 -0.0003
## 420 0.0816 nan 0.3000 -0.0001
## 440 0.0753 nan 0.3000 -0.0004
## 460 0.0713 nan 0.3000 -0.0010
## 480 0.0651 nan 0.3000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1825 nan 0.5000 0.0525
## 2 1.1093 nan 0.5000 0.0255
## 3 1.0472 nan 0.5000 0.0258
## 4 1.0027 nan 0.5000 0.0199
## 5 0.9767 nan 0.5000 0.0058
## 6 0.9643 nan 0.5000 -0.0002
## 7 0.9442 nan 0.5000 0.0058
## 8 0.9366 nan 0.5000 -0.0028
## 9 0.9254 nan 0.5000 0.0036
## 10 0.9144 nan 0.5000 0.0017
## 20 0.8590 nan 0.5000 -0.0043
## 40 0.7871 nan 0.5000 -0.0038
## 60 0.7516 nan 0.5000 -0.0006
## 80 0.7325 nan 0.5000 -0.0054
## 100 0.7046 nan 0.5000 -0.0027
## 120 0.7050 nan 0.5000 -0.0075
## 140 0.6830 nan 0.5000 -0.0001
## 160 0.6526 nan 0.5000 -0.0025
## 180 0.6400 nan 0.5000 -0.0032
## 200 0.6202 nan 0.5000 -0.0048
## 220 0.6093 nan 0.5000 -0.0026
## 240 0.5897 nan 0.5000 -0.0054
## 260 0.5821 nan 0.5000 -0.0028
## 280 0.5686 nan 0.5000 -0.0041
## 300 0.5558 nan 0.5000 -0.0001
## 320 0.5474 nan 0.5000 -0.0018
## 340 0.5419 nan 0.5000 -0.0027
## 360 0.5376 nan 0.5000 -0.0046
## 380 0.5256 nan 0.5000 -0.0017
## 400 0.5123 nan 0.5000 -0.0030
## 420 0.5052 nan 0.5000 -0.0053
## 440 0.4976 nan 0.5000 -0.0075
## 460 0.4842 nan 0.5000 -0.0011
## 480 0.4846 nan 0.5000 -0.0054
## 500 0.4740 nan 0.5000 -0.0036
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1675 nan 0.5000 0.0621
## 2 1.1046 nan 0.5000 0.0249
## 3 1.0431 nan 0.5000 0.0163
## 4 0.9914 nan 0.5000 0.0215
## 5 0.9766 nan 0.5000 0.0000
## 6 0.9599 nan 0.5000 0.0036
## 7 0.9504 nan 0.5000 -0.0018
## 8 0.9393 nan 0.5000 0.0013
## 9 0.9149 nan 0.5000 0.0101
## 10 0.9101 nan 0.5000 -0.0020
## 20 0.8456 nan 0.5000 -0.0020
## 40 0.8006 nan 0.5000 -0.0052
## 60 0.7430 nan 0.5000 -0.0002
## 80 0.7187 nan 0.5000 -0.0065
## 100 0.6974 nan 0.5000 -0.0035
## 120 0.6825 nan 0.5000 -0.0027
## 140 0.6560 nan 0.5000 -0.0053
## 160 0.6557 nan 0.5000 -0.0100
## 180 0.6383 nan 0.5000 -0.0046
## 200 0.6236 nan 0.5000 -0.0074
## 220 0.6095 nan 0.5000 -0.0074
## 240 0.5936 nan 0.5000 -0.0049
## 260 0.5861 nan 0.5000 -0.0087
## 280 0.5672 nan 0.5000 -0.0040
## 300 0.5523 nan 0.5000 -0.0062
## 320 0.5441 nan 0.5000 -0.0041
## 340 0.5318 nan 0.5000 -0.0071
## 360 0.5237 nan 0.5000 -0.0043
## 380 0.5156 nan 0.5000 -0.0079
## 400 0.5032 nan 0.5000 -0.0047
## 420 0.4973 nan 0.5000 -0.0033
## 440 0.4866 nan 0.5000 -0.0063
## 460 0.4730 nan 0.5000 -0.0026
## 480 0.4708 nan 0.5000 -0.0061
## 500 0.4705 nan 0.5000 -0.0039
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1589 nan 0.5000 0.0613
## 2 1.0993 nan 0.5000 0.0212
## 3 1.0445 nan 0.5000 0.0099
## 4 0.9979 nan 0.5000 0.0159
## 5 0.9794 nan 0.5000 0.0011
## 6 0.9602 nan 0.5000 0.0041
## 7 0.9453 nan 0.5000 0.0029
## 8 0.9321 nan 0.5000 0.0025
## 9 0.9169 nan 0.5000 0.0045
## 10 0.9110 nan 0.5000 -0.0031
## 20 0.8550 nan 0.5000 -0.0031
## 40 0.8072 nan 0.5000 -0.0064
## 60 0.7914 nan 0.5000 -0.0122
## 80 0.7605 nan 0.5000 0.0006
## 100 0.7313 nan 0.5000 -0.0028
## 120 0.7099 nan 0.5000 -0.0056
## 140 0.6834 nan 0.5000 -0.0017
## 160 0.6591 nan 0.5000 -0.0026
## 180 0.6409 nan 0.5000 -0.0060
## 200 0.6316 nan 0.5000 -0.0042
## 220 0.6245 nan 0.5000 -0.0034
## 240 0.6135 nan 0.5000 -0.0074
## 260 0.5986 nan 0.5000 -0.0004
## 280 0.5897 nan 0.5000 -0.0042
## 300 0.5747 nan 0.5000 -0.0061
## 320 0.5831 nan 0.5000 -0.0057
## 340 0.5652 nan 0.5000 -0.0039
## 360 0.5581 nan 0.5000 -0.0031
## 380 0.5455 nan 0.5000 -0.0030
## 400 0.5373 nan 0.5000 -0.0092
## 420 0.5408 nan 0.5000 -0.0033
## 440 0.5345 nan 0.5000 -0.0009
## 460 0.5187 nan 0.5000 -0.0028
## 480 0.5074 nan 0.5000 -0.0016
## 500 0.4932 nan 0.5000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1332 nan 0.5000 0.0730
## 2 1.0524 nan 0.5000 0.0302
## 3 0.9787 nan 0.5000 0.0164
## 4 0.9468 nan 0.5000 -0.0020
## 5 0.9244 nan 0.5000 -0.0020
## 6 0.9003 nan 0.5000 0.0023
## 7 0.8720 nan 0.5000 0.0074
## 8 0.8645 nan 0.5000 -0.0021
## 9 0.8500 nan 0.5000 -0.0043
## 10 0.8352 nan 0.5000 0.0008
## 20 0.7728 nan 0.5000 -0.0107
## 40 0.6945 nan 0.5000 -0.0056
## 60 0.6359 nan 0.5000 -0.0063
## 80 0.5822 nan 0.5000 -0.0078
## 100 0.5296 nan 0.5000 -0.0041
## 120 0.4773 nan 0.5000 -0.0069
## 140 0.4358 nan 0.5000 -0.0029
## 160 0.3869 nan 0.5000 -0.0107
## 180 0.3515 nan 0.5000 -0.0044
## 200 0.3253 nan 0.5000 -0.0049
## 220 0.3022 nan 0.5000 -0.0016
## 240 0.2750 nan 0.5000 -0.0013
## 260 0.2538 nan 0.5000 -0.0044
## 280 0.2309 nan 0.5000 -0.0057
## 300 0.2136 nan 0.5000 -0.0007
## 320 0.1954 nan 0.5000 -0.0018
## 340 0.1806 nan 0.5000 -0.0015
## 360 0.1647 nan 0.5000 -0.0037
## 380 0.1545 nan 0.5000 -0.0059
## 400 0.1442 nan 0.5000 -0.0039
## 420 0.1348 nan 0.5000 -0.0022
## 440 0.1232 nan 0.5000 -0.0016
## 460 0.1114 nan 0.5000 -0.0004
## 480 0.1051 nan 0.5000 -0.0024
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1283 nan 0.5000 0.0821
## 2 1.0481 nan 0.5000 0.0300
## 3 0.9882 nan 0.5000 0.0164
## 4 0.9544 nan 0.5000 0.0029
## 5 0.9245 nan 0.5000 0.0040
## 6 0.8926 nan 0.5000 0.0072
## 7 0.8832 nan 0.5000 -0.0050
## 8 0.8763 nan 0.5000 -0.0035
## 9 0.8698 nan 0.5000 -0.0150
## 10 0.8670 nan 0.5000 -0.0228
## 20 0.8026 nan 0.5000 -0.0029
## 40 0.7026 nan 0.5000 -0.0123
## 60 0.6268 nan 0.5000 -0.0063
## 80 0.5574 nan 0.5000 -0.0135
## 100 0.5001 nan 0.5000 -0.0046
## 120 0.4491 nan 0.5000 -0.0046
## 140 0.4245 nan 0.5000 -0.0122
## 160 0.3717 nan 0.5000 -0.0041
## 180 2.4636 nan 0.5000 -0.0033
## 200 2.4349 nan 0.5000 -0.0032
## 220 2.4107 nan 0.5000 -0.0005
## 240 2.3818 nan 0.5000 -0.0012
## 260 2.3641 nan 0.5000 -0.0025
## 280 2.3491 nan 0.5000 -0.0020
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1151 nan 0.5000 0.0854
## 2 1.0363 nan 0.5000 0.0265
## 3 0.9806 nan 0.5000 0.0247
## 4 0.9561 nan 0.5000 0.0027
## 5 0.9237 nan 0.5000 0.0047
## 6 0.9109 nan 0.5000 -0.0028
## 7 0.8965 nan 0.5000 -0.0020
## 8 0.8776 nan 0.5000 -0.0103
## 9 0.8777 nan 0.5000 -0.0109
## 10 0.8639 nan 0.5000 -0.0079
## 20 0.7820 nan 0.5000 -0.0082
## 40 0.6599 nan 0.5000 -0.0102
## 60 0.5801 nan 0.5000 -0.0055
## 80 0.5293 nan 0.5000 -0.0033
## 100 0.4655 nan 0.5000 -0.0077
## 120 0.4163 nan 0.5000 -0.0050
## 140 0.3786 nan 0.5000 -0.0183
## 160 0.3321 nan 0.5000 -0.0016
## 180 0.3149 nan 0.5000 -0.0065
## 200 0.2702 nan 0.5000 -0.0046
## 220 0.2379 nan 0.5000 -0.0023
## 240 0.2124 nan 0.5000 0.0000
## 260 0.1946 nan 0.5000 -0.0014
## 280 0.1806 nan 0.5000 -0.0038
## 300 0.1614 nan 0.5000 -0.0025
## 320 0.1500 nan 0.5000 -0.0012
## 340 0.1378 nan 0.5000 -0.0010
## 360 0.1239 nan 0.5000 -0.0014
## 380 0.1150 nan 0.5000 -0.0014
## 400 0.1053 nan 0.5000 -0.0004
## 420 0.0976 nan 0.5000 -0.0022
## 440 0.0892 nan 0.5000 -0.0007
## 460 0.0829 nan 0.5000 -0.0016
## 480 0.0755 nan 0.5000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1028 nan 0.5000 0.0668
## 2 1.0203 nan 0.5000 0.0151
## 3 0.9526 nan 0.5000 0.0206
## 4 0.9124 nan 0.5000 0.0112
## 5 0.8847 nan 0.5000 0.0048
## 6 0.8653 nan 0.5000 -0.0035
## 7 0.8601 nan 0.5000 -0.0127
## 8 0.8413 nan 0.5000 0.0014
## 9 0.8295 nan 0.5000 -0.0070
## 10 0.8100 nan 0.5000 -0.0013
## 20 0.7436 nan 0.5000 -0.0278
## 40 0.9243 nan 0.5000 -0.0096
## 60 0.8715 nan 0.5000 -0.0052
## 80 0.8021 nan 0.5000 -0.0079
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 -0.0045
## 140 inf nan 0.5000 0.0158
## 160 inf nan 0.5000 -0.0011
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
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## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1038 nan 0.5000 0.0846
## 2 1.0199 nan 0.5000 0.0298
## 3 0.9722 nan 0.5000 0.0101
## 4 0.9332 nan 0.5000 0.0009
## 5 0.9069 nan 0.5000 -0.0107
## 6 0.8788 nan 0.5000 0.0027
## 7 0.8601 nan 0.5000 -0.0148
## 8 0.8600 nan 0.5000 -0.0174
## 9 0.8439 nan 0.5000 -0.0078
## 10 0.8352 nan 0.5000 -0.0077
## 20 0.7398 nan 0.5000 -0.0061
## 40 0.6265 nan 0.5000 -0.0053
## 60 0.5152 nan 0.5000 -0.0116
## 80 0.4478 nan 0.5000 -0.0233
## 100 0.3240 nan 0.5000 -0.0052
## 120 0.2625 nan 0.5000 -0.0113
## 140 0.2184 nan 0.5000 -0.0033
## 160 0.1822 nan 0.5000 -0.0030
## 180 0.1523 nan 0.5000 -0.0021
## 200 0.1320 nan 0.5000 -0.0027
## 220 0.1145 nan 0.5000 -0.0006
## 240 0.1025 nan 0.5000 -0.0006
## 260 0.0908 nan 0.5000 -0.0015
## 280 0.0786 nan 0.5000 -0.0011
## 300 0.0659 nan 0.5000 -0.0010
## 320 0.0542 nan 0.5000 -0.0011
## 340 0.0473 nan 0.5000 -0.0008
## 360 0.0414 nan 0.5000 -0.0003
## 380 0.0368 nan 0.5000 -0.0003
## 400 0.0323 nan 0.5000 -0.0007
## 420 0.0284 nan 0.5000 -0.0003
## 440 0.0256 nan 0.5000 -0.0003
## 460 0.0231 nan 0.5000 -0.0005
## 480 0.0207 nan 0.5000 -0.0005
## 500 0.0191 nan 0.5000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0790 nan 0.5000 0.1023
## 2 0.9803 nan 0.5000 0.0392
## 3 0.9446 nan 0.5000 0.0029
## 4 0.9131 nan 0.5000 0.0031
## 5 0.8851 nan 0.5000 -0.0017
## 6 0.8558 nan 0.5000 0.0046
## 7 0.8393 nan 0.5000 -0.0028
## 8 0.8349 nan 0.5000 -0.0154
## 9 0.8124 nan 0.5000 -0.0087
## 10 0.8082 nan 0.5000 -0.0124
## 20 0.7281 nan 0.5000 -0.0242
## 40 0.5771 nan 0.5000 0.0007
## 60 0.4912 nan 0.5000 -0.0100
## 80 0.3982 nan 0.5000 -0.0034
## 100 0.3341 nan 0.5000 -0.0040
## 120 0.2716 nan 0.5000 -0.0035
## 140 0.2289 nan 0.5000 -0.0043
## 160 0.1808 nan 0.5000 -0.0026
## 180 0.1494 nan 0.5000 -0.0061
## 200 0.1259 nan 0.5000 -0.0009
## 220 0.1083 nan 0.5000 -0.0016
## 240 0.0971 nan 0.5000 -0.0023
## 260 0.0821 nan 0.5000 -0.0005
## 280 0.0724 nan 0.5000 -0.0005
## 300 0.0627 nan 0.5000 -0.0009
## 320 0.0544 nan 0.5000 -0.0006
## 340 0.0449 nan 0.5000 -0.0005
## 360 0.0390 nan 0.5000 -0.0008
## 380 0.0347 nan 0.5000 -0.0004
## 400 0.0313 nan 0.5000 -0.0004
## 420 0.0283 nan 0.5000 -0.0005
## 440 0.0245 nan 0.5000 -0.0005
## 460 0.0222 nan 0.5000 -0.0002
## 480 0.0196 nan 0.5000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1382 nan 1.0000 0.0456
## 2 1.0647 nan 1.0000 0.0250
## 3 1.0083 nan 1.0000 0.0228
## 4 1.0073 nan 1.0000 -0.0351
## 5 0.9985 nan 1.0000 -0.0183
## 6 0.9820 nan 1.0000 0.0015
## 7 1.0001 nan 1.0000 -0.0355
## 8 1.0132 nan 1.0000 -0.0298
## 9 1.0482 nan 1.0000 -0.0527
## 10 1.0352 nan 1.0000 -0.0138
## 20 0.9584 nan 1.0000 -0.0109
## 40 815.8534 nan 1.0000 -0.0309
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## 360 inf nan 1.0000 nan
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## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1308 nan 1.0000 0.0533
## 2 1.0584 nan 1.0000 0.0227
## 3 0.9932 nan 1.0000 0.0276
## 4 0.9718 nan 1.0000 -0.0053
## 5 0.9512 nan 1.0000 0.0002
## 6 0.9130 nan 1.0000 0.0166
## 7 0.8915 nan 1.0000 0.0050
## 8 0.8981 nan 1.0000 -0.0228
## 9 0.9088 nan 1.0000 -0.0318
## 10 0.9282 nan 1.0000 -0.0361
## 20 0.9302 nan 1.0000 -0.0369
## 40 0.8716 nan 1.0000 -0.0085
## 60 0.8253 nan 1.0000 -0.0137
## 80 1.0821 nan 1.0000 -0.0231
## 100 1.3220 nan 1.0000 -0.0417
## 120 1.2639 nan 1.0000 -0.0006
## 140 1.2501 nan 1.0000 0.0044
## 160 1.1942 nan 1.0000 0.0190
## 180 78097103364806.5625 nan 1.0000 -0.0098
## 200 78097103364806.5156 nan 1.0000 -0.0003
## 220 78097103364806.4844 nan 1.0000 0.0000
## 240 78097103364806.5156 nan 1.0000 -0.0233
## 260 78097103364806.5156 nan 1.0000 0.0005
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1167 nan 1.0000 0.0831
## 2 1.0419 nan 1.0000 0.0380
## 3 0.9908 nan 1.0000 0.0236
## 4 0.9917 nan 1.0000 -0.0167
## 5 0.9664 nan 1.0000 0.0057
## 6 0.9589 nan 1.0000 -0.0172
## 7 0.9616 nan 1.0000 -0.0207
## 8 0.9463 nan 1.0000 0.0029
## 9 0.9403 nan 1.0000 -0.0133
## 10 1.0631 nan 1.0000 -0.1246
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0598 nan 1.0000 0.1045
## 2 0.9947 nan 1.0000 0.0020
## 3 0.9612 nan 1.0000 0.0035
## 4 0.9410 nan 1.0000 -0.0339
## 5 0.9476 nan 1.0000 -0.0347
## 6 0.9557 nan 1.0000 -0.0436
## 7 0.9229 nan 1.0000 0.0012
## 8 0.8753 nan 1.0000 0.0074
## 9 0.8798 nan 1.0000 -0.0419
## 10 0.8735 nan 1.0000 -0.0348
## 20 3.8680 nan 1.0000 -0.0511
## 40 285500944196332567882882242806244086288440602442808646088286286206860608448608268686664888600880622482026826642628008226640.0000 nan 1.0000 0.0286
## 60 inf nan 1.0000 0.0021
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0902 nan 1.0000 0.0838
## 2 1.0118 nan 1.0000 0.0272
## 3 0.9694 nan 1.0000 0.0114
## 4 0.9320 nan 1.0000 -0.0046
## 5 0.9370 nan 1.0000 -0.0325
## 6 0.8878 nan 1.0000 0.0093
## 7 0.8823 nan 1.0000 -0.0487
## 8 0.8464 nan 1.0000 0.0035
## 9 0.8398 nan 1.0000 -0.0162
## 10 0.8347 nan 1.0000 -0.0349
## 20 0.8021 nan 1.0000 -0.0382
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0768 nan 1.0000 0.0904
## 2 0.9958 nan 1.0000 0.0203
## 3 0.9475 nan 1.0000 0.0173
## 4 0.9598 nan 1.0000 -0.0349
## 5 0.9413 nan 1.0000 -0.0281
## 6 0.9974 nan 1.0000 -0.0638
## 7 0.9811 nan 1.0000 -0.0169
## 8 1.1285 nan 1.0000 -0.1598
## 9 1.0966 nan 1.0000 -0.0142
## 10 1.1016 nan 1.0000 -0.0460
## 20 11.3047 nan 1.0000 -0.0326
## 40 353.1651 nan 1.0000 -0.1139
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0356 nan 1.0000 0.1158
## 2 0.9822 nan 1.0000 -0.0219
## 3 0.9385 nan 1.0000 -0.0199
## 4 0.9529 nan 1.0000 -0.0571
## 5 1.0267 nan 1.0000 -0.1162
## 6 0.9601 nan 1.0000 0.0036
## 7 0.9700 nan 1.0000 -0.0611
## 8 0.9235 nan 1.0000 -0.0014
## 9 1.0007 nan 1.0000 -0.1332
## 10 0.9852 nan 1.0000 -0.0492
## 20 2.2210 nan 1.0000 -0.0629
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0718 nan 1.0000 0.0901
## 2 0.9718 nan 1.0000 0.0124
## 3 0.9451 nan 1.0000 -0.0089
## 4 0.9314 nan 1.0000 -0.0192
## 5 0.9242 nan 1.0000 -0.0412
## 6 0.9078 nan 1.0000 -0.0427
## 7 0.8761 nan 1.0000 -0.0122
## 8 0.8709 nan 1.0000 -0.0317
## 9 0.8684 nan 1.0000 -0.0402
## 10 0.8902 nan 1.0000 -0.0543
## 20 0.8269 nan 1.0000 -0.0252
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0433 nan 1.0000 0.1059
## 2 0.9669 nan 1.0000 0.0247
## 3 0.9227 nan 1.0000 0.0007
## 4 0.9029 nan 1.0000 -0.0140
## 5 0.8680 nan 1.0000 -0.0115
## 6 0.8480 nan 1.0000 -0.0095
## 7 0.8419 nan 1.0000 -0.0313
## 8 0.8454 nan 1.0000 -0.0477
## 9 0.8619 nan 1.0000 -0.0426
## 10 0.8474 nan 1.0000 -0.0180
## 20 1.0227 nan 1.0000 -0.0480
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
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## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2859 nan 0.0010 0.0002
## 40 1.2786 nan 0.0010 0.0002
## 60 1.2716 nan 0.0010 0.0002
## 80 1.2647 nan 0.0010 0.0001
## 100 1.2581 nan 0.0010 0.0001
## 120 1.2520 nan 0.0010 0.0001
## 140 1.2457 nan 0.0010 0.0001
## 160 1.2396 nan 0.0010 0.0001
## 180 1.2339 nan 0.0010 0.0001
## 200 1.2285 nan 0.0010 0.0001
## 220 1.2231 nan 0.0010 0.0001
## 240 1.2179 nan 0.0010 0.0001
## 260 1.2128 nan 0.0010 0.0001
## 280 1.2078 nan 0.0010 0.0001
## 300 1.2031 nan 0.0010 0.0001
## 320 1.1986 nan 0.0010 0.0001
## 340 1.1941 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2914 nan 0.0010 0.0002
## 6 1.2910 nan 0.0010 0.0002
## 7 1.2906 nan 0.0010 0.0002
## 8 1.2903 nan 0.0010 0.0002
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## 10 1.2894 nan 0.0010 0.0002
## 20 1.2856 nan 0.0010 0.0002
## 40 1.2782 nan 0.0010 0.0002
## 60 1.2714 nan 0.0010 0.0002
## 80 1.2647 nan 0.0010 0.0002
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## 280 1.2082 nan 0.0010 0.0001
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## 320 1.1991 nan 0.0010 0.0001
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## 380 1.1859 nan 0.0010 0.0001
## 400 1.1817 nan 0.0010 0.0001
## 420 1.1775 nan 0.0010 0.0001
## 440 1.1735 nan 0.0010 0.0001
## 460 1.1697 nan 0.0010 0.0001
## 480 1.1659 nan 0.0010 0.0001
## 500 1.1623 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2921 nan 0.0010 0.0002
## 4 1.2917 nan 0.0010 0.0002
## 5 1.2913 nan 0.0010 0.0002
## 6 1.2909 nan 0.0010 0.0002
## 7 1.2905 nan 0.0010 0.0002
## 8 1.2901 nan 0.0010 0.0002
## 9 1.2897 nan 0.0010 0.0002
## 10 1.2893 nan 0.0010 0.0002
## 20 1.2854 nan 0.0010 0.0002
## 40 1.2782 nan 0.0010 0.0001
## 60 1.2711 nan 0.0010 0.0002
## 80 1.2643 nan 0.0010 0.0002
## 100 1.2578 nan 0.0010 0.0001
## 120 1.2515 nan 0.0010 0.0001
## 140 1.2455 nan 0.0010 0.0001
## 160 1.2396 nan 0.0010 0.0001
## 180 1.2338 nan 0.0010 0.0001
## 200 1.2282 nan 0.0010 0.0001
## 220 1.2227 nan 0.0010 0.0001
## 240 1.2178 nan 0.0010 0.0001
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## 320 1.1985 nan 0.0010 0.0001
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## 380 1.1857 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2913 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2899 nan 0.0010 0.0002
## 8 1.2894 nan 0.0010 0.0002
## 9 1.2890 nan 0.0010 0.0002
## 10 1.2885 nan 0.0010 0.0002
## 20 1.2838 nan 0.0010 0.0002
## 40 1.2743 nan 0.0010 0.0002
## 60 1.2653 nan 0.0010 0.0002
## 80 1.2565 nan 0.0010 0.0002
## 100 1.2480 nan 0.0010 0.0002
## 120 1.2397 nan 0.0010 0.0002
## 140 1.2316 nan 0.0010 0.0002
## 160 1.2239 nan 0.0010 0.0002
## 180 1.2165 nan 0.0010 0.0001
## 200 1.2089 nan 0.0010 0.0002
## 220 1.2019 nan 0.0010 0.0002
## 240 1.1950 nan 0.0010 0.0002
## 260 1.1884 nan 0.0010 0.0001
## 280 1.1820 nan 0.0010 0.0001
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## 380 1.1523 nan 0.0010 0.0001
## 400 1.1470 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0003
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2899 nan 0.0010 0.0002
## 8 1.2895 nan 0.0010 0.0002
## 9 1.2890 nan 0.0010 0.0002
## 10 1.2885 nan 0.0010 0.0002
## 20 1.2836 nan 0.0010 0.0002
## 40 1.2743 nan 0.0010 0.0002
## 60 1.2653 nan 0.0010 0.0002
## 80 1.2566 nan 0.0010 0.0002
## 100 1.2484 nan 0.0010 0.0002
## 120 1.2399 nan 0.0010 0.0002
## 140 1.2318 nan 0.0010 0.0002
## 160 1.2240 nan 0.0010 0.0002
## 180 1.2165 nan 0.0010 0.0002
## 200 1.2093 nan 0.0010 0.0002
## 220 1.2022 nan 0.0010 0.0002
## 240 1.1954 nan 0.0010 0.0001
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## 380 1.1525 nan 0.0010 0.0001
## 400 1.1472 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0003
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0003
## 8 1.2895 nan 0.0010 0.0002
## 9 1.2890 nan 0.0010 0.0002
## 10 1.2885 nan 0.0010 0.0002
## 20 1.2837 nan 0.0010 0.0002
## 40 1.2744 nan 0.0010 0.0002
## 60 1.2655 nan 0.0010 0.0002
## 80 1.2567 nan 0.0010 0.0002
## 100 1.2484 nan 0.0010 0.0002
## 120 1.2404 nan 0.0010 0.0002
## 140 1.2324 nan 0.0010 0.0002
## 160 1.2247 nan 0.0010 0.0002
## 180 1.2173 nan 0.0010 0.0002
## 200 1.2101 nan 0.0010 0.0002
## 220 1.2032 nan 0.0010 0.0001
## 240 1.1965 nan 0.0010 0.0001
## 260 1.1898 nan 0.0010 0.0002
## 280 1.1833 nan 0.0010 0.0001
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## 320 1.1709 nan 0.0010 0.0001
## 340 1.1649 nan 0.0010 0.0001
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## 380 1.1533 nan 0.0010 0.0001
## 400 1.1478 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0003
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2907 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0003
## 7 1.2895 nan 0.0010 0.0003
## 8 1.2889 nan 0.0010 0.0003
## 9 1.2884 nan 0.0010 0.0002
## 10 1.2879 nan 0.0010 0.0003
## 20 1.2823 nan 0.0010 0.0003
## 40 1.2715 nan 0.0010 0.0002
## 60 1.2612 nan 0.0010 0.0002
## 80 1.2510 nan 0.0010 0.0002
## 100 1.2413 nan 0.0010 0.0002
## 120 1.2319 nan 0.0010 0.0002
## 140 1.2228 nan 0.0010 0.0002
## 160 1.2139 nan 0.0010 0.0002
## 180 1.2054 nan 0.0010 0.0002
## 200 1.1973 nan 0.0010 0.0002
## 220 1.1893 nan 0.0010 0.0002
## 240 1.1815 nan 0.0010 0.0001
## 260 1.1739 nan 0.0010 0.0001
## 280 1.1665 nan 0.0010 0.0002
## 300 1.1594 nan 0.0010 0.0001
## 320 1.1526 nan 0.0010 0.0001
## 340 1.1459 nan 0.0010 0.0002
## 360 1.1395 nan 0.0010 0.0001
## 380 1.1330 nan 0.0010 0.0001
## 400 1.1268 nan 0.0010 0.0001
## 420 1.1208 nan 0.0010 0.0001
## 440 1.1150 nan 0.0010 0.0001
## 460 1.1092 nan 0.0010 0.0001
## 480 1.1036 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0003
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2907 nan 0.0010 0.0002
## 6 1.2902 nan 0.0010 0.0003
## 7 1.2896 nan 0.0010 0.0003
## 8 1.2890 nan 0.0010 0.0003
## 9 1.2884 nan 0.0010 0.0003
## 10 1.2878 nan 0.0010 0.0003
## 20 1.2823 nan 0.0010 0.0003
## 40 1.2716 nan 0.0010 0.0002
## 60 1.2612 nan 0.0010 0.0002
## 80 1.2515 nan 0.0010 0.0002
## 100 1.2414 nan 0.0010 0.0002
## 120 1.2320 nan 0.0010 0.0002
## 140 1.2230 nan 0.0010 0.0002
## 160 1.2142 nan 0.0010 0.0002
## 180 1.2058 nan 0.0010 0.0002
## 200 1.1975 nan 0.0010 0.0002
## 220 1.1895 nan 0.0010 0.0002
## 240 1.1817 nan 0.0010 0.0002
## 260 1.1741 nan 0.0010 0.0002
## 280 1.1668 nan 0.0010 0.0001
## 300 1.1597 nan 0.0010 0.0001
## 320 1.1526 nan 0.0010 0.0001
## 340 1.1458 nan 0.0010 0.0002
## 360 1.1394 nan 0.0010 0.0001
## 380 1.1331 nan 0.0010 0.0001
## 400 1.1270 nan 0.0010 0.0001
## 420 1.1209 nan 0.0010 0.0001
## 440 1.1150 nan 0.0010 0.0001
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## 480 1.1038 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2927 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0002
## 3 1.2916 nan 0.0010 0.0003
## 4 1.2910 nan 0.0010 0.0003
## 5 1.2905 nan 0.0010 0.0002
## 6 1.2899 nan 0.0010 0.0003
## 7 1.2893 nan 0.0010 0.0003
## 8 1.2886 nan 0.0010 0.0003
## 9 1.2881 nan 0.0010 0.0002
## 10 1.2875 nan 0.0010 0.0002
## 20 1.2820 nan 0.0010 0.0003
## 40 1.2713 nan 0.0010 0.0002
## 60 1.2612 nan 0.0010 0.0002
## 80 1.2510 nan 0.0010 0.0002
## 100 1.2411 nan 0.0010 0.0002
## 120 1.2314 nan 0.0010 0.0002
## 140 1.2221 nan 0.0010 0.0002
## 160 1.2133 nan 0.0010 0.0002
## 180 1.2049 nan 0.0010 0.0002
## 200 1.1965 nan 0.0010 0.0002
## 220 1.1885 nan 0.0010 0.0002
## 240 1.1809 nan 0.0010 0.0002
## 260 1.1734 nan 0.0010 0.0001
## 280 1.1659 nan 0.0010 0.0002
## 300 1.1587 nan 0.0010 0.0001
## 320 1.1520 nan 0.0010 0.0001
## 340 1.1454 nan 0.0010 0.0001
## 360 1.1387 nan 0.0010 0.0001
## 380 1.1321 nan 0.0010 0.0002
## 400 1.1259 nan 0.0010 0.0001
## 420 1.1197 nan 0.0010 0.0001
## 440 1.1138 nan 0.0010 0.0001
## 460 1.1080 nan 0.0010 0.0001
## 480 1.1025 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2547 nan 0.1000 0.0182
## 2 1.2264 nan 0.1000 0.0124
## 3 1.2008 nan 0.1000 0.0118
## 4 1.1806 nan 0.1000 0.0103
## 5 1.1620 nan 0.1000 0.0066
## 6 1.1439 nan 0.1000 0.0075
## 7 1.1264 nan 0.1000 0.0077
## 8 1.1133 nan 0.1000 0.0055
## 9 1.1014 nan 0.1000 0.0042
## 10 1.0897 nan 0.1000 0.0040
## 20 1.0035 nan 0.1000 0.0031
## 40 0.9130 nan 0.1000 0.0007
## 60 0.8755 nan 0.1000 -0.0006
## 80 0.8468 nan 0.1000 -0.0007
## 100 0.8327 nan 0.1000 -0.0016
## 120 0.8198 nan 0.1000 -0.0004
## 140 0.8089 nan 0.1000 -0.0027
## 160 0.8000 nan 0.1000 -0.0012
## 180 0.7909 nan 0.1000 -0.0005
## 200 0.7818 nan 0.1000 -0.0006
## 220 0.7772 nan 0.1000 -0.0014
## 240 0.7703 nan 0.1000 -0.0000
## 260 0.7627 nan 0.1000 -0.0009
## 280 0.7576 nan 0.1000 -0.0008
## 300 0.7512 nan 0.1000 -0.0006
## 320 0.7451 nan 0.1000 -0.0010
## 340 0.7394 nan 0.1000 -0.0011
## 360 0.7334 nan 0.1000 -0.0009
## 380 0.7281 nan 0.1000 -0.0007
## 400 0.7234 nan 0.1000 -0.0001
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## 460 0.7082 nan 0.1000 -0.0007
## 480 0.7040 nan 0.1000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2580 nan 0.1000 0.0159
## 2 1.2253 nan 0.1000 0.0149
## 3 1.2040 nan 0.1000 0.0078
## 4 1.1829 nan 0.1000 0.0092
## 5 1.1612 nan 0.1000 0.0090
## 6 1.1439 nan 0.1000 0.0070
## 7 1.1294 nan 0.1000 0.0069
## 8 1.1153 nan 0.1000 0.0069
## 9 1.1037 nan 0.1000 0.0051
## 10 1.0903 nan 0.1000 0.0050
## 20 0.9948 nan 0.1000 0.0018
## 40 0.9178 nan 0.1000 -0.0005
## 60 0.8775 nan 0.1000 -0.0010
## 80 0.8538 nan 0.1000 -0.0012
## 100 0.8402 nan 0.1000 -0.0013
## 120 0.8245 nan 0.1000 0.0001
## 140 0.8137 nan 0.1000 -0.0008
## 160 0.8060 nan 0.1000 -0.0003
## 180 0.7973 nan 0.1000 -0.0011
## 200 0.7888 nan 0.1000 -0.0025
## 220 0.7808 nan 0.1000 -0.0007
## 240 0.7750 nan 0.1000 -0.0008
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## 280 0.7609 nan 0.1000 -0.0010
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## 320 0.7492 nan 0.1000 -0.0006
## 340 0.7426 nan 0.1000 -0.0008
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## 380 0.7319 nan 0.1000 -0.0010
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## 460 0.7149 nan 0.1000 -0.0006
## 480 0.7105 nan 0.1000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2506 nan 0.1000 0.0177
## 2 1.2186 nan 0.1000 0.0118
## 3 1.1991 nan 0.1000 0.0074
## 4 1.1770 nan 0.1000 0.0114
## 5 1.1579 nan 0.1000 0.0079
## 6 1.1420 nan 0.1000 0.0075
## 7 1.1223 nan 0.1000 0.0076
## 8 1.1099 nan 0.1000 0.0057
## 9 1.0953 nan 0.1000 0.0054
## 10 1.0826 nan 0.1000 0.0057
## 20 0.9952 nan 0.1000 0.0016
## 40 0.9112 nan 0.1000 0.0003
## 60 0.8740 nan 0.1000 0.0003
## 80 0.8556 nan 0.1000 -0.0003
## 100 0.8361 nan 0.1000 -0.0001
## 120 0.8221 nan 0.1000 0.0005
## 140 0.8089 nan 0.1000 -0.0009
## 160 0.8006 nan 0.1000 -0.0016
## 180 0.7905 nan 0.1000 -0.0018
## 200 0.7840 nan 0.1000 -0.0005
## 220 0.7751 nan 0.1000 -0.0008
## 240 0.7679 nan 0.1000 -0.0008
## 260 0.7613 nan 0.1000 0.0001
## 280 0.7565 nan 0.1000 -0.0006
## 300 0.7476 nan 0.1000 -0.0012
## 320 0.7427 nan 0.1000 -0.0013
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## 380 0.7284 nan 0.1000 -0.0003
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## 460 0.7129 nan 0.1000 -0.0004
## 480 0.7061 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2421 nan 0.1000 0.0223
## 2 1.2062 nan 0.1000 0.0152
## 3 1.1706 nan 0.1000 0.0142
## 4 1.1384 nan 0.1000 0.0124
## 5 1.1131 nan 0.1000 0.0115
## 6 1.0903 nan 0.1000 0.0093
## 7 1.0700 nan 0.1000 0.0080
## 8 1.0576 nan 0.1000 0.0043
## 9 1.0431 nan 0.1000 0.0058
## 10 1.0263 nan 0.1000 0.0052
## 20 0.9313 nan 0.1000 0.0020
## 40 0.8439 nan 0.1000 -0.0013
## 60 0.8047 nan 0.1000 -0.0016
## 80 0.7774 nan 0.1000 -0.0002
## 100 0.7527 nan 0.1000 -0.0008
## 120 0.7272 nan 0.1000 0.0005
## 140 0.7005 nan 0.1000 -0.0006
## 160 0.6797 nan 0.1000 -0.0008
## 180 0.6593 nan 0.1000 -0.0014
## 200 0.6424 nan 0.1000 -0.0020
## 220 0.6230 nan 0.1000 0.0010
## 240 0.6098 nan 0.1000 -0.0016
## 260 0.5896 nan 0.1000 0.0001
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## 320 0.5430 nan 0.1000 -0.0010
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## 380 0.5065 nan 0.1000 -0.0015
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2475 nan 0.1000 0.0208
## 2 1.2082 nan 0.1000 0.0143
## 3 1.1787 nan 0.1000 0.0134
## 4 1.1492 nan 0.1000 0.0115
## 5 1.1239 nan 0.1000 0.0104
## 6 1.1024 nan 0.1000 0.0103
## 7 1.0796 nan 0.1000 0.0085
## 8 1.0628 nan 0.1000 0.0064
## 9 1.0469 nan 0.1000 0.0053
## 10 1.0313 nan 0.1000 0.0061
## 20 0.9315 nan 0.1000 0.0006
## 40 0.8475 nan 0.1000 -0.0001
## 60 0.7973 nan 0.1000 -0.0008
## 80 0.7635 nan 0.1000 -0.0009
## 100 0.7350 nan 0.1000 -0.0019
## 120 0.7147 nan 0.1000 -0.0007
## 140 0.6876 nan 0.1000 0.0001
## 160 0.6634 nan 0.1000 -0.0010
## 180 0.6448 nan 0.1000 -0.0010
## 200 0.6281 nan 0.1000 -0.0014
## 220 0.6113 nan 0.1000 -0.0010
## 240 0.5940 nan 0.1000 -0.0012
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## 300 0.5475 nan 0.1000 -0.0006
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## 380 0.4960 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2453 nan 0.1000 0.0213
## 2 1.2037 nan 0.1000 0.0179
## 3 1.1712 nan 0.1000 0.0159
## 4 1.1411 nan 0.1000 0.0108
## 5 1.1147 nan 0.1000 0.0113
## 6 1.0935 nan 0.1000 0.0099
## 7 1.0758 nan 0.1000 0.0054
## 8 1.0610 nan 0.1000 0.0046
## 9 1.0461 nan 0.1000 0.0052
## 10 1.0330 nan 0.1000 0.0044
## 20 0.9333 nan 0.1000 0.0016
## 40 0.8455 nan 0.1000 -0.0005
## 60 0.8018 nan 0.1000 -0.0014
## 80 0.7644 nan 0.1000 -0.0007
## 100 0.7347 nan 0.1000 -0.0003
## 120 0.7092 nan 0.1000 -0.0009
## 140 0.6871 nan 0.1000 -0.0015
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## 180 0.6460 nan 0.1000 -0.0016
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2329 nan 0.1000 0.0266
## 2 1.1928 nan 0.1000 0.0145
## 3 1.1526 nan 0.1000 0.0169
## 4 1.1197 nan 0.1000 0.0145
## 5 1.0876 nan 0.1000 0.0129
## 6 1.0618 nan 0.1000 0.0080
## 7 1.0387 nan 0.1000 0.0069
## 8 1.0205 nan 0.1000 0.0050
## 9 1.0040 nan 0.1000 0.0045
## 10 0.9882 nan 0.1000 0.0059
## 20 0.8847 nan 0.1000 0.0016
## 40 0.7966 nan 0.1000 0.0002
## 60 0.7446 nan 0.1000 -0.0005
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## 100 0.6572 nan 0.1000 -0.0018
## 120 0.6272 nan 0.1000 -0.0016
## 140 0.5948 nan 0.1000 -0.0005
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## 180 0.5382 nan 0.1000 -0.0013
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## 220 0.4932 nan 0.1000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2392 nan 0.1000 0.0255
## 2 1.1933 nan 0.1000 0.0189
## 3 1.1566 nan 0.1000 0.0173
## 4 1.1231 nan 0.1000 0.0139
## 5 1.0933 nan 0.1000 0.0116
## 6 1.0687 nan 0.1000 0.0085
## 7 1.0451 nan 0.1000 0.0069
## 8 1.0258 nan 0.1000 0.0040
## 9 1.0084 nan 0.1000 0.0058
## 10 0.9922 nan 0.1000 0.0046
## 20 0.8872 nan 0.1000 0.0011
## 40 0.7911 nan 0.1000 0.0001
## 60 0.7350 nan 0.1000 -0.0030
## 80 0.6974 nan 0.1000 -0.0016
## 100 0.6554 nan 0.1000 -0.0018
## 120 0.6245 nan 0.1000 -0.0014
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## 160 0.5702 nan 0.1000 -0.0018
## 180 0.5394 nan 0.1000 -0.0003
## 200 0.5146 nan 0.1000 -0.0000
## 220 0.4912 nan 0.1000 -0.0004
## 240 0.4704 nan 0.1000 -0.0012
## 260 0.4524 nan 0.1000 -0.0010
## 280 0.4355 nan 0.1000 -0.0012
## 300 0.4188 nan 0.1000 -0.0008
## 320 0.4026 nan 0.1000 -0.0005
## 340 0.3840 nan 0.1000 0.0001
## 360 0.3646 nan 0.1000 -0.0016
## 380 0.3522 nan 0.1000 -0.0007
## 400 0.3406 nan 0.1000 -0.0011
## 420 0.3275 nan 0.1000 -0.0010
## 440 0.3157 nan 0.1000 -0.0002
## 460 0.3040 nan 0.1000 -0.0007
## 480 0.2927 nan 0.1000 -0.0013
## 500 0.2834 nan 0.1000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2346 nan 0.1000 0.0284
## 2 1.1926 nan 0.1000 0.0211
## 3 1.1613 nan 0.1000 0.0122
## 4 1.1243 nan 0.1000 0.0174
## 5 1.0955 nan 0.1000 0.0116
## 6 1.0713 nan 0.1000 0.0096
## 7 1.0467 nan 0.1000 0.0094
## 8 1.0274 nan 0.1000 0.0059
## 9 1.0063 nan 0.1000 0.0075
## 10 0.9940 nan 0.1000 0.0045
## 20 0.8901 nan 0.1000 0.0016
## 40 0.7994 nan 0.1000 -0.0004
## 60 0.7333 nan 0.1000 -0.0002
## 80 0.6910 nan 0.1000 -0.0022
## 100 0.6548 nan 0.1000 -0.0005
## 120 0.6222 nan 0.1000 -0.0014
## 140 0.5841 nan 0.1000 -0.0007
## 160 0.5596 nan 0.1000 -0.0018
## 180 0.5337 nan 0.1000 -0.0014
## 200 0.5129 nan 0.1000 -0.0012
## 220 0.4923 nan 0.1000 -0.0005
## 240 0.4705 nan 0.1000 -0.0012
## 260 0.4501 nan 0.1000 -0.0016
## 280 0.4330 nan 0.1000 -0.0010
## 300 0.4167 nan 0.1000 -0.0007
## 320 0.4041 nan 0.1000 -0.0013
## 340 0.3885 nan 0.1000 -0.0010
## 360 0.3730 nan 0.1000 -0.0005
## 380 0.3557 nan 0.1000 -0.0005
## 400 0.3410 nan 0.1000 -0.0011
## 420 0.3294 nan 0.1000 -0.0012
## 440 0.3153 nan 0.1000 -0.0011
## 460 0.3024 nan 0.1000 -0.0002
## 480 0.2912 nan 0.1000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2259 nan 0.2000 0.0327
## 2 1.1839 nan 0.2000 0.0216
## 3 1.1507 nan 0.2000 0.0149
## 4 1.1216 nan 0.2000 0.0130
## 5 1.0915 nan 0.2000 0.0144
## 6 1.0683 nan 0.2000 0.0070
## 7 1.0526 nan 0.2000 0.0058
## 8 1.0334 nan 0.2000 0.0081
## 9 1.0159 nan 0.2000 0.0028
## 10 1.0063 nan 0.2000 0.0025
## 20 0.9189 nan 0.2000 -0.0003
## 40 0.8570 nan 0.2000 -0.0013
## 60 0.8280 nan 0.2000 -0.0009
## 80 0.8086 nan 0.2000 -0.0024
## 100 0.7876 nan 0.2000 -0.0013
## 120 0.7747 nan 0.2000 -0.0016
## 140 0.7662 nan 0.2000 -0.0010
## 160 0.7545 nan 0.2000 -0.0033
## 180 0.7387 nan 0.2000 -0.0010
## 200 0.7283 nan 0.2000 -0.0043
## 220 0.7175 nan 0.2000 -0.0022
## 240 0.7074 nan 0.2000 -0.0043
## 260 0.7022 nan 0.2000 -0.0013
## 280 0.6972 nan 0.2000 -0.0009
## 300 0.6889 nan 0.2000 -0.0020
## 320 0.6846 nan 0.2000 -0.0024
## 340 0.6786 nan 0.2000 -0.0015
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## 380 0.6668 nan 0.2000 -0.0015
## 400 0.6602 nan 0.2000 -0.0015
## 420 0.6508 nan 0.2000 -0.0024
## 440 0.6458 nan 0.2000 -0.0028
## 460 0.6439 nan 0.2000 -0.0016
## 480 0.6381 nan 0.2000 -0.0031
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2248 nan 0.2000 0.0317
## 2 1.1804 nan 0.2000 0.0190
## 3 1.1393 nan 0.2000 0.0159
## 4 1.1043 nan 0.2000 0.0126
## 5 1.0808 nan 0.2000 0.0119
## 6 1.0559 nan 0.2000 0.0101
## 7 1.0381 nan 0.2000 0.0044
## 8 1.0257 nan 0.2000 0.0051
## 9 1.0096 nan 0.2000 0.0036
## 10 0.9983 nan 0.2000 0.0036
## 20 0.9140 nan 0.2000 -0.0005
## 40 0.8509 nan 0.2000 -0.0018
## 60 0.8265 nan 0.2000 -0.0011
## 80 0.8021 nan 0.2000 -0.0023
## 100 0.7879 nan 0.2000 -0.0020
## 120 0.7733 nan 0.2000 -0.0009
## 140 0.7625 nan 0.2000 -0.0021
## 160 0.7516 nan 0.2000 -0.0016
## 180 0.7426 nan 0.2000 -0.0019
## 200 0.7286 nan 0.2000 -0.0007
## 220 0.7166 nan 0.2000 -0.0012
## 240 0.7087 nan 0.2000 -0.0030
## 260 0.7002 nan 0.2000 -0.0016
## 280 0.6937 nan 0.2000 -0.0019
## 300 0.6827 nan 0.2000 -0.0009
## 320 0.6776 nan 0.2000 -0.0034
## 340 0.6717 nan 0.2000 -0.0015
## 360 0.6635 nan 0.2000 -0.0027
## 380 0.6555 nan 0.2000 -0.0040
## 400 0.6483 nan 0.2000 -0.0024
## 420 0.6433 nan 0.2000 -0.0005
## 440 0.6396 nan 0.2000 -0.0013
## 460 0.6340 nan 0.2000 -0.0020
## 480 0.6292 nan 0.2000 -0.0013
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2158 nan 0.2000 0.0302
## 2 1.1672 nan 0.2000 0.0204
## 3 1.1393 nan 0.2000 0.0125
## 4 1.1095 nan 0.2000 0.0128
## 5 1.0790 nan 0.2000 0.0095
## 6 1.0518 nan 0.2000 0.0098
## 7 1.0305 nan 0.2000 0.0079
## 8 1.0142 nan 0.2000 0.0056
## 9 1.0020 nan 0.2000 0.0024
## 10 0.9907 nan 0.2000 0.0032
## 20 0.9098 nan 0.2000 0.0011
## 40 0.8479 nan 0.2000 -0.0006
## 60 0.8174 nan 0.2000 -0.0018
## 80 0.8013 nan 0.2000 -0.0019
## 100 0.7855 nan 0.2000 -0.0023
## 120 0.7780 nan 0.2000 -0.0015
## 140 0.7605 nan 0.2000 -0.0022
## 160 0.7458 nan 0.2000 -0.0011
## 180 0.7324 nan 0.2000 -0.0005
## 200 0.7182 nan 0.2000 -0.0012
## 220 0.7093 nan 0.2000 -0.0013
## 240 0.7032 nan 0.2000 -0.0029
## 260 0.6927 nan 0.2000 -0.0019
## 280 0.6845 nan 0.2000 -0.0022
## 300 0.6801 nan 0.2000 -0.0036
## 320 0.6720 nan 0.2000 -0.0023
## 340 0.6638 nan 0.2000 -0.0027
## 360 0.6580 nan 0.2000 -0.0019
## 380 0.6522 nan 0.2000 -0.0015
## 400 0.6454 nan 0.2000 -0.0018
## 420 0.6414 nan 0.2000 -0.0020
## 440 0.6332 nan 0.2000 -0.0020
## 460 0.6287 nan 0.2000 -0.0020
## 480 0.6273 nan 0.2000 -0.0011
## 500 0.6200 nan 0.2000 -0.0035
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2101 nan 0.2000 0.0375
## 2 1.1489 nan 0.2000 0.0300
## 3 1.0954 nan 0.2000 0.0250
## 4 1.0574 nan 0.2000 0.0137
## 5 1.0194 nan 0.2000 0.0145
## 6 0.9967 nan 0.2000 0.0103
## 7 0.9709 nan 0.2000 0.0080
## 8 0.9525 nan 0.2000 0.0041
## 9 0.9397 nan 0.2000 0.0030
## 10 0.9291 nan 0.2000 0.0028
## 20 0.8428 nan 0.2000 -0.0007
## 40 0.7707 nan 0.2000 -0.0012
## 60 0.7249 nan 0.2000 -0.0027
## 80 0.6830 nan 0.2000 -0.0029
## 100 0.6416 nan 0.2000 -0.0027
## 120 0.6075 nan 0.2000 -0.0031
## 140 0.5774 nan 0.2000 -0.0024
## 160 0.5467 nan 0.2000 -0.0002
## 180 0.5166 nan 0.2000 -0.0026
## 200 0.4991 nan 0.2000 -0.0028
## 220 0.4788 nan 0.2000 -0.0011
## 240 0.4564 nan 0.2000 -0.0012
## 260 0.4421 nan 0.2000 -0.0003
## 280 0.4243 nan 0.2000 -0.0009
## 300 0.4112 nan 0.2000 -0.0012
## 320 0.3899 nan 0.2000 -0.0042
## 340 0.3758 nan 0.2000 -0.0011
## 360 0.3656 nan 0.2000 -0.0005
## 380 0.3526 nan 0.2000 -0.0015
## 400 0.3415 nan 0.2000 -0.0013
## 420 0.3270 nan 0.2000 -0.0013
## 440 0.3135 nan 0.2000 -0.0023
## 460 0.3034 nan 0.2000 -0.0027
## 480 0.2931 nan 0.2000 -0.0017
## 500 0.2834 nan 0.2000 -0.0021
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2014 nan 0.2000 0.0420
## 2 1.1326 nan 0.2000 0.0279
## 3 1.0843 nan 0.2000 0.0194
## 4 1.0540 nan 0.2000 0.0123
## 5 1.0174 nan 0.2000 0.0130
## 6 0.9943 nan 0.2000 0.0082
## 7 0.9769 nan 0.2000 0.0031
## 8 0.9610 nan 0.2000 0.0066
## 9 0.9426 nan 0.2000 0.0076
## 10 0.9280 nan 0.2000 0.0037
## 20 0.8522 nan 0.2000 -0.0000
## 40 0.7831 nan 0.2000 0.0003
## 60 0.7370 nan 0.2000 -0.0007
## 80 0.6959 nan 0.2000 -0.0020
## 100 0.6487 nan 0.2000 -0.0019
## 120 0.6234 nan 0.2000 -0.0032
## 140 0.5898 nan 0.2000 -0.0054
## 160 0.5616 nan 0.2000 -0.0020
## 180 0.5315 nan 0.2000 -0.0007
## 200 0.5140 nan 0.2000 -0.0034
## 220 0.4888 nan 0.2000 -0.0043
## 240 0.4684 nan 0.2000 -0.0041
## 260 0.4412 nan 0.2000 -0.0017
## 280 0.4243 nan 0.2000 -0.0029
## 300 0.4007 nan 0.2000 -0.0023
## 320 0.3854 nan 0.2000 -0.0012
## 340 0.3685 nan 0.2000 -0.0023
## 360 0.3559 nan 0.2000 -0.0011
## 380 0.3417 nan 0.2000 -0.0025
## 400 0.3299 nan 0.2000 -0.0005
## 420 0.3170 nan 0.2000 -0.0014
## 440 0.3046 nan 0.2000 -0.0013
## 460 0.2990 nan 0.2000 -0.0013
## 480 0.2863 nan 0.2000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2032 nan 0.2000 0.0430
## 2 1.1403 nan 0.2000 0.0317
## 3 1.0918 nan 0.2000 0.0230
## 4 1.0613 nan 0.2000 0.0124
## 5 1.0317 nan 0.2000 0.0118
## 6 0.9997 nan 0.2000 0.0077
## 7 0.9757 nan 0.2000 0.0099
## 8 0.9591 nan 0.2000 0.0025
## 9 0.9366 nan 0.2000 0.0068
## 10 0.9282 nan 0.2000 0.0005
## 20 0.8527 nan 0.2000 -0.0059
## 40 0.7729 nan 0.2000 -0.0029
## 60 0.7293 nan 0.2000 -0.0026
## 80 0.6805 nan 0.2000 -0.0032
## 100 0.6457 nan 0.2000 -0.0018
## 120 0.6108 nan 0.2000 -0.0014
## 140 0.5848 nan 0.2000 -0.0028
## 160 0.5602 nan 0.2000 -0.0014
## 180 0.5383 nan 0.2000 -0.0037
## 200 0.5132 nan 0.2000 -0.0024
## 220 0.4958 nan 0.2000 -0.0018
## 240 0.4739 nan 0.2000 -0.0007
## 260 0.4523 nan 0.2000 -0.0022
## 280 0.4301 nan 0.2000 -0.0012
## 300 0.4131 nan 0.2000 -0.0018
## 320 0.3964 nan 0.2000 -0.0014
## 340 0.3826 nan 0.2000 -0.0005
## 360 0.3677 nan 0.2000 -0.0014
## 380 0.3557 nan 0.2000 -0.0027
## 400 0.3418 nan 0.2000 -0.0007
## 420 0.3298 nan 0.2000 -0.0017
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## 460 0.3088 nan 0.2000 -0.0028
## 480 0.2983 nan 0.2000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1782 nan 0.2000 0.0479
## 2 1.1136 nan 0.2000 0.0283
## 3 1.0600 nan 0.2000 0.0167
## 4 1.0213 nan 0.2000 0.0160
## 5 0.9848 nan 0.2000 0.0118
## 6 0.9589 nan 0.2000 0.0078
## 7 0.9395 nan 0.2000 0.0050
## 8 0.9165 nan 0.2000 0.0070
## 9 0.8997 nan 0.2000 0.0061
## 10 0.8829 nan 0.2000 0.0031
## 20 0.7958 nan 0.2000 -0.0065
## 40 0.7275 nan 0.2000 -0.0026
## 60 0.6338 nan 0.2000 -0.0027
## 80 0.5681 nan 0.2000 0.0001
## 100 0.5196 nan 0.2000 -0.0013
## 120 0.4734 nan 0.2000 -0.0014
## 140 0.4356 nan 0.2000 -0.0004
## 160 0.3966 nan 0.2000 -0.0034
## 180 0.3596 nan 0.2000 -0.0010
## 200 0.3392 nan 0.2000 -0.0025
## 220 0.3152 nan 0.2000 -0.0023
## 240 0.2963 nan 0.2000 -0.0033
## 260 0.2723 nan 0.2000 -0.0008
## 280 0.2562 nan 0.2000 -0.0004
## 300 0.2345 nan 0.2000 -0.0014
## 320 0.2206 nan 0.2000 -0.0008
## 340 0.2085 nan 0.2000 -0.0006
## 360 0.1946 nan 0.2000 -0.0009
## 380 0.1822 nan 0.2000 -0.0011
## 400 0.1725 nan 0.2000 -0.0009
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## 440 0.1564 nan 0.2000 -0.0023
## 460 0.1456 nan 0.2000 -0.0007
## 480 0.1377 nan 0.2000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1962 nan 0.2000 0.0474
## 2 1.1187 nan 0.2000 0.0310
## 3 1.0663 nan 0.2000 0.0108
## 4 1.0292 nan 0.2000 0.0168
## 5 0.9945 nan 0.2000 0.0134
## 6 0.9642 nan 0.2000 0.0093
## 7 0.9458 nan 0.2000 0.0049
## 8 0.9255 nan 0.2000 0.0010
## 9 0.9077 nan 0.2000 0.0008
## 10 0.8900 nan 0.2000 0.0019
## 20 0.8016 nan 0.2000 -0.0044
## 40 0.7081 nan 0.2000 -0.0025
## 60 0.6448 nan 0.2000 -0.0031
## 80 0.5851 nan 0.2000 -0.0012
## 100 0.5394 nan 0.2000 -0.0020
## 120 0.5033 nan 0.2000 -0.0032
## 140 0.4644 nan 0.2000 -0.0024
## 160 0.4261 nan 0.2000 -0.0017
## 180 0.3862 nan 0.2000 -0.0003
## 200 0.3532 nan 0.2000 -0.0007
## 220 0.3289 nan 0.2000 -0.0010
## 240 0.3086 nan 0.2000 -0.0003
## 260 0.2835 nan 0.2000 -0.0005
## 280 0.2681 nan 0.2000 -0.0026
## 300 0.2513 nan 0.2000 -0.0013
## 320 0.2359 nan 0.2000 -0.0017
## 340 0.2160 nan 0.2000 -0.0006
## 360 0.2033 nan 0.2000 -0.0015
## 380 0.1916 nan 0.2000 -0.0007
## 400 0.1801 nan 0.2000 -0.0004
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## 460 0.1468 nan 0.2000 -0.0008
## 480 0.1373 nan 0.2000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1869 nan 0.2000 0.0445
## 2 1.1132 nan 0.2000 0.0285
## 3 1.0644 nan 0.2000 0.0171
## 4 1.0195 nan 0.2000 0.0203
## 5 0.9850 nan 0.2000 0.0127
## 6 0.9580 nan 0.2000 0.0037
## 7 0.9354 nan 0.2000 0.0066
## 8 0.9207 nan 0.2000 0.0003
## 9 0.9063 nan 0.2000 -0.0013
## 10 0.8923 nan 0.2000 0.0011
## 20 0.8036 nan 0.2000 -0.0026
## 40 0.7150 nan 0.2000 -0.0042
## 60 0.6376 nan 0.2000 -0.0017
## 80 0.5825 nan 0.2000 -0.0022
## 100 0.5295 nan 0.2000 -0.0018
## 120 0.4794 nan 0.2000 -0.0012
## 140 0.4418 nan 0.2000 -0.0024
## 160 0.4093 nan 0.2000 -0.0034
## 180 0.3816 nan 0.2000 -0.0024
## 200 0.3479 nan 0.2000 -0.0015
## 220 0.3170 nan 0.2000 -0.0034
## 240 0.2946 nan 0.2000 -0.0005
## 260 0.2781 nan 0.2000 -0.0016
## 280 0.2613 nan 0.2000 -0.0025
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## 320 0.2255 nan 0.2000 -0.0015
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## 360 0.1981 nan 0.2000 -0.0007
## 380 0.1871 nan 0.2000 -0.0003
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## 460 0.1488 nan 0.2000 -0.0006
## 480 0.1382 nan 0.2000 -0.0016
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1938 nan 0.3000 0.0445
## 2 1.1434 nan 0.3000 0.0217
## 3 1.0966 nan 0.3000 0.0151
## 4 1.0644 nan 0.3000 0.0129
## 5 1.0261 nan 0.3000 0.0160
## 6 1.0064 nan 0.3000 0.0058
## 7 0.9882 nan 0.3000 0.0059
## 8 0.9688 nan 0.3000 0.0070
## 9 0.9591 nan 0.3000 -0.0019
## 10 0.9513 nan 0.3000 0.0018
## 20 0.8823 nan 0.3000 -0.0006
## 40 0.8337 nan 0.3000 -0.0066
## 60 0.8123 nan 0.3000 -0.0017
## 80 0.7875 nan 0.3000 -0.0038
## 100 0.7713 nan 0.3000 -0.0030
## 120 0.7553 nan 0.3000 -0.0050
## 140 0.7353 nan 0.3000 -0.0020
## 160 0.7214 nan 0.3000 -0.0026
## 180 0.7080 nan 0.3000 -0.0031
## 200 0.6993 nan 0.3000 -0.0056
## 220 0.6867 nan 0.3000 -0.0039
## 240 0.6823 nan 0.3000 -0.0004
## 260 0.6666 nan 0.3000 -0.0036
## 280 0.6583 nan 0.3000 -0.0018
## 300 0.6500 nan 0.3000 -0.0025
## 320 0.6442 nan 0.3000 -0.0008
## 340 0.6328 nan 0.3000 -0.0038
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## 380 0.6170 nan 0.3000 -0.0024
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## 460 0.5964 nan 0.3000 -0.0015
## 480 0.5849 nan 0.3000 -0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2117 nan 0.3000 0.0382
## 2 1.1409 nan 0.3000 0.0260
## 3 1.1007 nan 0.3000 0.0176
## 4 1.0598 nan 0.3000 0.0170
## 5 1.0323 nan 0.3000 0.0125
## 6 1.0079 nan 0.3000 0.0100
## 7 0.9820 nan 0.3000 0.0051
## 8 0.9687 nan 0.3000 -0.0010
## 9 0.9535 nan 0.3000 0.0016
## 10 0.9402 nan 0.3000 0.0013
## 20 0.8717 nan 0.3000 0.0009
## 40 0.8323 nan 0.3000 -0.0073
## 60 0.8163 nan 0.3000 -0.0038
## 80 0.7914 nan 0.3000 -0.0030
## 100 0.7730 nan 0.3000 -0.0061
## 120 0.7545 nan 0.3000 -0.0029
## 140 0.7412 nan 0.3000 -0.0009
## 160 0.7299 nan 0.3000 -0.0028
## 180 0.7104 nan 0.3000 -0.0032
## 200 0.7056 nan 0.3000 -0.0060
## 220 0.6940 nan 0.3000 -0.0025
## 240 0.6854 nan 0.3000 -0.0047
## 260 0.6736 nan 0.3000 -0.0027
## 280 0.6680 nan 0.3000 -0.0030
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## 320 0.6499 nan 0.3000 -0.0045
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## 480 0.5969 nan 0.3000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1948 nan 0.3000 0.0445
## 2 1.1399 nan 0.3000 0.0237
## 3 1.1048 nan 0.3000 0.0126
## 4 1.0741 nan 0.3000 0.0097
## 5 1.0406 nan 0.3000 0.0128
## 6 1.0159 nan 0.3000 0.0086
## 7 0.9865 nan 0.3000 0.0137
## 8 0.9787 nan 0.3000 0.0001
## 9 0.9637 nan 0.3000 0.0062
## 10 0.9509 nan 0.3000 0.0027
## 20 0.8732 nan 0.3000 -0.0017
## 40 0.8246 nan 0.3000 -0.0018
## 60 0.8021 nan 0.3000 -0.0006
## 80 0.7859 nan 0.3000 -0.0029
## 100 0.7621 nan 0.3000 -0.0016
## 120 0.7436 nan 0.3000 -0.0026
## 140 0.7295 nan 0.3000 -0.0035
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## 180 0.7057 nan 0.3000 -0.0019
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## 220 0.6846 nan 0.3000 -0.0013
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## 380 0.6143 nan 0.3000 -0.0050
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## 480 0.5823 nan 0.3000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1654 nan 0.3000 0.0540
## 2 1.0882 nan 0.3000 0.0327
## 3 1.0377 nan 0.3000 0.0226
## 4 0.9995 nan 0.3000 0.0119
## 5 0.9666 nan 0.3000 0.0151
## 6 0.9463 nan 0.3000 0.0019
## 7 0.9265 nan 0.3000 0.0041
## 8 0.9120 nan 0.3000 0.0002
## 9 0.9059 nan 0.3000 -0.0089
## 10 0.8984 nan 0.3000 -0.0010
## 20 0.8159 nan 0.3000 -0.0033
## 40 0.7303 nan 0.3000 -0.0030
## 60 0.6693 nan 0.3000 -0.0044
## 80 0.6194 nan 0.3000 -0.0025
## 100 0.5767 nan 0.3000 -0.0003
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## 140 0.5124 nan 0.3000 -0.0078
## 160 0.4776 nan 0.3000 0.0001
## 180 0.4560 nan 0.3000 -0.0028
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## 220 0.3914 nan 0.3000 -0.0004
## 240 0.3708 nan 0.3000 -0.0070
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## 320 0.2851 nan 0.3000 -0.0001
## 340 0.2737 nan 0.3000 -0.0036
## 360 0.2610 nan 0.3000 -0.0019
## 380 0.2445 nan 0.3000 -0.0007
## 400 0.2316 nan 0.3000 -0.0007
## 420 0.2217 nan 0.3000 -0.0022
## 440 0.2097 nan 0.3000 -0.0006
## 460 0.2004 nan 0.3000 -0.0007
## 480 0.1963 nan 0.3000 -0.0009
## 500 0.1860 nan 0.3000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1546 nan 0.3000 0.0611
## 2 1.0836 nan 0.3000 0.0296
## 3 1.0251 nan 0.3000 0.0204
## 4 0.9940 nan 0.3000 0.0092
## 5 0.9704 nan 0.3000 0.0074
## 6 0.9485 nan 0.3000 0.0011
## 7 0.9312 nan 0.3000 0.0008
## 8 0.9089 nan 0.3000 0.0078
## 9 0.8997 nan 0.3000 -0.0025
## 10 0.8871 nan 0.3000 0.0025
## 20 0.8125 nan 0.3000 -0.0038
## 40 0.7357 nan 0.3000 -0.0016
## 60 0.6873 nan 0.3000 -0.0043
## 80 0.6371 nan 0.3000 -0.0044
## 100 0.5992 nan 0.3000 -0.0038
## 120 0.5602 nan 0.3000 -0.0074
## 140 0.5261 nan 0.3000 -0.0035
## 160 0.4924 nan 0.3000 -0.0030
## 180 0.4631 nan 0.3000 -0.0030
## 200 0.4357 nan 0.3000 -0.0020
## 220 0.4054 nan 0.3000 -0.0027
## 240 0.3768 nan 0.3000 -0.0019
## 260 0.3597 nan 0.3000 -0.0044
## 280 0.3418 nan 0.3000 -0.0022
## 300 0.3256 nan 0.3000 -0.0032
## 320 0.3095 nan 0.3000 -0.0016
## 340 0.2937 nan 0.3000 -0.0014
## 360 0.2783 nan 0.3000 -0.0023
## 380 0.2632 nan 0.3000 -0.0022
## 400 0.2475 nan 0.3000 -0.0018
## 420 0.2322 nan 0.3000 -0.0001
## 440 0.2173 nan 0.3000 -0.0011
## 460 0.2062 nan 0.3000 -0.0021
## 480 0.1966 nan 0.3000 -0.0002
## 500 0.1839 nan 0.3000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1902 nan 0.3000 0.0426
## 2 1.1005 nan 0.3000 0.0359
## 3 1.0525 nan 0.3000 0.0122
## 4 1.0093 nan 0.3000 0.0176
## 5 0.9817 nan 0.3000 0.0037
## 6 0.9561 nan 0.3000 0.0079
## 7 0.9305 nan 0.3000 0.0120
## 8 0.9191 nan 0.3000 -0.0024
## 9 0.8998 nan 0.3000 0.0054
## 10 0.8902 nan 0.3000 0.0013
## 20 0.8142 nan 0.3000 -0.0050
## 40 0.7431 nan 0.3000 -0.0026
## 60 0.6758 nan 0.3000 -0.0023
## 80 0.6160 nan 0.3000 -0.0012
## 100 0.5831 nan 0.3000 -0.0044
## 120 0.5303 nan 0.3000 -0.0028
## 140 0.5063 nan 0.3000 -0.0040
## 160 0.4783 nan 0.3000 -0.0017
## 180 0.4456 nan 0.3000 -0.0024
## 200 0.4260 nan 0.3000 -0.0047
## 220 0.3900 nan 0.3000 -0.0012
## 240 0.3626 nan 0.3000 -0.0037
## 260 0.3401 nan 0.3000 -0.0022
## 280 0.3251 nan 0.3000 -0.0014
## 300 0.3096 nan 0.3000 -0.0028
## 320 0.2895 nan 0.3000 -0.0012
## 340 0.2731 nan 0.3000 -0.0028
## 360 0.2583 nan 0.3000 -0.0023
## 380 0.2487 nan 0.3000 -0.0041
## 400 0.2347 nan 0.3000 -0.0009
## 420 0.2241 nan 0.3000 -0.0025
## 440 0.2133 nan 0.3000 -0.0007
## 460 0.2054 nan 0.3000 -0.0017
## 480 0.1952 nan 0.3000 -0.0003
## 500 0.1876 nan 0.3000 -0.0013
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1462 nan 0.3000 0.0636
## 2 1.0671 nan 0.3000 0.0319
## 3 1.0143 nan 0.3000 0.0169
## 4 0.9766 nan 0.3000 0.0099
## 5 0.9425 nan 0.3000 0.0104
## 6 0.9194 nan 0.3000 -0.0028
## 7 0.8989 nan 0.3000 0.0011
## 8 0.8872 nan 0.3000 -0.0061
## 9 0.8681 nan 0.3000 -0.0013
## 10 0.8534 nan 0.3000 -0.0024
## 20 0.7738 nan 0.3000 -0.0058
## 40 0.6842 nan 0.3000 -0.0042
## 60 0.5655 nan 0.3000 -0.0069
## 80 0.4840 nan 0.3000 -0.0026
## 100 0.4161 nan 0.3000 -0.0022
## 120 0.3731 nan 0.3000 -0.0022
## 140 0.3281 nan 0.3000 -0.0011
## 160 0.2949 nan 0.3000 -0.0037
## 180 0.2621 nan 0.3000 -0.0021
## 200 0.2357 nan 0.3000 -0.0019
## 220 0.2063 nan 0.3000 -0.0008
## 240 0.1820 nan 0.3000 -0.0016
## 260 0.1645 nan 0.3000 -0.0019
## 280 0.1523 nan 0.3000 -0.0008
## 300 0.1406 nan 0.3000 -0.0017
## 320 0.1284 nan 0.3000 -0.0010
## 340 0.1195 nan 0.3000 -0.0019
## 360 0.1111 nan 0.3000 -0.0006
## 380 0.1020 nan 0.3000 -0.0001
## 400 0.0920 nan 0.3000 -0.0002
## 420 0.0854 nan 0.3000 -0.0007
## 440 0.0804 nan 0.3000 -0.0006
## 460 0.0737 nan 0.3000 -0.0004
## 480 0.0684 nan 0.3000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1600 nan 0.3000 0.0658
## 2 1.0646 nan 0.3000 0.0409
## 3 1.0160 nan 0.3000 0.0103
## 4 0.9747 nan 0.3000 0.0156
## 5 0.9332 nan 0.3000 0.0107
## 6 0.9018 nan 0.3000 0.0059
## 7 0.8791 nan 0.3000 0.0058
## 8 0.8677 nan 0.3000 -0.0052
## 9 0.8515 nan 0.3000 -0.0019
## 10 0.8349 nan 0.3000 -0.0003
## 20 0.7542 nan 0.3000 -0.0064
## 40 0.6344 nan 0.3000 -0.0064
## 60 0.5638 nan 0.3000 -0.0012
## 80 0.4905 nan 0.3000 -0.0085
## 100 0.4374 nan 0.3000 -0.0006
## 120 0.3859 nan 0.3000 -0.0042
## 140 0.3445 nan 0.3000 -0.0026
## 160 0.3076 nan 0.3000 -0.0025
## 180 0.2829 nan 0.3000 -0.0015
## 200 0.2483 nan 0.3000 -0.0000
## 220 0.2237 nan 0.3000 -0.0021
## 240 0.2072 nan 0.3000 -0.0025
## 260 0.1915 nan 0.3000 -0.0011
## 280 0.1770 nan 0.3000 -0.0011
## 300 0.1563 nan 0.3000 -0.0010
## 320 0.1445 nan 0.3000 -0.0013
## 340 0.1299 nan 0.3000 -0.0005
## 360 0.1190 nan 0.3000 -0.0004
## 380 0.1062 nan 0.3000 -0.0005
## 400 0.0971 nan 0.3000 -0.0002
## 420 0.0886 nan 0.3000 -0.0005
## 440 0.0825 nan 0.3000 -0.0011
## 460 0.0762 nan 0.3000 -0.0005
## 480 0.0686 nan 0.3000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1662 nan 0.3000 0.0509
## 2 1.0914 nan 0.3000 0.0305
## 3 1.0282 nan 0.3000 0.0207
## 4 0.9831 nan 0.3000 0.0152
## 5 0.9499 nan 0.3000 0.0104
## 6 0.9291 nan 0.3000 -0.0029
## 7 0.9082 nan 0.3000 0.0032
## 8 0.8950 nan 0.3000 -0.0017
## 9 0.8877 nan 0.3000 -0.0084
## 10 0.8726 nan 0.3000 -0.0041
## 20 0.7765 nan 0.3000 -0.0051
## 40 0.6636 nan 0.3000 -0.0132
## 60 0.5813 nan 0.3000 -0.0003
## 80 0.5012 nan 0.3000 -0.0017
## 100 0.4428 nan 0.3000 -0.0019
## 120 0.4011 nan 0.3000 -0.0021
## 140 0.3671 nan 0.3000 -0.0019
## 160 0.3309 nan 0.3000 -0.0020
## 180 0.2875 nan 0.3000 -0.0002
## 200 0.2552 nan 0.3000 -0.0004
## 220 0.2204 nan 0.3000 -0.0023
## 240 0.2013 nan 0.3000 -0.0014
## 260 0.1820 nan 0.3000 -0.0023
## 280 0.1641 nan 0.3000 -0.0015
## 300 0.1514 nan 0.3000 -0.0014
## 320 0.1377 nan 0.3000 -0.0001
## 340 0.1262 nan 0.3000 -0.0003
## 360 0.1170 nan 0.3000 -0.0019
## 380 0.1058 nan 0.3000 -0.0005
## 400 0.0955 nan 0.3000 -0.0008
## 420 0.0876 nan 0.3000 -0.0002
## 440 0.0808 nan 0.3000 -0.0011
## 460 0.0736 nan 0.3000 -0.0003
## 480 0.0675 nan 0.3000 -0.0003
## 500 0.0627 nan 0.3000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1495 nan 0.5000 0.0649
## 2 1.0875 nan 0.5000 0.0263
## 3 1.0420 nan 0.5000 0.0203
## 4 0.9980 nan 0.5000 0.0143
## 5 0.9799 nan 0.5000 0.0016
## 6 0.9725 nan 0.5000 -0.0056
## 7 0.9555 nan 0.5000 0.0052
## 8 0.9455 nan 0.5000 -0.0053
## 9 0.9289 nan 0.5000 0.0051
## 10 0.9133 nan 0.5000 0.0036
## 20 0.8578 nan 0.5000 0.0011
## 40 0.7879 nan 0.5000 -0.0053
## 60 0.7552 nan 0.5000 -0.0087
## 80 0.7269 nan 0.5000 -0.0038
## 100 0.7065 nan 0.5000 -0.0016
## 120 0.6870 nan 0.5000 -0.0069
## 140 0.6795 nan 0.5000 -0.0015
## 160 0.6616 nan 0.5000 -0.0047
## 180 0.6445 nan 0.5000 -0.0019
## 200 0.6299 nan 0.5000 -0.0058
## 220 0.6167 nan 0.5000 -0.0012
## 240 0.6046 nan 0.5000 -0.0049
## 260 0.6024 nan 0.5000 -0.0079
## 280 0.5921 nan 0.5000 -0.0048
## 300 0.5830 nan 0.5000 -0.0140
## 320 0.5702 nan 0.5000 -0.0025
## 340 0.5612 nan 0.5000 -0.0035
## 360 0.5606 nan 0.5000 -0.0109
## 380 0.5514 nan 0.5000 -0.0101
## 400 0.5405 nan 0.5000 -0.0028
## 420 0.5327 nan 0.5000 -0.0045
## 440 0.5454 nan 0.5000 -0.0198
## 460 0.5204 nan 0.5000 -0.0013
## 480 0.5116 nan 0.5000 -0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1401 nan 0.5000 0.0565
## 2 1.0762 nan 0.5000 0.0267
## 3 1.0214 nan 0.5000 0.0187
## 4 0.9764 nan 0.5000 0.0157
## 5 0.9655 nan 0.5000 0.0003
## 6 0.9468 nan 0.5000 0.0043
## 7 0.9320 nan 0.5000 0.0044
## 8 0.9104 nan 0.5000 0.0022
## 9 0.9139 nan 0.5000 -0.0113
## 10 0.9095 nan 0.5000 -0.0049
## 20 0.8467 nan 0.5000 -0.0046
## 40 0.7873 nan 0.5000 -0.0049
## 60 0.7627 nan 0.5000 -0.0046
## 80 0.7472 nan 0.5000 -0.0044
## 100 0.7340 nan 0.5000 -0.0031
## 120 0.7095 nan 0.5000 -0.0034
## 140 0.6966 nan 0.5000 -0.0046
## 160 0.6748 nan 0.5000 -0.0056
## 180 0.6629 nan 0.5000 -0.0082
## 200 0.6443 nan 0.5000 -0.0040
## 220 0.6271 nan 0.5000 -0.0045
## 240 0.6286 nan 0.5000 -0.0058
## 260 0.6078 nan 0.5000 -0.0065
## 280 0.6002 nan 0.5000 -0.0036
## 300 0.5846 nan 0.5000 -0.0022
## 320 0.5727 nan 0.5000 -0.0051
## 340 0.5637 nan 0.5000 -0.0039
## 360 0.5534 nan 0.5000 -0.0055
## 380 0.5427 nan 0.5000 0.0007
## 400 0.5416 nan 0.5000 -0.0028
## 420 0.5241 nan 0.5000 -0.0029
## 440 0.5201 nan 0.5000 -0.0020
## 460 0.5233 nan 0.5000 -0.0048
## 480 0.5124 nan 0.5000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1579 nan 0.5000 0.0642
## 2 1.0900 nan 0.5000 0.0260
## 3 1.0309 nan 0.5000 0.0196
## 4 1.0028 nan 0.5000 0.0126
## 5 0.9837 nan 0.5000 0.0011
## 6 0.9725 nan 0.5000 0.0021
## 7 0.9521 nan 0.5000 0.0022
## 8 0.9419 nan 0.5000 -0.0016
## 9 0.9363 nan 0.5000 -0.0048
## 10 0.9225 nan 0.5000 0.0015
## 20 0.8767 nan 0.5000 0.0001
## 40 0.8337 nan 0.5000 -0.0023
## 60 0.7960 nan 0.5000 -0.0042
## 80 0.7708 nan 0.5000 0.0005
## 100 0.7391 nan 0.5000 -0.0076
## 120 0.7223 nan 0.5000 -0.0028
## 140 0.7108 nan 0.5000 -0.0035
## 160 0.6948 nan 0.5000 -0.0137
## 180 0.6762 nan 0.5000 -0.0096
## 200 0.6418 nan 0.5000 -0.0018
## 220 0.6386 nan 0.5000 -0.0038
## 240 0.6288 nan 0.5000 -0.0090
## 260 0.6156 nan 0.5000 -0.0039
## 280 0.6018 nan 0.5000 -0.0039
## 300 0.5976 nan 0.5000 -0.0121
## 320 0.5902 nan 0.5000 -0.0113
## 340 0.5751 nan 0.5000 -0.0085
## 360 0.5621 nan 0.5000 -0.0055
## 380 0.5536 nan 0.5000 -0.0040
## 400 0.5463 nan 0.5000 -0.0103
## 420 0.5335 nan 0.5000 -0.0013
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## 460 0.5120 nan 0.5000 -0.0015
## 480 0.5115 nan 0.5000 -0.0116
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1259 nan 0.5000 0.0789
## 2 1.0393 nan 0.5000 0.0374
## 3 0.9861 nan 0.5000 0.0218
## 4 0.9363 nan 0.5000 0.0167
## 5 0.9175 nan 0.5000 -0.0122
## 6 0.8911 nan 0.5000 -0.0016
## 7 0.8789 nan 0.5000 -0.0018
## 8 0.8732 nan 0.5000 -0.0103
## 9 0.8653 nan 0.5000 -0.0153
## 10 0.8524 nan 0.5000 -0.0002
## 20 0.7874 nan 0.5000 -0.0038
## 40 0.7052 nan 0.5000 -0.0132
## 60 0.6022 nan 0.5000 -0.0114
## 80 0.6109 nan 0.5000 -0.0006
## 100 0.5789 nan 0.5000 -0.0460
## 120 0.4534 nan 0.5000 -0.0085
## 140 0.4035 nan 0.5000 -0.0023
## 160 0.3442 nan 0.5000 -0.0040
## 180 0.3067 nan 0.5000 -0.0034
## 200 0.2694 nan 0.5000 -0.0038
## 220 0.2327 nan 0.5000 -0.0011
## 240 0.2215 nan 0.5000 -0.0057
## 260 0.1898 nan 0.5000 -0.0004
## 280 0.1631 nan 0.5000 -0.0014
## 300 0.1450 nan 0.5000 -0.0007
## 320 0.1261 nan 0.5000 -0.0010
## 340 0.1180 nan 0.5000 -0.0012
## 360 0.1066 nan 0.5000 -0.0009
## 380 0.0981 nan 0.5000 -0.0010
## 400 0.0906 nan 0.5000 -0.0007
## 420 0.0856 nan 0.5000 -0.0016
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## 460 0.0754 nan 0.5000 0.0001
## 480 0.0717 nan 0.5000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1294 nan 0.5000 0.0783
## 2 1.0382 nan 0.5000 0.0438
## 3 0.9760 nan 0.5000 0.0277
## 4 0.9521 nan 0.5000 -0.0050
## 5 0.9201 nan 0.5000 0.0035
## 6 0.8932 nan 0.5000 0.0054
## 7 0.8699 nan 0.5000 0.0035
## 8 0.8568 nan 0.5000 0.0019
## 9 0.8404 nan 0.5000 -0.0060
## 10 0.8291 nan 0.5000 -0.0092
## 20 0.7749 nan 0.5000 -0.0148
## 40 0.6899 nan 0.5000 0.0009
## 60 0.6123 nan 0.5000 0.0012
## 80 0.5464 nan 0.5000 -0.0049
## 100 0.4719 nan 0.5000 -0.0037
## 120 0.4447 nan 0.5000 -0.0164
## 140 0.4059 nan 0.5000 -0.0062
## 160 0.3713 nan 0.5000 -0.0013
## 180 0.3277 nan 0.5000 -0.0095
## 200 0.2854 nan 0.5000 -0.0016
## 220 0.2586 nan 0.5000 -0.0029
## 240 0.2424 nan 0.5000 -0.0049
## 260 0.2178 nan 0.5000 -0.0011
## 280 0.1881 nan 0.5000 -0.0023
## 300 0.1619 nan 0.5000 -0.0038
## 320 0.1501 nan 0.5000 -0.0019
## 340 0.1362 nan 0.5000 -0.0029
## 360 0.1281 nan 0.5000 -0.0008
## 380 0.1168 nan 0.5000 -0.0009
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## 420 0.1013 nan 0.5000 -0.0002
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## 480 0.0804 nan 0.5000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1086 nan 0.5000 0.0875
## 2 1.0377 nan 0.5000 0.0214
## 3 0.9617 nan 0.5000 0.0316
## 4 0.9233 nan 0.5000 0.0108
## 5 0.9022 nan 0.5000 -0.0016
## 6 0.8849 nan 0.5000 -0.0024
## 7 0.8716 nan 0.5000 -0.0029
## 8 0.8608 nan 0.5000 -0.0032
## 9 0.8477 nan 0.5000 0.0015
## 10 0.8440 nan 0.5000 -0.0096
## 20 0.7874 nan 0.5000 -0.0096
## 40 0.7081 nan 0.5000 -0.0121
## 60 0.6314 nan 0.5000 -0.0109
## 80 0.5548 nan 0.5000 -0.0038
## 100 0.5263 nan 0.5000 -0.0038
## 120 0.4902 nan 0.5000 -0.0033
## 140 0.4407 nan 0.5000 -0.0053
## 160 0.3897 nan 0.5000 -0.0062
## 180 0.3525 nan 0.5000 -0.0045
## 200 0.3285 nan 0.5000 -0.0023
## 220 0.3077 nan 0.5000 -0.0053
## 240 0.2773 nan 0.5000 -0.0062
## 260 0.2519 nan 0.5000 -0.0022
## 280 0.2237 nan 0.5000 -0.0035
## 300 0.2007 nan 0.5000 -0.0014
## 320 0.1884 nan 0.5000 -0.0042
## 340 0.1767 nan 0.5000 -0.0034
## 360 0.1624 nan 0.5000 -0.0015
## 380 0.1500 nan 0.5000 -0.0031
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## 440 0.1149 nan 0.5000 -0.0021
## 460 0.1078 nan 0.5000 -0.0014
## 480 0.0988 nan 0.5000 -0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0828 nan 0.5000 0.1073
## 2 1.0048 nan 0.5000 0.0037
## 3 0.9465 nan 0.5000 0.0048
## 4 0.9207 nan 0.5000 -0.0110
## 5 0.9075 nan 0.5000 -0.0164
## 6 0.8876 nan 0.5000 -0.0057
## 7 0.8836 nan 0.5000 -0.0222
## 8 0.8517 nan 0.5000 -0.0024
## 9 0.8820 nan 0.5000 -0.0511
## 10 0.8554 nan 0.5000 0.0048
## 20 0.7915 nan 0.5000 -0.0137
## 40 0.6373 nan 0.5000 0.0027
## 60 0.5351 nan 0.5000 -0.0058
## 80 0.7450 nan 0.5000 -0.0112
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0870 nan 0.5000 0.0947
## 2 0.9938 nan 0.5000 0.0299
## 3 0.9479 nan 0.5000 0.0153
## 4 0.9094 nan 0.5000 0.0017
## 5 0.9019 nan 0.5000 -0.0137
## 6 0.8774 nan 0.5000 -0.0055
## 7 0.8618 nan 0.5000 -0.0075
## 8 0.8520 nan 0.5000 -0.0115
## 9 0.8349 nan 0.5000 -0.0069
## 10 0.8248 nan 0.5000 -0.0065
## 20 0.7488 nan 0.5000 -0.0147
## 40 0.5872 nan 0.5000 -0.0003
## 60 0.4806 nan 0.5000 -0.0151
## 80 0.3911 nan 0.5000 -0.0059
## 100 0.3049 nan 0.5000 -0.0072
## 120 0.2502 nan 0.5000 -0.0023
## 140 0.2044 nan 0.5000 0.0001
## 160 0.1759 nan 0.5000 -0.0020
## 180 0.1525 nan 0.5000 -0.0026
## 200 0.1289 nan 0.5000 -0.0023
## 220 0.1104 nan 0.5000 -0.0011
## 240 0.0976 nan 0.5000 -0.0012
## 260 0.0856 nan 0.5000 -0.0011
## 280 0.0745 nan 0.5000 -0.0003
## 300 0.0649 nan 0.5000 -0.0006
## 320 0.0585 nan 0.5000 -0.0019
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## 380 0.0374 nan 0.5000 -0.0003
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## 460 0.0244 nan 0.5000 -0.0007
## 480 0.0214 nan 0.5000 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0951 nan 0.5000 0.0876
## 2 1.0055 nan 0.5000 0.0299
## 3 0.9596 nan 0.5000 -0.0005
## 4 0.9137 nan 0.5000 0.0221
## 5 0.8873 nan 0.5000 0.0045
## 6 0.8766 nan 0.5000 -0.0120
## 7 0.8543 nan 0.5000 -0.0019
## 8 0.8218 nan 0.5000 -0.0007
## 9 0.8138 nan 0.5000 -0.0121
## 10 0.8031 nan 0.5000 -0.0124
## 20 0.7179 nan 0.5000 -0.0040
## 40 0.6052 nan 0.5000 -0.0148
## 60 0.4815 nan 0.5000 -0.0128
## 80 0.4005 nan 0.5000 -0.0038
## 100 0.3291 nan 0.5000 -0.0086
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 -0.0010
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 -0.0006
## 460 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1052 nan 1.0000 0.0915
## 2 1.0293 nan 1.0000 0.0097
## 3 0.9846 nan 1.0000 0.0166
## 4 0.9658 nan 1.0000 -0.0016
## 5 0.9719 nan 1.0000 -0.0230
## 6 0.9619 nan 1.0000 -0.0153
## 7 0.9259 nan 1.0000 0.0082
## 8 0.9378 nan 1.0000 -0.0250
## 9 1.0770 nan 1.0000 -0.1611
## 10 1600.9974 nan 1.0000 0.0401
## 20 1600.9939 nan 1.0000 -0.0075
## 40 204070.0254 nan 1.0000 -0.0012
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 -0.0015
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 0.0016
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 -0.0022
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1169 nan 1.0000 0.0596
## 2 1.0384 nan 1.0000 0.0348
## 3 0.9964 nan 1.0000 0.0114
## 4 0.9862 nan 1.0000 -0.0030
## 5 0.9792 nan 1.0000 -0.0116
## 6 0.9736 nan 1.0000 -0.0152
## 7 0.9650 nan 1.0000 -0.0182
## 8 0.9601 nan 1.0000 -0.0057
## 9 0.9300 nan 1.0000 0.0060
## 10 0.9181 nan 1.0000 -0.0003
## 20 0.8895 nan 1.0000 -0.0052
## 40 0.8366 nan 1.0000 -0.0158
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1091 nan 1.0000 0.0818
## 2 1.0335 nan 1.0000 0.0373
## 3 1.0102 nan 1.0000 -0.0093
## 4 0.9735 nan 1.0000 0.0126
## 5 0.9718 nan 1.0000 -0.0133
## 6 0.9553 nan 1.0000 -0.0019
## 7 0.9498 nan 1.0000 -0.0175
## 8 0.9368 nan 1.0000 -0.0134
## 9 0.9347 nan 1.0000 -0.0055
## 10 0.9413 nan 1.0000 -0.0209
## 20 0.9194 nan 1.0000 -0.0185
## 40 inf nan 1.0000 nan
## 60 46073074850.7146 nan 1.0000 -0.4585
## 80 46073074851.7939 nan 1.0000 -0.0034
## 100 46073080803.2583 nan 1.0000 -145.3566
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0550 nan 1.0000 0.1031
## 2 0.9934 nan 1.0000 0.0220
## 3 0.9770 nan 1.0000 -0.0213
## 4 0.9603 nan 1.0000 -0.0184
## 5 0.9533 nan 1.0000 -0.0358
## 6 0.9465 nan 1.0000 -0.0199
## 7 1.0447 nan 1.0000 -0.1426
## 8 0.9964 nan 1.0000 0.0143
## 9 0.9773 nan 1.0000 -0.0054
## 10 0.9718 nan 1.0000 -0.0233
## 20 1.0984 nan 1.0000 -0.1315
## 40 6546938.5132 nan 1.0000 0.0033
## 60 6546938.0698 nan 1.0000 -0.0026
## 80 6549694.2249 nan 1.0000 -0.2204
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0601 nan 1.0000 0.1224
## 2 0.9886 nan 1.0000 0.0221
## 3 0.9339 nan 1.0000 0.0197
## 4 0.9083 nan 1.0000 -0.0087
## 5 0.9177 nan 1.0000 -0.0291
## 6 0.9270 nan 1.0000 -0.0284
## 7 0.9009 nan 1.0000 -0.0000
## 8 0.8565 nan 1.0000 0.0117
## 9 0.8708 nan 1.0000 -0.0392
## 10 0.8881 nan 1.0000 -0.0355
## 20 0.8126 nan 1.0000 -0.0501
## 40 914738289765.2891 nan 1.0000 -0.0137
## 60 914738289765.1559 nan 1.0000 -0.0161
## 80 914738289765.1772 nan 1.0000 -0.0098
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0606 nan 1.0000 0.0904
## 2 0.9622 nan 1.0000 0.0435
## 3 0.9412 nan 1.0000 -0.0142
## 4 0.9173 nan 1.0000 -0.0021
## 5 0.9025 nan 1.0000 -0.0067
## 6 0.9058 nan 1.0000 -0.0261
## 7 0.8888 nan 1.0000 -0.0153
## 8 0.9150 nan 1.0000 -0.0470
## 9 0.9267 nan 1.0000 -0.0365
## 10 0.8939 nan 1.0000 -0.0103
## 20 0.9896 nan 1.0000 -0.0726
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9921 nan 1.0000 0.1450
## 2 0.9267 nan 1.0000 0.0206
## 3 0.9218 nan 1.0000 -0.0479
## 4 0.9224 nan 1.0000 -0.0522
## 5 0.9008 nan 1.0000 -0.0258
## 6 0.9223 nan 1.0000 -0.0618
## 7 0.9334 nan 1.0000 -0.0671
## 8 0.9207 nan 1.0000 -0.0325
## 9 0.8915 nan 1.0000 -0.0188
## 10 0.8970 nan 1.0000 -0.0555
## 20 2.3337 nan 1.0000 -0.0284
## 40 4.3426 nan 1.0000 -0.0052
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0306 nan 1.0000 0.1127
## 2 0.9492 nan 1.0000 0.0042
## 3 0.9292 nan 1.0000 -0.0376
## 4 0.9185 nan 1.0000 -0.0399
## 5 0.9414 nan 1.0000 -0.0671
## 6 0.9513 nan 1.0000 -0.0555
## 7 0.9515 nan 1.0000 -0.0307
## 8 0.9443 nan 1.0000 -0.0387
## 9 0.9119 nan 1.0000 -0.0128
## 10 0.9021 nan 1.0000 -0.0225
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 200 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0219 nan 1.0000 0.1064
## 2 0.9360 nan 1.0000 0.0274
## 3 0.9362 nan 1.0000 -0.0443
## 4 0.9026 nan 1.0000 -0.0101
## 5 0.8893 nan 1.0000 -0.0163
## 6 0.8932 nan 1.0000 -0.0272
## 7 0.8879 nan 1.0000 -0.0245
## 8 0.8995 nan 1.0000 -0.0517
## 9 0.8728 nan 1.0000 -0.0229
## 10 0.8635 nan 1.0000 -0.0411
## 20 inf nan 1.0000 -inf
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
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## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
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## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 40 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 360 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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## 480 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2927 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0001
## 6 1.2913 nan 0.0010 0.0002
## 7 1.2910 nan 0.0010 0.0002
## 8 1.2906 nan 0.0010 0.0002
## 9 1.2902 nan 0.0010 0.0002
## 10 1.2898 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0001
## 40 1.2792 nan 0.0010 0.0001
## 60 1.2724 nan 0.0010 0.0002
## 80 1.2659 nan 0.0010 0.0001
## 100 1.2595 nan 0.0010 0.0001
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## 140 1.2474 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2927 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
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## 10 1.2896 nan 0.0010 0.0002
## 20 1.2858 nan 0.0010 0.0002
## 40 1.2789 nan 0.0010 0.0002
## 60 1.2722 nan 0.0010 0.0001
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## 140 1.2474 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
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## 10 1.2897 nan 0.0010 0.0001
## 20 1.2864 nan 0.0010 0.0002
## 40 1.2791 nan 0.0010 0.0002
## 60 1.2723 nan 0.0010 0.0001
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## 140 1.2478 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2913 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2895 nan 0.0010 0.0002
## 9 1.2890 nan 0.0010 0.0002
## 10 1.2885 nan 0.0010 0.0003
## 20 1.2840 nan 0.0010 0.0002
## 40 1.2749 nan 0.0010 0.0002
## 60 1.2662 nan 0.0010 0.0002
## 80 1.2577 nan 0.0010 0.0002
## 100 1.2491 nan 0.0010 0.0002
## 120 1.2410 nan 0.0010 0.0002
## 140 1.2332 nan 0.0010 0.0001
## 160 1.2258 nan 0.0010 0.0002
## 180 1.2185 nan 0.0010 0.0001
## 200 1.2114 nan 0.0010 0.0002
## 220 1.2047 nan 0.0010 0.0001
## 240 1.1979 nan 0.0010 0.0001
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## 380 1.1551 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2895 nan 0.0010 0.0002
## 9 1.2890 nan 0.0010 0.0002
## 10 1.2885 nan 0.0010 0.0002
## 20 1.2838 nan 0.0010 0.0002
## 40 1.2746 nan 0.0010 0.0002
## 60 1.2658 nan 0.0010 0.0002
## 80 1.2573 nan 0.0010 0.0002
## 100 1.2492 nan 0.0010 0.0002
## 120 1.2412 nan 0.0010 0.0002
## 140 1.2334 nan 0.0010 0.0002
## 160 1.2259 nan 0.0010 0.0002
## 180 1.2185 nan 0.0010 0.0002
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## 220 1.2043 nan 0.0010 0.0001
## 240 1.1977 nan 0.0010 0.0001
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## 280 1.1848 nan 0.0010 0.0001
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## 380 1.1556 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2895 nan 0.0010 0.0002
## 9 1.2891 nan 0.0010 0.0002
## 10 1.2886 nan 0.0010 0.0002
## 20 1.2839 nan 0.0010 0.0002
## 40 1.2747 nan 0.0010 0.0002
## 60 1.2660 nan 0.0010 0.0002
## 80 1.2573 nan 0.0010 0.0002
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## 120 1.2409 nan 0.0010 0.0002
## 140 1.2330 nan 0.0010 0.0002
## 160 1.2254 nan 0.0010 0.0002
## 180 1.2182 nan 0.0010 0.0001
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## 220 1.2041 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2916 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0003
## 5 1.2905 nan 0.0010 0.0003
## 6 1.2899 nan 0.0010 0.0003
## 7 1.2894 nan 0.0010 0.0002
## 8 1.2889 nan 0.0010 0.0002
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## 10 1.2878 nan 0.0010 0.0003
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## 40 1.2719 nan 0.0010 0.0002
## 60 1.2617 nan 0.0010 0.0002
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## 140 1.2237 nan 0.0010 0.0002
## 160 1.2150 nan 0.0010 0.0002
## 180 1.2067 nan 0.0010 0.0001
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## 280 1.1680 nan 0.0010 0.0002
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## 320 1.1540 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2916 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2900 nan 0.0010 0.0003
## 7 1.2894 nan 0.0010 0.0002
## 8 1.2888 nan 0.0010 0.0003
## 9 1.2883 nan 0.0010 0.0003
## 10 1.2877 nan 0.0010 0.0003
## 20 1.2823 nan 0.0010 0.0002
## 40 1.2716 nan 0.0010 0.0002
## 60 1.2614 nan 0.0010 0.0002
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## 140 1.2241 nan 0.0010 0.0002
## 160 1.2151 nan 0.0010 0.0002
## 180 1.2065 nan 0.0010 0.0002
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## 380 1.1339 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0003
## 4 1.2911 nan 0.0010 0.0003
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2900 nan 0.0010 0.0003
## 7 1.2894 nan 0.0010 0.0003
## 8 1.2888 nan 0.0010 0.0002
## 9 1.2882 nan 0.0010 0.0003
## 10 1.2877 nan 0.0010 0.0003
## 20 1.2821 nan 0.0010 0.0003
## 40 1.2718 nan 0.0010 0.0002
## 60 1.2616 nan 0.0010 0.0002
## 80 1.2518 nan 0.0010 0.0002
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## 140 1.2242 nan 0.0010 0.0002
## 160 1.2156 nan 0.0010 0.0001
## 180 1.2069 nan 0.0010 0.0002
## 200 1.1985 nan 0.0010 0.0002
## 220 1.1906 nan 0.0010 0.0002
## 240 1.1829 nan 0.0010 0.0002
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## 320 1.1542 nan 0.0010 0.0001
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## 380 1.1347 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2567 nan 0.1000 0.0151
## 2 1.2252 nan 0.1000 0.0122
## 3 1.2029 nan 0.1000 0.0122
## 4 1.1788 nan 0.1000 0.0088
## 5 1.1593 nan 0.1000 0.0064
## 6 1.1446 nan 0.1000 0.0058
## 7 1.1291 nan 0.1000 0.0064
## 8 1.1167 nan 0.1000 0.0056
## 9 1.1028 nan 0.1000 0.0060
## 10 1.0936 nan 0.1000 0.0039
## 20 1.0079 nan 0.1000 0.0017
## 40 0.9284 nan 0.1000 0.0013
## 60 0.8847 nan 0.1000 -0.0002
## 80 0.8571 nan 0.1000 -0.0005
## 100 0.8313 nan 0.1000 -0.0011
## 120 0.8184 nan 0.1000 -0.0004
## 140 0.8090 nan 0.1000 -0.0011
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## 180 0.7889 nan 0.1000 -0.0005
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## 240 0.7610 nan 0.1000 -0.0015
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## 280 0.7451 nan 0.1000 -0.0005
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## 460 0.6984 nan 0.1000 -0.0009
## 480 0.6917 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2583 nan 0.1000 0.0164
## 2 1.2251 nan 0.1000 0.0115
## 3 1.2009 nan 0.1000 0.0103
## 4 1.1785 nan 0.1000 0.0083
## 5 1.1612 nan 0.1000 0.0071
## 6 1.1428 nan 0.1000 0.0062
## 7 1.1308 nan 0.1000 0.0057
## 8 1.1170 nan 0.1000 0.0067
## 9 1.1021 nan 0.1000 0.0056
## 10 1.0900 nan 0.1000 0.0048
## 20 1.0058 nan 0.1000 0.0020
## 40 0.9251 nan 0.1000 0.0001
## 60 0.8808 nan 0.1000 -0.0003
## 80 0.8507 nan 0.1000 -0.0006
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## 140 0.7972 nan 0.1000 -0.0007
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## 180 0.7806 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2634 nan 0.1000 0.0133
## 2 1.2362 nan 0.1000 0.0136
## 3 1.2102 nan 0.1000 0.0119
## 4 1.1892 nan 0.1000 0.0112
## 5 1.1706 nan 0.1000 0.0086
## 6 1.1535 nan 0.1000 0.0071
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## 8 1.1235 nan 0.1000 0.0054
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## 20 1.0045 nan 0.1000 0.0020
## 40 0.9232 nan 0.1000 -0.0006
## 60 0.8845 nan 0.1000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2483 nan 0.1000 0.0185
## 2 1.2139 nan 0.1000 0.0127
## 3 1.1811 nan 0.1000 0.0163
## 4 1.1479 nan 0.1000 0.0127
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## 6 1.1008 nan 0.1000 0.0105
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## 8 1.0638 nan 0.1000 0.0060
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2485 nan 0.1000 0.0179
## 2 1.2119 nan 0.1000 0.0154
## 3 1.1793 nan 0.1000 0.0149
## 4 1.1485 nan 0.1000 0.0132
## 5 1.1230 nan 0.1000 0.0110
## 6 1.1009 nan 0.1000 0.0085
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## 9 1.0537 nan 0.1000 0.0022
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## 240 0.5897 nan 0.1000 -0.0012
## 260 0.5741 nan 0.1000 -0.0019
## 280 0.5586 nan 0.1000 -0.0014
## 300 0.5435 nan 0.1000 -0.0003
## 320 0.5327 nan 0.1000 -0.0013
## 340 0.5204 nan 0.1000 -0.0008
## 360 0.5072 nan 0.1000 -0.0003
## 380 0.4959 nan 0.1000 -0.0004
## 400 0.4857 nan 0.1000 -0.0009
## 420 0.4755 nan 0.1000 -0.0005
## 440 0.4646 nan 0.1000 -0.0011
## 460 0.4535 nan 0.1000 -0.0011
## 480 0.4405 nan 0.1000 -0.0023
## 500 0.4330 nan 0.1000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2489 nan 0.1000 0.0207
## 2 1.2143 nan 0.1000 0.0169
## 3 1.1834 nan 0.1000 0.0147
## 4 1.1512 nan 0.1000 0.0128
## 5 1.1301 nan 0.1000 0.0105
## 6 1.1072 nan 0.1000 0.0076
## 7 1.0853 nan 0.1000 0.0083
## 8 1.0664 nan 0.1000 0.0076
## 9 1.0528 nan 0.1000 0.0052
## 10 1.0375 nan 0.1000 0.0067
## 20 0.9411 nan 0.1000 0.0009
## 40 0.8567 nan 0.1000 -0.0013
## 60 0.8035 nan 0.1000 -0.0016
## 80 0.7736 nan 0.1000 -0.0004
## 100 0.7465 nan 0.1000 -0.0019
## 120 0.7181 nan 0.1000 -0.0011
## 140 0.6949 nan 0.1000 -0.0017
## 160 0.6721 nan 0.1000 -0.0007
## 180 0.6478 nan 0.1000 -0.0020
## 200 0.6298 nan 0.1000 -0.0020
## 220 0.6094 nan 0.1000 -0.0013
## 240 0.5921 nan 0.1000 -0.0002
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## 280 0.5618 nan 0.1000 -0.0020
## 300 0.5480 nan 0.1000 -0.0019
## 320 0.5310 nan 0.1000 -0.0014
## 340 0.5177 nan 0.1000 -0.0003
## 360 0.5024 nan 0.1000 -0.0015
## 380 0.4906 nan 0.1000 -0.0016
## 400 0.4784 nan 0.1000 -0.0004
## 420 0.4662 nan 0.1000 -0.0003
## 440 0.4583 nan 0.1000 -0.0000
## 460 0.4492 nan 0.1000 -0.0005
## 480 0.4384 nan 0.1000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2416 nan 0.1000 0.0227
## 2 1.1956 nan 0.1000 0.0190
## 3 1.1565 nan 0.1000 0.0156
## 4 1.1234 nan 0.1000 0.0146
## 5 1.0931 nan 0.1000 0.0128
## 6 1.0694 nan 0.1000 0.0103
## 7 1.0498 nan 0.1000 0.0056
## 8 1.0302 nan 0.1000 0.0058
## 9 1.0114 nan 0.1000 0.0049
## 10 0.9965 nan 0.1000 0.0054
## 20 0.8951 nan 0.1000 0.0013
## 40 0.7985 nan 0.1000 -0.0007
## 60 0.7344 nan 0.1000 -0.0028
## 80 0.6896 nan 0.1000 -0.0013
## 100 0.6417 nan 0.1000 -0.0015
## 120 0.6069 nan 0.1000 -0.0011
## 140 0.5737 nan 0.1000 -0.0016
## 160 0.5464 nan 0.1000 -0.0016
## 180 0.5205 nan 0.1000 -0.0004
## 200 0.4924 nan 0.1000 -0.0002
## 220 0.4716 nan 0.1000 -0.0002
## 240 0.4438 nan 0.1000 -0.0013
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## 280 0.4079 nan 0.1000 -0.0012
## 300 0.3905 nan 0.1000 -0.0005
## 320 0.3748 nan 0.1000 -0.0009
## 340 0.3597 nan 0.1000 -0.0010
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## 380 0.3312 nan 0.1000 -0.0005
## 400 0.3167 nan 0.1000 -0.0001
## 420 0.3058 nan 0.1000 -0.0006
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## 460 0.2859 nan 0.1000 -0.0009
## 480 0.2752 nan 0.1000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2391 nan 0.1000 0.0205
## 2 1.1914 nan 0.1000 0.0212
## 3 1.1541 nan 0.1000 0.0165
## 4 1.1216 nan 0.1000 0.0123
## 5 1.0873 nan 0.1000 0.0128
## 6 1.0671 nan 0.1000 0.0064
## 7 1.0478 nan 0.1000 0.0056
## 8 1.0245 nan 0.1000 0.0088
## 9 1.0051 nan 0.1000 0.0066
## 10 0.9946 nan 0.1000 0.0008
## 20 0.8939 nan 0.1000 0.0020
## 40 0.7965 nan 0.1000 -0.0015
## 60 0.7364 nan 0.1000 -0.0013
## 80 0.6868 nan 0.1000 0.0000
## 100 0.6406 nan 0.1000 -0.0007
## 120 0.6090 nan 0.1000 -0.0019
## 140 0.5793 nan 0.1000 -0.0006
## 160 0.5553 nan 0.1000 -0.0011
## 180 0.5273 nan 0.1000 -0.0013
## 200 0.5046 nan 0.1000 0.0003
## 220 0.4823 nan 0.1000 -0.0010
## 240 0.4619 nan 0.1000 -0.0008
## 260 0.4404 nan 0.1000 -0.0005
## 280 0.4200 nan 0.1000 -0.0017
## 300 0.4022 nan 0.1000 -0.0006
## 320 0.3879 nan 0.1000 -0.0007
## 340 0.3724 nan 0.1000 -0.0005
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## 380 0.3429 nan 0.1000 -0.0008
## 400 0.3310 nan 0.1000 -0.0010
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## 460 0.2914 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2421 nan 0.1000 0.0236
## 2 1.2030 nan 0.1000 0.0150
## 3 1.1665 nan 0.1000 0.0159
## 4 1.1342 nan 0.1000 0.0110
## 5 1.1050 nan 0.1000 0.0119
## 6 1.0745 nan 0.1000 0.0121
## 7 1.0532 nan 0.1000 0.0080
## 8 1.0325 nan 0.1000 0.0078
## 9 1.0159 nan 0.1000 0.0054
## 10 0.9962 nan 0.1000 0.0045
## 20 0.8983 nan 0.1000 0.0007
## 40 0.7928 nan 0.1000 -0.0007
## 60 0.7352 nan 0.1000 -0.0016
## 80 0.6923 nan 0.1000 -0.0018
## 100 0.6518 nan 0.1000 -0.0019
## 120 0.6198 nan 0.1000 0.0001
## 140 0.5857 nan 0.1000 -0.0010
## 160 0.5593 nan 0.1000 -0.0013
## 180 0.5325 nan 0.1000 -0.0002
## 200 0.5050 nan 0.1000 -0.0013
## 220 0.4834 nan 0.1000 -0.0009
## 240 0.4625 nan 0.1000 -0.0003
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## 280 0.4240 nan 0.1000 -0.0006
## 300 0.4081 nan 0.1000 -0.0008
## 320 0.3895 nan 0.1000 -0.0013
## 340 0.3710 nan 0.1000 -0.0010
## 360 0.3531 nan 0.1000 -0.0015
## 380 0.3383 nan 0.1000 -0.0010
## 400 0.3258 nan 0.1000 -0.0011
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## 440 0.3023 nan 0.1000 -0.0008
## 460 0.2909 nan 0.1000 -0.0004
## 480 0.2807 nan 0.1000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2266 nan 0.2000 0.0333
## 2 1.1838 nan 0.2000 0.0182
## 3 1.1482 nan 0.2000 0.0169
## 4 1.1204 nan 0.2000 0.0115
## 5 1.0973 nan 0.2000 0.0103
## 6 1.0719 nan 0.2000 0.0102
## 7 1.0542 nan 0.2000 0.0080
## 8 1.0405 nan 0.2000 0.0010
## 9 1.0301 nan 0.2000 0.0016
## 10 1.0121 nan 0.2000 0.0058
## 20 0.9238 nan 0.2000 -0.0009
## 40 0.8507 nan 0.2000 0.0002
## 60 0.8156 nan 0.2000 -0.0020
## 80 0.7960 nan 0.2000 -0.0030
## 100 0.7757 nan 0.2000 -0.0013
## 120 0.7598 nan 0.2000 -0.0010
## 140 0.7428 nan 0.2000 -0.0028
## 160 0.7277 nan 0.2000 -0.0014
## 180 0.7176 nan 0.2000 -0.0016
## 200 0.7054 nan 0.2000 -0.0008
## 220 0.6967 nan 0.2000 -0.0017
## 240 0.6850 nan 0.2000 -0.0009
## 260 0.6782 nan 0.2000 -0.0021
## 280 0.6735 nan 0.2000 -0.0013
## 300 0.6675 nan 0.2000 -0.0036
## 320 0.6592 nan 0.2000 -0.0025
## 340 0.6558 nan 0.2000 -0.0027
## 360 0.6469 nan 0.2000 -0.0033
## 380 0.6410 nan 0.2000 -0.0002
## 400 0.6352 nan 0.2000 -0.0021
## 420 0.6279 nan 0.2000 -0.0014
## 440 0.6234 nan 0.2000 -0.0013
## 460 0.6159 nan 0.2000 -0.0005
## 480 0.6068 nan 0.2000 -0.0014
## 500 0.6034 nan 0.2000 -0.0031
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2330 nan 0.2000 0.0268
## 2 1.1858 nan 0.2000 0.0234
## 3 1.1441 nan 0.2000 0.0183
## 4 1.1182 nan 0.2000 0.0088
## 5 1.0902 nan 0.2000 0.0121
## 6 1.0650 nan 0.2000 0.0108
## 7 1.0489 nan 0.2000 0.0044
## 8 1.0336 nan 0.2000 0.0066
## 9 1.0175 nan 0.2000 0.0041
## 10 1.0113 nan 0.2000 0.0005
## 20 0.9236 nan 0.2000 0.0020
## 40 0.8536 nan 0.2000 -0.0009
## 60 0.8295 nan 0.2000 -0.0029
## 80 0.8012 nan 0.2000 -0.0012
## 100 0.7827 nan 0.2000 -0.0014
## 120 0.7693 nan 0.2000 -0.0012
## 140 0.7541 nan 0.2000 -0.0022
## 160 0.7446 nan 0.2000 -0.0027
## 180 0.7296 nan 0.2000 -0.0018
## 200 0.7231 nan 0.2000 -0.0024
## 220 0.7079 nan 0.2000 -0.0007
## 240 0.6991 nan 0.2000 -0.0020
## 260 0.6916 nan 0.2000 -0.0032
## 280 0.6822 nan 0.2000 -0.0011
## 300 0.6725 nan 0.2000 -0.0026
## 320 0.6628 nan 0.2000 -0.0009
## 340 0.6549 nan 0.2000 -0.0017
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## 380 0.6419 nan 0.2000 -0.0013
## 400 0.6359 nan 0.2000 -0.0003
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## 460 0.6164 nan 0.2000 -0.0017
## 480 0.6115 nan 0.2000 -0.0028
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2276 nan 0.2000 0.0331
## 2 1.1831 nan 0.2000 0.0169
## 3 1.1463 nan 0.2000 0.0139
## 4 1.1206 nan 0.2000 0.0078
## 5 1.0923 nan 0.2000 0.0113
## 6 1.0706 nan 0.2000 0.0071
## 7 1.0511 nan 0.2000 0.0103
## 8 1.0334 nan 0.2000 0.0064
## 9 1.0225 nan 0.2000 0.0042
## 10 1.0080 nan 0.2000 0.0061
## 20 0.9297 nan 0.2000 0.0016
## 40 0.8638 nan 0.2000 -0.0023
## 60 0.8224 nan 0.2000 -0.0024
## 80 0.7935 nan 0.2000 -0.0024
## 100 0.7823 nan 0.2000 -0.0027
## 120 0.7683 nan 0.2000 -0.0020
## 140 0.7586 nan 0.2000 -0.0023
## 160 0.7420 nan 0.2000 -0.0024
## 180 0.7295 nan 0.2000 -0.0021
## 200 0.7182 nan 0.2000 -0.0026
## 220 0.7075 nan 0.2000 -0.0029
## 240 0.6990 nan 0.2000 -0.0003
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## 280 0.6848 nan 0.2000 -0.0008
## 300 0.6747 nan 0.2000 -0.0038
## 320 0.6664 nan 0.2000 -0.0019
## 340 0.6600 nan 0.2000 -0.0007
## 360 0.6529 nan 0.2000 -0.0012
## 380 0.6461 nan 0.2000 -0.0021
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2034 nan 0.2000 0.0388
## 2 1.1403 nan 0.2000 0.0233
## 3 1.0958 nan 0.2000 0.0184
## 4 1.0595 nan 0.2000 0.0156
## 5 1.0294 nan 0.2000 0.0113
## 6 1.0054 nan 0.2000 0.0090
## 7 0.9812 nan 0.2000 0.0073
## 8 0.9652 nan 0.2000 0.0025
## 9 0.9509 nan 0.2000 0.0013
## 10 0.9410 nan 0.2000 0.0007
## 20 0.8579 nan 0.2000 0.0005
## 40 0.7771 nan 0.2000 -0.0014
## 60 0.7236 nan 0.2000 -0.0027
## 80 0.6754 nan 0.2000 -0.0030
## 100 0.6375 nan 0.2000 -0.0018
## 120 0.5984 nan 0.2000 -0.0018
## 140 0.5615 nan 0.2000 -0.0031
## 160 0.5295 nan 0.2000 -0.0021
## 180 0.5011 nan 0.2000 -0.0004
## 200 0.4773 nan 0.2000 -0.0025
## 220 0.4521 nan 0.2000 -0.0013
## 240 0.4272 nan 0.2000 -0.0016
## 260 0.4055 nan 0.2000 -0.0009
## 280 0.3897 nan 0.2000 -0.0008
## 300 0.3742 nan 0.2000 -0.0004
## 320 0.3565 nan 0.2000 -0.0015
## 340 0.3448 nan 0.2000 -0.0012
## 360 0.3305 nan 0.2000 -0.0005
## 380 0.3178 nan 0.2000 -0.0012
## 400 0.3069 nan 0.2000 -0.0011
## 420 0.2959 nan 0.2000 -0.0015
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## 460 0.2720 nan 0.2000 -0.0021
## 480 0.2615 nan 0.2000 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1997 nan 0.2000 0.0376
## 2 1.1463 nan 0.2000 0.0239
## 3 1.0949 nan 0.2000 0.0202
## 4 1.0573 nan 0.2000 0.0168
## 5 1.0234 nan 0.2000 0.0133
## 6 1.0007 nan 0.2000 0.0074
## 7 0.9798 nan 0.2000 0.0064
## 8 0.9647 nan 0.2000 0.0009
## 9 0.9538 nan 0.2000 0.0015
## 10 0.9445 nan 0.2000 0.0002
## 20 0.8574 nan 0.2000 0.0001
## 40 0.7754 nan 0.2000 -0.0046
## 60 0.7198 nan 0.2000 -0.0024
## 80 0.6777 nan 0.2000 -0.0043
## 100 0.6313 nan 0.2000 -0.0011
## 120 0.5990 nan 0.2000 -0.0018
## 140 0.5653 nan 0.2000 -0.0034
## 160 0.5245 nan 0.2000 -0.0037
## 180 0.5024 nan 0.2000 -0.0016
## 200 0.4748 nan 0.2000 -0.0028
## 220 0.4497 nan 0.2000 -0.0025
## 240 0.4360 nan 0.2000 -0.0021
## 260 0.4179 nan 0.2000 -0.0039
## 280 0.4067 nan 0.2000 -0.0024
## 300 0.3882 nan 0.2000 -0.0036
## 320 0.3711 nan 0.2000 -0.0008
## 340 0.3553 nan 0.2000 -0.0021
## 360 0.3420 nan 0.2000 -0.0011
## 380 0.3302 nan 0.2000 -0.0002
## 400 0.3169 nan 0.2000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2139 nan 0.2000 0.0355
## 2 1.1434 nan 0.2000 0.0341
## 3 1.0941 nan 0.2000 0.0164
## 4 1.0554 nan 0.2000 0.0144
## 5 1.0267 nan 0.2000 0.0137
## 6 1.0044 nan 0.2000 0.0074
## 7 0.9819 nan 0.2000 0.0049
## 8 0.9649 nan 0.2000 0.0041
## 9 0.9532 nan 0.2000 0.0011
## 10 0.9372 nan 0.2000 0.0038
## 20 0.8504 nan 0.2000 -0.0008
## 40 0.7573 nan 0.2000 0.0001
## 60 0.7201 nan 0.2000 -0.0031
## 80 0.6638 nan 0.2000 -0.0028
## 100 0.6175 nan 0.2000 -0.0020
## 120 0.5893 nan 0.2000 -0.0042
## 140 0.5673 nan 0.2000 -0.0016
## 160 0.5421 nan 0.2000 -0.0023
## 180 0.5145 nan 0.2000 -0.0029
## 200 0.4885 nan 0.2000 -0.0013
## 220 0.4660 nan 0.2000 -0.0010
## 240 0.4465 nan 0.2000 -0.0026
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## 280 0.4046 nan 0.2000 -0.0011
## 300 0.3876 nan 0.2000 -0.0015
## 320 0.3740 nan 0.2000 -0.0028
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## 380 0.3300 nan 0.2000 -0.0001
## 400 0.3138 nan 0.2000 -0.0017
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## 480 0.2718 nan 0.2000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1968 nan 0.2000 0.0432
## 2 1.1341 nan 0.2000 0.0253
## 3 1.0793 nan 0.2000 0.0208
## 4 1.0479 nan 0.2000 0.0088
## 5 1.0135 nan 0.2000 0.0142
## 6 0.9799 nan 0.2000 0.0133
## 7 0.9530 nan 0.2000 0.0067
## 8 0.9338 nan 0.2000 0.0048
## 9 0.9167 nan 0.2000 0.0033
## 10 0.9016 nan 0.2000 0.0020
## 20 0.8102 nan 0.2000 -0.0011
## 40 0.7096 nan 0.2000 -0.0030
## 60 0.6517 nan 0.2000 -0.0033
## 80 0.5936 nan 0.2000 -0.0045
## 100 0.5358 nan 0.2000 -0.0004
## 120 0.4884 nan 0.2000 -0.0036
## 140 0.4514 nan 0.2000 -0.0040
## 160 0.4112 nan 0.2000 -0.0050
## 180 0.3710 nan 0.2000 -0.0028
## 200 0.3461 nan 0.2000 -0.0014
## 220 0.3155 nan 0.2000 -0.0025
## 240 0.2917 nan 0.2000 -0.0004
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## 280 0.2466 nan 0.2000 -0.0010
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## 380 0.1661 nan 0.2000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1961 nan 0.2000 0.0478
## 2 1.1317 nan 0.2000 0.0228
## 3 1.0873 nan 0.2000 0.0153
## 4 1.0409 nan 0.2000 0.0160
## 5 1.0038 nan 0.2000 0.0098
## 6 0.9733 nan 0.2000 0.0063
## 7 0.9529 nan 0.2000 0.0046
## 8 0.9375 nan 0.2000 0.0043
## 9 0.9175 nan 0.2000 0.0065
## 10 0.9032 nan 0.2000 -0.0001
## 20 0.8120 nan 0.2000 -0.0015
## 40 0.6958 nan 0.2000 -0.0035
## 60 0.6141 nan 0.2000 -0.0044
## 80 0.5535 nan 0.2000 -0.0026
## 100 0.5019 nan 0.2000 -0.0013
## 120 0.4632 nan 0.2000 -0.0055
## 140 0.4284 nan 0.2000 -0.0010
## 160 0.3946 nan 0.2000 -0.0034
## 180 0.3632 nan 0.2000 -0.0009
## 200 0.3350 nan 0.2000 -0.0026
## 220 0.3092 nan 0.2000 -0.0013
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1978 nan 0.2000 0.0422
## 2 1.1265 nan 0.2000 0.0261
## 3 1.0804 nan 0.2000 0.0151
## 4 1.0491 nan 0.2000 0.0079
## 5 1.0130 nan 0.2000 0.0125
## 6 0.9784 nan 0.2000 0.0117
## 7 0.9503 nan 0.2000 0.0024
## 8 0.9253 nan 0.2000 0.0074
## 9 0.9104 nan 0.2000 -0.0006
## 10 0.8998 nan 0.2000 -0.0024
## 20 0.8040 nan 0.2000 -0.0004
## 40 0.7034 nan 0.2000 -0.0012
## 60 0.6273 nan 0.2000 -0.0027
## 80 0.5700 nan 0.2000 -0.0020
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## 120 0.4711 nan 0.2000 -0.0020
## 140 0.4381 nan 0.2000 -0.0029
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## 180 0.3631 nan 0.2000 -0.0019
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## 240 0.2918 nan 0.2000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2030 nan 0.3000 0.0373
## 2 1.1451 nan 0.3000 0.0188
## 3 1.1163 nan 0.3000 0.0094
## 4 1.0825 nan 0.3000 0.0093
## 5 1.0509 nan 0.3000 0.0083
## 6 1.0211 nan 0.3000 0.0159
## 7 1.0008 nan 0.3000 0.0076
## 8 0.9915 nan 0.3000 -0.0004
## 9 0.9806 nan 0.3000 0.0016
## 10 0.9716 nan 0.3000 -0.0012
## 20 0.9009 nan 0.3000 0.0007
## 40 0.8310 nan 0.3000 -0.0042
## 60 0.8035 nan 0.3000 -0.0013
## 80 0.7835 nan 0.3000 -0.0008
## 100 0.7705 nan 0.3000 -0.0037
## 120 0.7559 nan 0.3000 -0.0065
## 140 0.7297 nan 0.3000 -0.0022
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## 180 0.6966 nan 0.3000 -0.0035
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## 220 0.6677 nan 0.3000 -0.0037
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## 400 0.5807 nan 0.3000 -0.0033
## 420 0.5690 nan 0.3000 -0.0040
## 440 0.5617 nan 0.3000 -0.0011
## 460 0.5571 nan 0.3000 -0.0031
## 480 0.5492 nan 0.3000 -0.0005
## 500 0.5462 nan 0.3000 -0.0019
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1994 nan 0.3000 0.0383
## 2 1.1580 nan 0.3000 0.0188
## 3 1.1063 nan 0.3000 0.0217
## 4 1.0716 nan 0.3000 0.0154
## 5 1.0464 nan 0.3000 0.0112
## 6 1.0289 nan 0.3000 0.0030
## 7 1.0039 nan 0.3000 0.0054
## 8 0.9854 nan 0.3000 0.0063
## 9 0.9742 nan 0.3000 -0.0001
## 10 0.9561 nan 0.3000 0.0069
## 20 0.8826 nan 0.3000 -0.0030
## 40 0.8326 nan 0.3000 -0.0036
## 60 0.7972 nan 0.3000 -0.0023
## 80 0.7702 nan 0.3000 -0.0025
## 100 0.7558 nan 0.3000 -0.0021
## 120 0.7349 nan 0.3000 -0.0041
## 140 0.7213 nan 0.3000 -0.0021
## 160 0.6967 nan 0.3000 -0.0041
## 180 0.6870 nan 0.3000 -0.0058
## 200 0.6743 nan 0.3000 -0.0022
## 220 0.6633 nan 0.3000 -0.0017
## 240 0.6537 nan 0.3000 -0.0034
## 260 0.6422 nan 0.3000 -0.0024
## 280 0.6375 nan 0.3000 -0.0060
## 300 0.6271 nan 0.3000 -0.0040
## 320 0.6198 nan 0.3000 -0.0025
## 340 0.6045 nan 0.3000 -0.0005
## 360 0.6001 nan 0.3000 -0.0026
## 380 0.5916 nan 0.3000 -0.0019
## 400 0.5845 nan 0.3000 -0.0019
## 420 0.5810 nan 0.3000 0.0003
## 440 0.5706 nan 0.3000 -0.0016
## 460 0.5638 nan 0.3000 -0.0015
## 480 0.5616 nan 0.3000 -0.0031
## 500 0.5534 nan 0.3000 -0.0019
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1948 nan 0.3000 0.0419
## 2 1.1401 nan 0.3000 0.0232
## 3 1.1038 nan 0.3000 0.0176
## 4 1.0743 nan 0.3000 0.0082
## 5 1.0357 nan 0.3000 0.0132
## 6 1.0126 nan 0.3000 0.0067
## 7 0.9991 nan 0.3000 0.0009
## 8 0.9859 nan 0.3000 0.0038
## 9 0.9684 nan 0.3000 0.0035
## 10 0.9522 nan 0.3000 0.0047
## 20 0.8846 nan 0.3000 -0.0026
## 40 0.8171 nan 0.3000 -0.0022
## 60 0.7870 nan 0.3000 -0.0007
## 80 0.7586 nan 0.3000 -0.0010
## 100 0.7455 nan 0.3000 -0.0007
## 120 0.7276 nan 0.3000 -0.0051
## 140 0.7131 nan 0.3000 -0.0028
## 160 0.6984 nan 0.3000 -0.0018
## 180 0.6857 nan 0.3000 -0.0023
## 200 0.6778 nan 0.3000 -0.0044
## 220 0.6656 nan 0.3000 -0.0047
## 240 0.6515 nan 0.3000 -0.0024
## 260 0.6437 nan 0.3000 -0.0027
## 280 0.6335 nan 0.3000 -0.0034
## 300 0.6251 nan 0.3000 -0.0023
## 320 0.6169 nan 0.3000 -0.0016
## 340 0.6110 nan 0.3000 -0.0025
## 360 0.6045 nan 0.3000 -0.0021
## 380 0.5963 nan 0.3000 -0.0027
## 400 0.5923 nan 0.3000 -0.0016
## 420 0.5848 nan 0.3000 -0.0057
## 440 0.5776 nan 0.3000 -0.0033
## 460 0.5723 nan 0.3000 -0.0035
## 480 0.5645 nan 0.3000 -0.0040
## 500 0.5607 nan 0.3000 -0.0018
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1852 nan 0.3000 0.0545
## 2 1.0894 nan 0.3000 0.0458
## 3 1.0433 nan 0.3000 0.0182
## 4 1.0215 nan 0.3000 0.0045
## 5 0.9944 nan 0.3000 0.0011
## 6 0.9618 nan 0.3000 0.0078
## 7 0.9367 nan 0.3000 0.0067
## 8 0.9253 nan 0.3000 -0.0009
## 9 0.9048 nan 0.3000 0.0031
## 10 0.8945 nan 0.3000 -0.0008
## 20 0.8068 nan 0.3000 -0.0034
## 40 0.7332 nan 0.3000 -0.0062
## 60 0.6815 nan 0.3000 -0.0053
## 80 0.6216 nan 0.3000 -0.0027
## 100 1.2472 nan 0.3000 -0.0014
## 120 1.2267 nan 0.3000 -0.0007
## 140 1.1739 nan 0.3000 -0.0022
## 160 1.1577 nan 0.3000 -0.0000
## 180 1.1399 nan 0.3000 -0.0014
## 200 1.1259 nan 0.3000 -0.0028
## 220 1.1023 nan 0.3000 -0.0030
## 240 1.0910 nan 0.3000 -0.0019
## 260 1.0806 nan 0.3000 -0.0022
## 280 1.0577 nan 0.3000 -0.0009
## 300 1.0467 nan 0.3000 -0.0005
## 320 1.0389 nan 0.3000 -0.0019
## 340 0.3961 nan 0.3000 -0.0011
## 360 0.3655 nan 0.3000 -0.0008
## 380 0.3451 nan 0.3000 -0.0036
## 400 0.3217 nan 0.3000 -0.0011
## 420 0.2989 nan 0.3000 -0.0014
## 440 0.2816 nan 0.3000 -0.0009
## 460 0.2669 nan 0.3000 -0.0033
## 480 inf nan 0.3000 -inf
## 500 inf nan 0.3000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1722 nan 0.3000 0.0463
## 2 1.0913 nan 0.3000 0.0395
## 3 1.0300 nan 0.3000 0.0166
## 4 0.9903 nan 0.3000 0.0105
## 5 0.9700 nan 0.3000 0.0035
## 6 0.9474 nan 0.3000 0.0059
## 7 0.9299 nan 0.3000 0.0045
## 8 0.9115 nan 0.3000 0.0016
## 9 0.9029 nan 0.3000 -0.0039
## 10 0.8920 nan 0.3000 -0.0019
## 20 0.7993 nan 0.3000 -0.0031
## 40 0.7068 nan 0.3000 -0.0050
## 60 0.6428 nan 0.3000 -0.0013
## 80 0.5892 nan 0.3000 -0.0028
## 100 0.5511 nan 0.3000 -0.0054
## 120 0.4967 nan 0.3000 -0.0011
## 140 0.4594 nan 0.3000 -0.0030
## 160 0.4317 nan 0.3000 -0.0018
## 180 0.4096 nan 0.3000 -0.0031
## 200 0.3847 nan 0.3000 0.0001
## 220 0.3649 nan 0.3000 -0.0053
## 240 0.3452 nan 0.3000 -0.0013
## 260 0.3265 nan 0.3000 -0.0024
## 280 0.3099 nan 0.3000 -0.0021
## 300 0.2930 nan 0.3000 -0.0028
## 320 0.2742 nan 0.3000 -0.0018
## 340 0.2560 nan 0.3000 -0.0014
## 360 0.2408 nan 0.3000 -0.0019
## 380 0.2307 nan 0.3000 -0.0011
## 400 0.2129 nan 0.3000 -0.0017
## 420 0.2011 nan 0.3000 -0.0013
## 440 0.1918 nan 0.3000 -0.0004
## 460 0.1821 nan 0.3000 -0.0022
## 480 0.1732 nan 0.3000 -0.0001
## 500 0.1641 nan 0.3000 -0.0013
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1744 nan 0.3000 0.0534
## 2 1.0866 nan 0.3000 0.0348
## 3 1.0383 nan 0.3000 0.0201
## 4 0.9995 nan 0.3000 0.0176
## 5 0.9666 nan 0.3000 0.0138
## 6 0.9403 nan 0.3000 0.0068
## 7 0.9168 nan 0.3000 0.0066
## 8 0.9062 nan 0.3000 -0.0022
## 9 0.9004 nan 0.3000 -0.0071
## 10 0.8935 nan 0.3000 -0.0095
## 20 0.7978 nan 0.3000 -0.0013
## 40 0.6967 nan 0.3000 -0.0083
## 60 0.6405 nan 0.3000 -0.0028
## 80 0.5877 nan 0.3000 -0.0043
## 100 0.5427 nan 0.3000 -0.0046
## 120 0.5076 nan 0.3000 -0.0052
## 140 0.4735 nan 0.3000 -0.0038
## 160 0.4418 nan 0.3000 -0.0035
## 180 0.4126 nan 0.3000 -0.0034
## 200 0.3884 nan 0.3000 -0.0017
## 220 0.3676 nan 0.3000 -0.0032
## 240 0.3465 nan 0.3000 -0.0037
## 260 0.3258 nan 0.3000 -0.0017
## 280 0.3070 nan 0.3000 -0.0029
## 300 0.2895 nan 0.3000 -0.0015
## 320 0.2758 nan 0.3000 -0.0016
## 340 0.2612 nan 0.3000 -0.0020
## 360 0.2455 nan 0.3000 -0.0013
## 380 0.2294 nan 0.3000 -0.0016
## 400 0.2147 nan 0.3000 -0.0008
## 420 0.2034 nan 0.3000 -0.0016
## 440 0.1916 nan 0.3000 -0.0024
## 460 0.1814 nan 0.3000 -0.0012
## 480 0.1718 nan 0.3000 -0.0027
## 500 0.1649 nan 0.3000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1407 nan 0.3000 0.0647
## 2 1.0593 nan 0.3000 0.0354
## 3 0.9999 nan 0.3000 0.0214
## 4 0.9622 nan 0.3000 0.0104
## 5 0.9295 nan 0.3000 0.0107
## 6 0.9059 nan 0.3000 0.0045
## 7 0.8886 nan 0.3000 0.0008
## 8 0.8695 nan 0.3000 -0.0029
## 9 0.8503 nan 0.3000 0.0021
## 10 0.8417 nan 0.3000 -0.0020
## 20 0.7401 nan 0.3000 -0.0077
## 40 0.6307 nan 0.3000 -0.0067
## 60 0.5491 nan 0.3000 -0.0073
## 80 0.4746 nan 0.3000 -0.0027
## 100 0.4056 nan 0.3000 -0.0030
## 120 0.3557 nan 0.3000 -0.0018
## 140 0.3111 nan 0.3000 -0.0034
## 160 0.2878 nan 0.3000 -0.0047
## 180 0.2520 nan 0.3000 -0.0007
## 200 0.2240 nan 0.3000 -0.0017
## 220 0.2031 nan 0.3000 -0.0008
## 240 0.1792 nan 0.3000 0.0004
## 260 0.1628 nan 0.3000 -0.0004
## 280 0.1490 nan 0.3000 -0.0015
## 300 0.1381 nan 0.3000 -0.0006
## 320 0.1242 nan 0.3000 -0.0008
## 340 0.1162 nan 0.3000 -0.0011
## 360 0.1056 nan 0.3000 -0.0002
## 380 0.0958 nan 0.3000 -0.0005
## 400 0.0867 nan 0.3000 -0.0000
## 420 0.0813 nan 0.3000 -0.0015
## 440 0.0742 nan 0.3000 -0.0004
## 460 0.0694 nan 0.3000 -0.0005
## 480 0.0641 nan 0.3000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1586 nan 0.3000 0.0570
## 2 1.0711 nan 0.3000 0.0398
## 3 1.0177 nan 0.3000 0.0119
## 4 0.9729 nan 0.3000 0.0140
## 5 0.9482 nan 0.3000 -0.0012
## 6 0.9195 nan 0.3000 0.0104
## 7 0.8944 nan 0.3000 0.0042
## 8 0.8837 nan 0.3000 -0.0066
## 9 0.8675 nan 0.3000 -0.0039
## 10 0.8527 nan 0.3000 -0.0010
## 20 0.7414 nan 0.3000 -0.0024
## 40 0.6376 nan 0.3000 -0.0057
## 60 0.5519 nan 0.3000 -0.0065
## 80 0.4744 nan 0.3000 -0.0035
## 100 0.4289 nan 0.3000 -0.0044
## 120 0.3733 nan 0.3000 -0.0037
## 140 0.3170 nan 0.3000 -0.0013
## 160 0.2833 nan 0.3000 -0.0034
## 180 0.2553 nan 0.3000 -0.0020
## 200 0.2261 nan 0.3000 -0.0025
## 220 0.2028 nan 0.3000 -0.0014
## 240 0.1869 nan 0.3000 -0.0021
## 260 0.1667 nan 0.3000 -0.0004
## 280 0.1503 nan 0.3000 -0.0004
## 300 0.1380 nan 0.3000 -0.0012
## 320 0.1250 nan 0.3000 -0.0010
## 340 0.1133 nan 0.3000 -0.0006
## 360 0.1027 nan 0.3000 -0.0006
## 380 0.0934 nan 0.3000 -0.0009
## 400 0.0837 nan 0.3000 -0.0005
## 420 0.0780 nan 0.3000 -0.0004
## 440 0.0721 nan 0.3000 -0.0005
## 460 0.0658 nan 0.3000 -0.0004
## 480 0.0596 nan 0.3000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1639 nan 0.3000 0.0568
## 2 1.0672 nan 0.3000 0.0384
## 3 1.0076 nan 0.3000 0.0237
## 4 0.9690 nan 0.3000 0.0049
## 5 0.9305 nan 0.3000 0.0169
## 6 0.9111 nan 0.3000 0.0049
## 7 0.8838 nan 0.3000 0.0100
## 8 0.8628 nan 0.3000 -0.0023
## 9 0.8540 nan 0.3000 -0.0084
## 10 0.8483 nan 0.3000 -0.0065
## 20 0.7672 nan 0.3000 -0.0073
## 40 0.6502 nan 0.3000 -0.0025
## 60 0.5610 nan 0.3000 -0.0020
## 80 0.4851 nan 0.3000 -0.0044
## 100 0.4201 nan 0.3000 -0.0009
## 120 0.3746 nan 0.3000 -0.0029
## 140 0.3403 nan 0.3000 -0.0046
## 160 0.3032 nan 0.3000 -0.0016
## 180 0.2588 nan 0.3000 -0.0002
## 200 0.2352 nan 0.3000 -0.0013
## 220 0.2128 nan 0.3000 -0.0015
## 240 0.1925 nan 0.3000 -0.0011
## 260 0.1747 nan 0.3000 -0.0003
## 280 0.1604 nan 0.3000 -0.0029
## 300 0.1456 nan 0.3000 -0.0011
## 320 0.1319 nan 0.3000 -0.0018
## 340 0.1212 nan 0.3000 -0.0005
## 360 0.1097 nan 0.3000 -0.0019
## 380 0.1013 nan 0.3000 -0.0003
## 400 0.0924 nan 0.3000 -0.0012
## 420 0.0858 nan 0.3000 -0.0016
## 440 0.0788 nan 0.3000 -0.0001
## 460 0.0710 nan 0.3000 -0.0003
## 480 0.0641 nan 0.3000 -0.0002
## 500 0.0592 nan 0.3000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1554 nan 0.5000 0.0580
## 2 1.1020 nan 0.5000 0.0247
## 3 1.0488 nan 0.5000 0.0145
## 4 1.0175 nan 0.5000 0.0102
## 5 0.9929 nan 0.5000 0.0103
## 6 0.9674 nan 0.5000 0.0057
## 7 0.9485 nan 0.5000 0.0003
## 8 0.9368 nan 0.5000 0.0004
## 9 0.9253 nan 0.5000 0.0007
## 10 0.9106 nan 0.5000 0.0006
## 20 0.8604 nan 0.5000 -0.0021
## 40 0.7985 nan 0.5000 -0.0080
## 60 0.7688 nan 0.5000 -0.0082
## 80 0.7321 nan 0.5000 -0.0013
## 100 0.7015 nan 0.5000 -0.0024
## 120 0.6906 nan 0.5000 -0.0024
## 140 0.6768 nan 0.5000 -0.0041
## 160 0.6555 nan 0.5000 0.0008
## 180 0.6451 nan 0.5000 -0.0059
## 200 0.6298 nan 0.5000 -0.0079
## 220 0.6141 nan 0.5000 -0.0023
## 240 0.6027 nan 0.5000 -0.0028
## 260 0.5924 nan 0.5000 -0.0051
## 280 0.5772 nan 0.5000 -0.0094
## 300 0.5655 nan 0.5000 -0.0016
## 320 0.5478 nan 0.5000 -0.0069
## 340 0.5398 nan 0.5000 0.0055
## 360 0.5282 nan 0.5000 -0.0023
## 380 0.5114 nan 0.5000 -0.0025
## 400 0.5106 nan 0.5000 -0.0035
## 420 0.4956 nan 0.5000 -0.0039
## 440 0.4939 nan 0.5000 -0.0072
## 460 0.4892 nan 0.5000 -0.0057
## 480 0.4833 nan 0.5000 -0.0046
## 500 0.4637 nan 0.5000 -0.0042
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1678 nan 0.5000 0.0642
## 2 1.1045 nan 0.5000 0.0157
## 3 1.0502 nan 0.5000 0.0181
## 4 1.0069 nan 0.5000 0.0179
## 5 0.9842 nan 0.5000 0.0050
## 6 0.9670 nan 0.5000 0.0004
## 7 0.9540 nan 0.5000 -0.0026
## 8 0.9377 nan 0.5000 0.0015
## 9 0.9242 nan 0.5000 0.0013
## 10 0.9136 nan 0.5000 0.0002
## 20 0.8673 nan 0.5000 -0.0096
## 40 0.8017 nan 0.5000 -0.0066
## 60 0.7644 nan 0.5000 -0.0015
## 80 0.7362 nan 0.5000 -0.0050
## 100 0.7210 nan 0.5000 -0.0107
## 120 0.6855 nan 0.5000 -0.0020
## 140 0.6775 nan 0.5000 -0.0076
## 160 0.6560 nan 0.5000 -0.0059
## 180 0.6379 nan 0.5000 -0.0086
## 200 0.6321 nan 0.5000 -0.0027
## 220 0.6202 nan 0.5000 -0.0042
## 240 0.6030 nan 0.5000 -0.0060
## 260 0.5949 nan 0.5000 -0.0161
## 280 0.5773 nan 0.5000 -0.0017
## 300 0.5653 nan 0.5000 -0.0034
## 320 1.1336 nan 0.5000 -0.0005
## 340 83384773687.4878 nan 0.5000 -0.0000
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 2057629316492615546448660.0000 nan 0.5000 -23463.1675
## 460 2057629316492615546448660.0000 nan 0.5000 0.0000
## 480 2057629316492615546448660.0000 nan 0.5000 0.0003
## 500 2057629316492615546448660.0000 nan 0.5000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1449 nan 0.5000 0.0476
## 2 1.0776 nan 0.5000 0.0187
## 3 1.0283 nan 0.5000 0.0212
## 4 0.9924 nan 0.5000 0.0155
## 5 0.9774 nan 0.5000 0.0017
## 6 0.9578 nan 0.5000 0.0038
## 7 0.9478 nan 0.5000 0.0007
## 8 0.9431 nan 0.5000 -0.0056
## 9 0.9318 nan 0.5000 -0.0062
## 10 0.9118 nan 0.5000 0.0038
## 20 0.8583 nan 0.5000 -0.0050
## 40 0.8031 nan 0.5000 -0.0040
## 60 0.7519 nan 0.5000 -0.0017
## 80 0.7309 nan 0.5000 -0.0054
## 100 0.6950 nan 0.5000 -0.0015
## 120 0.6877 nan 0.5000 -0.0002
## 140 0.6645 nan 0.5000 -0.0054
## 160 0.6416 nan 0.5000 0.0016
## 180 0.6281 nan 0.5000 -0.0034
## 200 0.6267 nan 0.5000 -0.0052
## 220 0.6036 nan 0.5000 -0.0049
## 240 0.5926 nan 0.5000 -0.0101
## 260 0.5794 nan 0.5000 0.0007
## 280 0.5737 nan 0.5000 -0.0069
## 300 0.5634 nan 0.5000 -0.0040
## 320 0.5546 nan 0.5000 -0.0062
## 340 0.5464 nan 0.5000 -0.0079
## 360 0.5329 nan 0.5000 -0.0038
## 380 0.5332 nan 0.5000 -0.0063
## 400 0.5291 nan 0.5000 -0.0051
## 420 0.5176 nan 0.5000 -0.0019
## 440 0.5130 nan 0.5000 -0.0075
## 460 0.5056 nan 0.5000 -0.0040
## 480 0.4972 nan 0.5000 -0.0102
## 500 0.4884 nan 0.5000 -0.0059
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1103 nan 0.5000 0.0505
## 2 1.0278 nan 0.5000 0.0363
## 3 0.9739 nan 0.5000 0.0106
## 4 0.9277 nan 0.5000 0.0170
## 5 0.9060 nan 0.5000 0.0019
## 6 0.8945 nan 0.5000 -0.0095
## 7 0.8749 nan 0.5000 -0.0017
## 8 0.8540 nan 0.5000 -0.0026
## 9 0.8532 nan 0.5000 -0.0179
## 10 0.8402 nan 0.5000 -0.0014
## 20 0.7885 nan 0.5000 -0.0073
## 40 0.7477 nan 0.5000 -0.0071
## 60 1.3496 nan 0.5000 -0.0014
## 80 1.2614 nan 0.5000 -0.0082
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1173 nan 0.5000 0.0766
## 2 1.0388 nan 0.5000 0.0337
## 3 0.9878 nan 0.5000 0.0172
## 4 0.9563 nan 0.5000 0.0084
## 5 0.9329 nan 0.5000 0.0021
## 6 0.9113 nan 0.5000 -0.0079
## 7 0.8947 nan 0.5000 -0.0080
## 8 0.8867 nan 0.5000 -0.0103
## 9 0.8595 nan 0.5000 -0.0022
## 10 0.8537 nan 0.5000 -0.0069
## 20 0.7908 nan 0.5000 -0.0026
## 40 0.6608 nan 0.5000 -0.0064
## 60 0.6018 nan 0.5000 -0.0038
## 80 0.5409 nan 0.5000 -0.0091
## 100 0.4762 nan 0.5000 -0.0068
## 120 0.4322 nan 0.5000 -0.0048
## 140 0.3888 nan 0.5000 -0.0066
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
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## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1204 nan 0.5000 0.0828
## 2 1.0218 nan 0.5000 0.0424
## 3 0.9780 nan 0.5000 0.0083
## 4 0.9385 nan 0.5000 0.0057
## 5 0.9089 nan 0.5000 0.0035
## 6 0.8935 nan 0.5000 -0.0060
## 7 0.8701 nan 0.5000 0.0064
## 8 0.8595 nan 0.5000 -0.0015
## 9 0.8448 nan 0.5000 0.0013
## 10 0.8380 nan 0.5000 -0.0020
## 20 0.7715 nan 0.5000 -0.0026
## 40 0.7002 nan 0.5000 -0.0060
## 60 0.6303 nan 0.5000 -0.0122
## 80 0.5535 nan 0.5000 -0.0044
## 100 0.4908 nan 0.5000 -0.0029
## 120 0.4407 nan 0.5000 -0.0060
## 140 0.3923 nan 0.5000 -0.0051
## 160 0.3487 nan 0.5000 -0.0032
## 180 160.2560 nan 0.5000 -0.0057
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0908 nan 0.5000 0.1030
## 2 0.9874 nan 0.5000 0.0411
## 3 0.9432 nan 0.5000 0.0058
## 4 0.9146 nan 0.5000 -0.0144
## 5 0.8902 nan 0.5000 0.0005
## 6 0.8578 nan 0.5000 0.0030
## 7 0.8315 nan 0.5000 -0.0047
## 8 0.8095 nan 0.5000 -0.0018
## 9 0.7978 nan 0.5000 -0.0058
## 10 0.7742 nan 0.5000 0.0057
## 20 0.7157 nan 0.5000 -0.0505
## 40 0.6226 nan 0.5000 -0.0105
## 60 5.5910 nan 0.5000 -0.0042
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
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## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
## 500 inf nan 0.5000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1019 nan 0.5000 0.0770
## 2 0.9988 nan 0.5000 0.0510
## 3 0.9631 nan 0.5000 0.0031
## 4 0.9285 nan 0.5000 0.0013
## 5 0.9064 nan 0.5000 0.0011
## 6 0.8773 nan 0.5000 0.0008
## 7 0.8674 nan 0.5000 -0.0069
## 8 0.8479 nan 0.5000 -0.0016
## 9 0.8194 nan 0.5000 0.0058
## 10 0.8109 nan 0.5000 -0.0134
## 20 0.7064 nan 0.5000 -0.0084
## 40 0.5321 nan 0.5000 -0.0044
## 60 0.4483 nan 0.5000 -0.0112
## 80 0.3520 nan 0.5000 -0.0044
## 100 0.2885 nan 0.5000 -0.0026
## 120 0.2352 nan 0.5000 -0.0048
## 140 0.1842 nan 0.5000 -0.0038
## 160 0.1555 nan 0.5000 -0.0012
## 180 0.1336 nan 0.5000 -0.0005
## 200 0.1140 nan 0.5000 -0.0019
## 220 0.0977 nan 0.5000 -0.0008
## 240 0.0840 nan 0.5000 -0.0007
## 260 0.0744 nan 0.5000 -0.0018
## 280 0.0658 nan 0.5000 -0.0010
## 300 0.0582 nan 0.5000 -0.0007
## 320 0.0505 nan 0.5000 -0.0009
## 340 0.0447 nan 0.5000 -0.0003
## 360 0.0389 nan 0.5000 -0.0004
## 380 0.0345 nan 0.5000 -0.0006
## 400 0.0312 nan 0.5000 -0.0004
## 420 0.0292 nan 0.5000 -0.0008
## 440 0.0248 nan 0.5000 -0.0003
## 460 0.0219 nan 0.5000 -0.0004
## 480 0.0198 nan 0.5000 -0.0003
## 500 0.0171 nan 0.5000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0947 nan 0.5000 0.1042
## 2 0.9885 nan 0.5000 0.0426
## 3 0.9478 nan 0.5000 0.0119
## 4 0.9151 nan 0.5000 0.0026
## 5 0.8799 nan 0.5000 0.0086
## 6 0.8513 nan 0.5000 0.0017
## 7 0.8383 nan 0.5000 -0.0101
## 8 0.8258 nan 0.5000 -0.0112
## 9 0.8123 nan 0.5000 -0.0041
## 10 0.8007 nan 0.5000 -0.0079
## 20 0.7015 nan 0.5000 -0.0108
## 40 0.5807 nan 0.5000 -0.0038
## 60 0.4848 nan 0.5000 -0.0078
## 80 0.4019 nan 0.5000 -0.0099
## 100 0.3465 nan 0.5000 -0.0039
## 120 0.2725 nan 0.5000 -0.0040
## 140 0.2395 nan 0.5000 -0.0006
## 160 0.1805 nan 0.5000 -0.0074
## 180 0.1454 nan 0.5000 -0.0028
## 200 0.1206 nan 0.5000 -0.0020
## 220 0.1000 nan 0.5000 -0.0005
## 240 0.0876 nan 0.5000 -0.0007
## 260 0.0746 nan 0.5000 -0.0013
## 280 0.0643 nan 0.5000 -0.0011
## 300 0.0556 nan 0.5000 -0.0022
## 320 0.0477 nan 0.5000 -0.0002
## 340 0.0406 nan 0.5000 -0.0003
## 360 0.0359 nan 0.5000 -0.0004
## 380 0.0323 nan 0.5000 -0.0005
## 400 0.0282 nan 0.5000 -0.0002
## 420 0.0252 nan 0.5000 -0.0002
## 440 0.0229 nan 0.5000 -0.0006
## 460 0.0204 nan 0.5000 -0.0002
## 480 0.0185 nan 0.5000 -0.0005
## 500 0.0161 nan 0.5000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1228 nan 1.0000 0.0711
## 2 1.0716 nan 1.0000 -0.0005
## 3 1.0375 nan 1.0000 0.0050
## 4 1.0041 nan 1.0000 0.0082
## 5 1.0095 nan 1.0000 -0.0276
## 6 1.0020 nan 1.0000 -0.0176
## 7 0.9693 nan 1.0000 0.0169
## 8 0.9604 nan 1.0000 -0.0122
## 9 1.0494 nan 1.0000 -0.1044
## 10 1.0319 nan 1.0000 0.0014
## 20 8672.6516 nan 1.0000 -0.1412
## 40 82536747.5347 nan 1.0000 -0.0054
## 60 82536747.5419 nan 1.0000 -0.0516
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1556 nan 1.0000 0.0501
## 2 1.0650 nan 1.0000 0.0300
## 3 1.0012 nan 1.0000 0.0314
## 4 0.9441 nan 1.0000 0.0280
## 5 0.9299 nan 1.0000 -0.0067
## 6 0.9255 nan 1.0000 -0.0077
## 7 0.9350 nan 1.0000 -0.0183
## 8 0.9200 nan 1.0000 0.0038
## 9 0.9295 nan 1.0000 -0.0240
## 10 0.9198 nan 1.0000 -0.0039
## 20 0.8886 nan 1.0000 -0.0132
## 40 0.8197 nan 1.0000 0.0008
## 60 0.7890 nan 1.0000 -0.0013
## 80 0.7631 nan 1.0000 -0.0249
## 100 0.7520 nan 1.0000 -0.0115
## 120 0.6915 nan 1.0000 -0.0019
## 140 3.1163 nan 1.0000 -0.0082
## 160 3.1058 nan 1.0000 -0.0019
## 180 3.1117 nan 1.0000 -0.0004
## 200 3.0599 nan 1.0000 -0.0033
## 220 3.0518 nan 1.0000 -0.0047
## 240 3.0419 nan 1.0000 0.0022
## 260 3.0454 nan 1.0000 -0.0209
## 280 3.0229 nan 1.0000 0.0018
## 300 3.0225 nan 1.0000 -0.0298
## 320 3.0873 nan 1.0000 0.0020
## 340 2.7514 nan 1.0000 0.0024
## 360 2.7578 nan 1.0000 -0.0038
## 380 2.7305 nan 1.0000 -0.0004
## 400 2.7319 nan 1.0000 -0.0143
## 420 2.8077 nan 1.0000 -0.0010
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1234 nan 1.0000 0.0601
## 2 1.0455 nan 1.0000 0.0219
## 3 0.9994 nan 1.0000 0.0131
## 4 0.9811 nan 1.0000 -0.0022
## 5 0.9495 nan 1.0000 0.0138
## 6 0.9585 nan 1.0000 -0.0254
## 7 0.9680 nan 1.0000 -0.0227
## 8 0.9373 nan 1.0000 -0.0052
## 9 0.9265 nan 1.0000 -0.0058
## 10 0.9225 nan 1.0000 -0.0137
## 20 inf nan 1.0000 nan
## 40 9667.4060 nan 1.0000 -0.0210
## 60 9667.3836 nan 1.0000 -0.0151
## 80 9667.3099 nan 1.0000 -0.0118
## 100 9667.2865 nan 1.0000 -0.0100
## 120 9667.2583 nan 1.0000 0.0001
## 140 9667.2500 nan 1.0000 -0.0324
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0741 nan 1.0000 0.0946
## 2 0.9690 nan 1.0000 0.0422
## 3 0.9448 nan 1.0000 -0.0174
## 4 0.9656 nan 1.0000 -0.0712
## 5 0.9663 nan 1.0000 -0.0333
## 6 0.9835 nan 1.0000 -0.0469
## 7 1.0388 nan 1.0000 -0.1105
## 8 0.9186 nan 1.0000 -0.0090
## 9 0.8826 nan 1.0000 0.0018
## 10 0.9116 nan 1.0000 -0.0464
## 20 0.9300 nan 1.0000 -0.0135
## 40 inf nan 1.0000 -inf
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0548 nan 1.0000 0.0971
## 2 0.9746 nan 1.0000 0.0167
## 3 0.9573 nan 1.0000 -0.0068
## 4 0.9443 nan 1.0000 -0.0186
## 5 0.9067 nan 1.0000 0.0068
## 6 0.9038 nan 1.0000 -0.0237
## 7 0.9179 nan 1.0000 -0.0479
## 8 0.9178 nan 1.0000 -0.0254
## 9 0.9014 nan 1.0000 -0.0108
## 10 0.9081 nan 1.0000 -0.0531
## 20 0.8904 nan 1.0000 -0.0370
## 40 6.3216 nan 1.0000 -0.0523
## 60 8.1270 nan 1.0000 -0.0214
## 80 9.0762 nan 1.0000 0.0058
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0784 nan 1.0000 0.0552
## 2 1.0107 nan 1.0000 0.0166
## 3 0.9907 nan 1.0000 -0.0218
## 4 0.9679 nan 1.0000 -0.0288
## 5 1.1076 nan 1.0000 -0.1699
## 6 3.6409 nan 1.0000 -2.6389
## 7 3.6380 nan 1.0000 -0.0245
## 8 3.5963 nan 1.0000 -0.0037
## 9 3.6063 nan 1.0000 -0.0221
## 10 3.6760 nan 1.0000 -0.1221
## 20 45.6147 nan 1.0000 -2.7568
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0513 nan 1.0000 0.0539
## 2 0.9869 nan 1.0000 0.0016
## 3 0.9226 nan 1.0000 0.0067
## 4 0.9090 nan 1.0000 -0.0157
## 5 0.8854 nan 1.0000 -0.0190
## 6 0.8667 nan 1.0000 -0.0165
## 7 0.8604 nan 1.0000 -0.0375
## 8 0.8270 nan 1.0000 -0.0041
## 9 0.8160 nan 1.0000 -0.0158
## 10 0.7888 nan 1.0000 -0.0067
## 20 inf nan 1.0000 inf
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0189 nan 1.0000 0.1050
## 2 0.9545 nan 1.0000 0.0047
## 3 0.9235 nan 1.0000 -0.0074
## 4 0.9104 nan 1.0000 -0.0361
## 5 0.8653 nan 1.0000 -0.0034
## 6 0.8511 nan 1.0000 -0.0372
## 7 0.8509 nan 1.0000 -0.0538
## 8 0.8958 nan 1.0000 -0.0619
## 9 0.8925 nan 1.0000 -0.0399
## 10 0.9032 nan 1.0000 -0.0682
## 20 inf nan 1.0000 -inf
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0510 nan 1.0000 0.0792
## 2 0.9851 nan 1.0000 0.0125
## 3 0.9474 nan 1.0000 -0.0020
## 4 0.9358 nan 1.0000 -0.0178
## 5 0.9549 nan 1.0000 -0.0644
## 6 0.9648 nan 1.0000 -0.0540
## 7 0.9399 nan 1.0000 -0.0138
## 8 1.0775 nan 1.0000 -0.1676
## 9 1.0306 nan 1.0000 -0.0091
## 10 1.0144 nan 1.0000 -0.0452
## 20 45.5424 nan 1.0000 -0.0438
## 40 2607004125874.7700 nan 1.0000 0.2182
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2900 nan 0.0010 0.0002
## 10 1.2896 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2790 nan 0.0010 0.0002
## 60 1.2721 nan 0.0010 0.0002
## 80 1.2654 nan 0.0010 0.0002
## 100 1.2592 nan 0.0010 0.0001
## 120 1.2531 nan 0.0010 0.0001
## 140 1.2469 nan 0.0010 0.0001
## 160 1.2411 nan 0.0010 0.0001
## 180 1.2354 nan 0.0010 0.0001
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## 240 1.2195 nan 0.0010 0.0001
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## 280 1.2095 nan 0.0010 0.0001
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## 320 1.2002 nan 0.0010 0.0001
## 340 1.1957 nan 0.0010 0.0001
## 360 1.1914 nan 0.0010 0.0001
## 380 1.1872 nan 0.0010 0.0001
## 400 1.1830 nan 0.0010 0.0001
## 420 1.1791 nan 0.0010 0.0001
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## 460 1.1711 nan 0.0010 0.0001
## 480 1.1673 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2859 nan 0.0010 0.0002
## 40 1.2788 nan 0.0010 0.0002
## 60 1.2718 nan 0.0010 0.0002
## 80 1.2652 nan 0.0010 0.0001
## 100 1.2587 nan 0.0010 0.0001
## 120 1.2525 nan 0.0010 0.0001
## 140 1.2462 nan 0.0010 0.0001
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## 220 1.2240 nan 0.0010 0.0001
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## 280 1.2091 nan 0.0010 0.0001
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## 320 1.1999 nan 0.0010 0.0001
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## 380 1.1870 nan 0.0010 0.0001
## 400 1.1829 nan 0.0010 0.0001
## 420 1.1787 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2789 nan 0.0010 0.0002
## 60 1.2720 nan 0.0010 0.0001
## 80 1.2651 nan 0.0010 0.0001
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## 140 1.2463 nan 0.0010 0.0001
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## 280 1.2095 nan 0.0010 0.0001
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## 380 1.1870 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2895 nan 0.0010 0.0002
## 9 1.2890 nan 0.0010 0.0002
## 10 1.2886 nan 0.0010 0.0002
## 20 1.2837 nan 0.0010 0.0002
## 40 1.2747 nan 0.0010 0.0002
## 60 1.2656 nan 0.0010 0.0002
## 80 1.2570 nan 0.0010 0.0002
## 100 1.2486 nan 0.0010 0.0001
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## 140 1.2324 nan 0.0010 0.0002
## 160 1.2247 nan 0.0010 0.0002
## 180 1.2173 nan 0.0010 0.0001
## 200 1.2100 nan 0.0010 0.0002
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## 240 1.1962 nan 0.0010 0.0001
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## 380 1.1539 nan 0.0010 0.0001
## 400 1.1484 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2913 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2899 nan 0.0010 0.0002
## 8 1.2894 nan 0.0010 0.0002
## 9 1.2889 nan 0.0010 0.0002
## 10 1.2884 nan 0.0010 0.0002
## 20 1.2838 nan 0.0010 0.0002
## 40 1.2744 nan 0.0010 0.0002
## 60 1.2656 nan 0.0010 0.0002
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## 140 1.2327 nan 0.0010 0.0002
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## 180 1.2179 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2916 nan 0.0010 0.0002
## 5 1.2911 nan 0.0010 0.0002
## 6 1.2906 nan 0.0010 0.0002
## 7 1.2902 nan 0.0010 0.0002
## 8 1.2897 nan 0.0010 0.0002
## 9 1.2893 nan 0.0010 0.0002
## 10 1.2888 nan 0.0010 0.0002
## 20 1.2841 nan 0.0010 0.0002
## 40 1.2748 nan 0.0010 0.0002
## 60 1.2657 nan 0.0010 0.0002
## 80 1.2571 nan 0.0010 0.0002
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## 140 1.2328 nan 0.0010 0.0001
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## 220 1.2034 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2907 nan 0.0010 0.0002
## 6 1.2902 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0003
## 8 1.2891 nan 0.0010 0.0002
## 9 1.2885 nan 0.0010 0.0002
## 10 1.2880 nan 0.0010 0.0003
## 20 1.2826 nan 0.0010 0.0002
## 40 1.2717 nan 0.0010 0.0002
## 60 1.2611 nan 0.0010 0.0003
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## 180 1.2055 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2917 nan 0.0010 0.0003
## 4 1.2911 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0003
## 6 1.2900 nan 0.0010 0.0003
## 7 1.2895 nan 0.0010 0.0002
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## 10 1.2878 nan 0.0010 0.0002
## 20 1.2823 nan 0.0010 0.0002
## 40 1.2714 nan 0.0010 0.0003
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## 180 1.2058 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2916 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0002
## 5 1.2905 nan 0.0010 0.0003
## 6 1.2899 nan 0.0010 0.0003
## 7 1.2894 nan 0.0010 0.0003
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## 9 1.2884 nan 0.0010 0.0002
## 10 1.2878 nan 0.0010 0.0002
## 20 1.2824 nan 0.0010 0.0003
## 40 1.2715 nan 0.0010 0.0002
## 60 1.2609 nan 0.0010 0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2598 nan 0.1000 0.0169
## 2 1.2302 nan 0.1000 0.0151
## 3 1.2052 nan 0.1000 0.0125
## 4 1.1820 nan 0.1000 0.0096
## 5 1.1634 nan 0.1000 0.0074
## 6 1.1457 nan 0.1000 0.0069
## 7 1.1317 nan 0.1000 0.0063
## 8 1.1194 nan 0.1000 0.0040
## 9 1.1036 nan 0.1000 0.0068
## 10 1.0913 nan 0.1000 0.0053
## 20 1.0006 nan 0.1000 0.0013
## 40 0.9128 nan 0.1000 0.0012
## 60 0.8684 nan 0.1000 -0.0003
## 80 0.8436 nan 0.1000 -0.0002
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## 140 0.7993 nan 0.1000 -0.0014
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## 180 0.7738 nan 0.1000 -0.0016
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## 340 0.7232 nan 0.1000 -0.0010
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## 480 0.6869 nan 0.1000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2568 nan 0.1000 0.0178
## 2 1.2250 nan 0.1000 0.0136
## 3 1.1989 nan 0.1000 0.0106
## 4 1.1789 nan 0.1000 0.0089
## 5 1.1555 nan 0.1000 0.0069
## 6 1.1399 nan 0.1000 0.0063
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## 20 0.9944 nan 0.1000 0.0007
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## 60 0.8731 nan 0.1000 -0.0005
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## 180 0.7793 nan 0.1000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2610 nan 0.1000 0.0174
## 2 1.2328 nan 0.1000 0.0130
## 3 1.2049 nan 0.1000 0.0099
## 4 1.1868 nan 0.1000 0.0083
## 5 1.1625 nan 0.1000 0.0093
## 6 1.1449 nan 0.1000 0.0063
## 7 1.1283 nan 0.1000 0.0061
## 8 1.1159 nan 0.1000 0.0044
## 9 1.1020 nan 0.1000 0.0063
## 10 1.0865 nan 0.1000 0.0059
## 20 0.9947 nan 0.1000 0.0021
## 40 0.9105 nan 0.1000 0.0003
## 60 0.8706 nan 0.1000 0.0004
## 80 0.8457 nan 0.1000 0.0002
## 100 0.8271 nan 0.1000 -0.0006
## 120 0.8121 nan 0.1000 -0.0011
## 140 0.8011 nan 0.1000 -0.0007
## 160 0.7888 nan 0.1000 -0.0013
## 180 0.7779 nan 0.1000 -0.0015
## 200 0.7693 nan 0.1000 -0.0006
## 220 0.7632 nan 0.1000 -0.0020
## 240 0.7573 nan 0.1000 -0.0007
## 260 0.7504 nan 0.1000 -0.0009
## 280 0.7442 nan 0.1000 -0.0007
## 300 0.7394 nan 0.1000 -0.0004
## 320 0.7338 nan 0.1000 -0.0015
## 340 0.7284 nan 0.1000 -0.0014
## 360 0.7230 nan 0.1000 -0.0011
## 380 0.7169 nan 0.1000 -0.0007
## 400 0.7131 nan 0.1000 -0.0004
## 420 0.7067 nan 0.1000 -0.0012
## 440 0.7017 nan 0.1000 -0.0015
## 460 0.6970 nan 0.1000 -0.0013
## 480 0.6937 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2472 nan 0.1000 0.0213
## 2 1.2083 nan 0.1000 0.0188
## 3 1.1747 nan 0.1000 0.0141
## 4 1.1437 nan 0.1000 0.0111
## 5 1.1191 nan 0.1000 0.0100
## 6 1.0939 nan 0.1000 0.0085
## 7 1.0746 nan 0.1000 0.0086
## 8 1.0537 nan 0.1000 0.0076
## 9 1.0377 nan 0.1000 0.0055
## 10 1.0275 nan 0.1000 0.0020
## 20 0.9273 nan 0.1000 0.0022
## 40 0.8437 nan 0.1000 -0.0016
## 60 0.7957 nan 0.1000 -0.0015
## 80 0.7588 nan 0.1000 -0.0009
## 100 0.7320 nan 0.1000 -0.0023
## 120 0.7050 nan 0.1000 -0.0020
## 140 0.6790 nan 0.1000 0.0006
## 160 0.6623 nan 0.1000 -0.0014
## 180 0.6431 nan 0.1000 -0.0010
## 200 0.6279 nan 0.1000 -0.0012
## 220 0.6112 nan 0.1000 -0.0011
## 240 0.5892 nan 0.1000 -0.0010
## 260 0.5719 nan 0.1000 -0.0012
## 280 0.5596 nan 0.1000 -0.0018
## 300 0.5449 nan 0.1000 -0.0006
## 320 0.5286 nan 0.1000 -0.0009
## 340 0.5126 nan 0.1000 -0.0006
## 360 0.4997 nan 0.1000 -0.0021
## 380 0.4876 nan 0.1000 -0.0005
## 400 0.4755 nan 0.1000 -0.0003
## 420 0.4639 nan 0.1000 -0.0004
## 440 0.4537 nan 0.1000 -0.0004
## 460 0.4407 nan 0.1000 -0.0010
## 480 0.4313 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2465 nan 0.1000 0.0213
## 2 1.2100 nan 0.1000 0.0194
## 3 1.1772 nan 0.1000 0.0146
## 4 1.1467 nan 0.1000 0.0112
## 5 1.1202 nan 0.1000 0.0108
## 6 1.0935 nan 0.1000 0.0107
## 7 1.0722 nan 0.1000 0.0080
## 8 1.0518 nan 0.1000 0.0067
## 9 1.0382 nan 0.1000 0.0053
## 10 1.0220 nan 0.1000 0.0052
## 20 0.9247 nan 0.1000 0.0025
## 40 0.8494 nan 0.1000 -0.0003
## 60 0.8059 nan 0.1000 -0.0005
## 80 0.7647 nan 0.1000 -0.0010
## 100 0.7384 nan 0.1000 -0.0021
## 120 0.7147 nan 0.1000 -0.0028
## 140 0.6910 nan 0.1000 -0.0008
## 160 0.6678 nan 0.1000 -0.0015
## 180 0.6441 nan 0.1000 -0.0011
## 200 0.6249 nan 0.1000 -0.0012
## 220 0.6102 nan 0.1000 -0.0005
## 240 0.5925 nan 0.1000 -0.0005
## 260 0.5723 nan 0.1000 -0.0003
## 280 0.5546 nan 0.1000 -0.0009
## 300 0.5404 nan 0.1000 -0.0015
## 320 0.5238 nan 0.1000 -0.0004
## 340 0.5087 nan 0.1000 -0.0005
## 360 0.4960 nan 0.1000 -0.0020
## 380 0.4827 nan 0.1000 -0.0010
## 400 0.4725 nan 0.1000 -0.0008
## 420 0.4617 nan 0.1000 -0.0001
## 440 0.4523 nan 0.1000 -0.0003
## 460 0.4403 nan 0.1000 -0.0010
## 480 0.4288 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2512 nan 0.1000 0.0196
## 2 1.2131 nan 0.1000 0.0164
## 3 1.1763 nan 0.1000 0.0146
## 4 1.1473 nan 0.1000 0.0122
## 5 1.1238 nan 0.1000 0.0087
## 6 1.0992 nan 0.1000 0.0113
## 7 1.0781 nan 0.1000 0.0087
## 8 1.0576 nan 0.1000 0.0095
## 9 1.0414 nan 0.1000 0.0035
## 10 1.0278 nan 0.1000 0.0054
## 20 0.9377 nan 0.1000 0.0001
## 40 0.8471 nan 0.1000 -0.0003
## 60 0.8003 nan 0.1000 -0.0000
## 80 0.7678 nan 0.1000 -0.0018
## 100 0.7389 nan 0.1000 -0.0004
## 120 0.7174 nan 0.1000 -0.0013
## 140 0.6959 nan 0.1000 -0.0015
## 160 0.6780 nan 0.1000 -0.0007
## 180 0.6559 nan 0.1000 -0.0010
## 200 0.6400 nan 0.1000 -0.0009
## 220 0.6214 nan 0.1000 -0.0014
## 240 0.6022 nan 0.1000 -0.0011
## 260 0.5837 nan 0.1000 -0.0011
## 280 0.5657 nan 0.1000 -0.0014
## 300 0.5500 nan 0.1000 -0.0008
## 320 0.5379 nan 0.1000 -0.0012
## 340 0.5233 nan 0.1000 -0.0010
## 360 0.5106 nan 0.1000 -0.0010
## 380 0.4990 nan 0.1000 -0.0004
## 400 0.4897 nan 0.1000 -0.0014
## 420 0.4816 nan 0.1000 -0.0009
## 440 0.4719 nan 0.1000 -0.0013
## 460 0.4581 nan 0.1000 -0.0019
## 480 0.4477 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2385 nan 0.1000 0.0239
## 2 1.1929 nan 0.1000 0.0188
## 3 1.1550 nan 0.1000 0.0162
## 4 1.1242 nan 0.1000 0.0141
## 5 1.0916 nan 0.1000 0.0133
## 6 1.0660 nan 0.1000 0.0114
## 7 1.0440 nan 0.1000 0.0061
## 8 1.0254 nan 0.1000 0.0079
## 9 1.0092 nan 0.1000 0.0050
## 10 0.9911 nan 0.1000 0.0060
## 20 0.8902 nan 0.1000 -0.0011
## 40 0.7860 nan 0.1000 0.0004
## 60 0.7257 nan 0.1000 -0.0006
## 80 0.6850 nan 0.1000 -0.0023
## 100 0.6422 nan 0.1000 -0.0005
## 120 0.6045 nan 0.1000 -0.0017
## 140 0.5724 nan 0.1000 -0.0004
## 160 0.5418 nan 0.1000 -0.0004
## 180 0.5159 nan 0.1000 -0.0014
## 200 0.4843 nan 0.1000 -0.0013
## 220 0.4614 nan 0.1000 -0.0005
## 240 0.4418 nan 0.1000 -0.0019
## 260 0.4225 nan 0.1000 -0.0015
## 280 0.4059 nan 0.1000 -0.0006
## 300 0.3881 nan 0.1000 -0.0010
## 320 0.3738 nan 0.1000 -0.0010
## 340 0.3566 nan 0.1000 -0.0006
## 360 0.3393 nan 0.1000 -0.0006
## 380 0.3276 nan 0.1000 -0.0006
## 400 0.3143 nan 0.1000 -0.0004
## 420 0.3022 nan 0.1000 -0.0011
## 440 0.2911 nan 0.1000 -0.0007
## 460 0.2815 nan 0.1000 -0.0007
## 480 0.2710 nan 0.1000 -0.0005
## 500 0.2623 nan 0.1000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2332 nan 0.1000 0.0259
## 2 1.1914 nan 0.1000 0.0176
## 3 1.1503 nan 0.1000 0.0178
## 4 1.1168 nan 0.1000 0.0147
## 5 1.0853 nan 0.1000 0.0132
## 6 1.0600 nan 0.1000 0.0082
## 7 1.0397 nan 0.1000 0.0073
## 8 1.0169 nan 0.1000 0.0080
## 9 1.0007 nan 0.1000 0.0055
## 10 0.9844 nan 0.1000 0.0049
## 20 0.8839 nan 0.1000 -0.0009
## 40 0.7879 nan 0.1000 -0.0020
## 60 0.7350 nan 0.1000 -0.0020
## 80 0.6933 nan 0.1000 -0.0015
## 100 0.6511 nan 0.1000 -0.0017
## 120 0.6142 nan 0.1000 -0.0011
## 140 0.5887 nan 0.1000 -0.0011
## 160 0.5594 nan 0.1000 -0.0002
## 180 0.5306 nan 0.1000 -0.0018
## 200 0.5057 nan 0.1000 0.0003
## 220 0.4835 nan 0.1000 -0.0011
## 240 0.4572 nan 0.1000 -0.0007
## 260 0.4363 nan 0.1000 -0.0006
## 280 0.4182 nan 0.1000 -0.0009
## 300 0.4047 nan 0.1000 -0.0019
## 320 0.3864 nan 0.1000 -0.0012
## 340 0.3696 nan 0.1000 -0.0006
## 360 0.3572 nan 0.1000 -0.0019
## 380 0.3425 nan 0.1000 -0.0009
## 400 0.3264 nan 0.1000 -0.0004
## 420 0.3123 nan 0.1000 -0.0006
## 440 0.3001 nan 0.1000 -0.0011
## 460 0.2879 nan 0.1000 -0.0005
## 480 0.2787 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2423 nan 0.1000 0.0236
## 2 1.1962 nan 0.1000 0.0205
## 3 1.1592 nan 0.1000 0.0140
## 4 1.1296 nan 0.1000 0.0122
## 5 1.0990 nan 0.1000 0.0108
## 6 1.0760 nan 0.1000 0.0082
## 7 1.0507 nan 0.1000 0.0086
## 8 1.0314 nan 0.1000 0.0084
## 9 1.0124 nan 0.1000 0.0074
## 10 0.9961 nan 0.1000 0.0044
## 20 0.8841 nan 0.1000 0.0002
## 40 0.7893 nan 0.1000 -0.0012
## 60 0.7375 nan 0.1000 -0.0009
## 80 0.6945 nan 0.1000 -0.0044
## 100 0.6575 nan 0.1000 -0.0021
## 120 0.6282 nan 0.1000 -0.0025
## 140 0.5944 nan 0.1000 -0.0014
## 160 0.5689 nan 0.1000 -0.0015
## 180 0.5403 nan 0.1000 -0.0011
## 200 0.5181 nan 0.1000 -0.0015
## 220 0.4954 nan 0.1000 -0.0011
## 240 0.4733 nan 0.1000 -0.0019
## 260 0.4523 nan 0.1000 -0.0012
## 280 0.4343 nan 0.1000 -0.0004
## 300 0.4163 nan 0.1000 -0.0002
## 320 0.3970 nan 0.1000 0.0000
## 340 0.3802 nan 0.1000 0.0001
## 360 0.3640 nan 0.1000 -0.0012
## 380 0.3478 nan 0.1000 -0.0006
## 400 0.3337 nan 0.1000 -0.0001
## 420 0.3218 nan 0.1000 -0.0007
## 440 0.3118 nan 0.1000 -0.0011
## 460 0.2991 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2212 nan 0.2000 0.0309
## 2 1.1738 nan 0.2000 0.0176
## 3 1.1406 nan 0.2000 0.0155
## 4 1.1084 nan 0.2000 0.0143
## 5 1.0798 nan 0.2000 0.0116
## 6 1.0530 nan 0.2000 0.0112
## 7 1.0337 nan 0.2000 0.0047
## 8 1.0163 nan 0.2000 0.0064
## 9 1.0010 nan 0.2000 0.0056
## 10 0.9867 nan 0.2000 0.0060
## 20 0.9144 nan 0.2000 0.0011
## 40 0.8434 nan 0.2000 -0.0028
## 60 0.8128 nan 0.2000 -0.0012
## 80 0.7894 nan 0.2000 -0.0018
## 100 0.7722 nan 0.2000 -0.0028
## 120 0.7565 nan 0.2000 -0.0038
## 140 0.7411 nan 0.2000 -0.0012
## 160 0.7285 nan 0.2000 -0.0021
## 180 0.7157 nan 0.2000 -0.0023
## 200 0.7068 nan 0.2000 -0.0033
## 220 0.6942 nan 0.2000 -0.0019
## 240 0.6860 nan 0.2000 -0.0040
## 260 0.6809 nan 0.2000 -0.0030
## 280 0.6704 nan 0.2000 -0.0030
## 300 0.6619 nan 0.2000 -0.0014
## 320 0.6568 nan 0.2000 -0.0010
## 340 0.6522 nan 0.2000 -0.0016
## 360 0.6453 nan 0.2000 -0.0023
## 380 0.6358 nan 0.2000 -0.0022
## 400 0.6335 nan 0.2000 -0.0041
## 420 0.6258 nan 0.2000 -0.0024
## 440 0.6192 nan 0.2000 -0.0007
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## 480 0.6087 nan 0.2000 -0.0021
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2246 nan 0.2000 0.0306
## 2 1.1771 nan 0.2000 0.0205
## 3 1.1457 nan 0.2000 0.0117
## 4 1.1143 nan 0.2000 0.0137
## 5 1.0861 nan 0.2000 0.0107
## 6 1.0624 nan 0.2000 0.0114
## 7 1.0445 nan 0.2000 0.0092
## 8 1.0279 nan 0.2000 0.0044
## 9 1.0125 nan 0.2000 0.0064
## 10 1.0013 nan 0.2000 0.0026
## 20 0.9240 nan 0.2000 0.0005
## 40 0.8551 nan 0.2000 0.0003
## 60 0.8178 nan 0.2000 -0.0019
## 80 0.7890 nan 0.2000 -0.0014
## 100 0.7747 nan 0.2000 -0.0008
## 120 0.7603 nan 0.2000 -0.0008
## 140 0.7500 nan 0.2000 -0.0007
## 160 0.7390 nan 0.2000 -0.0028
## 180 0.7296 nan 0.2000 -0.0033
## 200 0.7190 nan 0.2000 -0.0023
## 220 0.7116 nan 0.2000 -0.0016
## 240 0.7034 nan 0.2000 -0.0017
## 260 0.6952 nan 0.2000 -0.0020
## 280 0.6887 nan 0.2000 -0.0034
## 300 0.6781 nan 0.2000 -0.0017
## 320 0.6682 nan 0.2000 -0.0012
## 340 0.6602 nan 0.2000 -0.0017
## 360 0.6553 nan 0.2000 -0.0019
## 380 0.6476 nan 0.2000 -0.0004
## 400 0.6419 nan 0.2000 -0.0017
## 420 0.6366 nan 0.2000 -0.0027
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## 460 0.6252 nan 0.2000 -0.0004
## 480 0.6199 nan 0.2000 -0.0013
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2273 nan 0.2000 0.0320
## 2 1.1843 nan 0.2000 0.0217
## 3 1.1448 nan 0.2000 0.0188
## 4 1.1170 nan 0.2000 0.0110
## 5 1.0903 nan 0.2000 0.0119
## 6 1.0628 nan 0.2000 0.0113
## 7 1.0472 nan 0.2000 0.0050
## 8 1.0304 nan 0.2000 0.0045
## 9 1.0099 nan 0.2000 0.0078
## 10 0.9952 nan 0.2000 0.0050
## 20 0.9174 nan 0.2000 -0.0009
## 40 0.8519 nan 0.2000 -0.0026
## 60 0.8172 nan 0.2000 -0.0021
## 80 0.7949 nan 0.2000 -0.0017
## 100 0.7826 nan 0.2000 -0.0009
## 120 0.7674 nan 0.2000 -0.0025
## 140 0.7534 nan 0.2000 -0.0044
## 160 0.7411 nan 0.2000 -0.0028
## 180 0.7305 nan 0.2000 -0.0009
## 200 0.7181 nan 0.2000 -0.0021
## 220 0.7093 nan 0.2000 -0.0033
## 240 0.7010 nan 0.2000 -0.0014
## 260 0.6903 nan 0.2000 -0.0010
## 280 0.6829 nan 0.2000 -0.0014
## 300 0.6742 nan 0.2000 -0.0003
## 320 0.6677 nan 0.2000 -0.0025
## 340 0.6624 nan 0.2000 -0.0033
## 360 0.6559 nan 0.2000 -0.0013
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2082 nan 0.2000 0.0425
## 2 1.1498 nan 0.2000 0.0267
## 3 1.1017 nan 0.2000 0.0228
## 4 1.0640 nan 0.2000 0.0172
## 5 1.0323 nan 0.2000 0.0137
## 6 1.0052 nan 0.2000 0.0125
## 7 0.9865 nan 0.2000 0.0063
## 8 0.9663 nan 0.2000 0.0040
## 9 0.9524 nan 0.2000 0.0021
## 10 0.9369 nan 0.2000 0.0062
## 20 0.8456 nan 0.2000 -0.0021
## 40 0.7633 nan 0.2000 -0.0036
## 60 0.7129 nan 0.2000 -0.0013
## 80 0.6754 nan 0.2000 -0.0014
## 100 0.6506 nan 0.2000 -0.0026
## 120 0.6187 nan 0.2000 -0.0035
## 140 0.5925 nan 0.2000 -0.0017
## 160 0.5543 nan 0.2000 -0.0014
## 180 0.5326 nan 0.2000 -0.0032
## 200 0.5038 nan 0.2000 -0.0015
## 220 0.4852 nan 0.2000 -0.0047
## 240 0.4669 nan 0.2000 -0.0021
## 260 0.4475 nan 0.2000 -0.0054
## 280 0.4173 nan 0.2000 -0.0026
## 300 0.3955 nan 0.2000 -0.0010
## 320 0.3784 nan 0.2000 -0.0025
## 340 0.3588 nan 0.2000 -0.0011
## 360 0.3433 nan 0.2000 -0.0014
## 380 0.3242 nan 0.2000 -0.0012
## 400 0.3107 nan 0.2000 -0.0008
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## 460 0.2798 nan 0.2000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2137 nan 0.2000 0.0359
## 2 1.1410 nan 0.2000 0.0347
## 3 1.0912 nan 0.2000 0.0230
## 4 1.0563 nan 0.2000 0.0135
## 5 1.0307 nan 0.2000 0.0065
## 6 1.0112 nan 0.2000 0.0034
## 7 0.9907 nan 0.2000 0.0070
## 8 0.9711 nan 0.2000 0.0062
## 9 0.9525 nan 0.2000 0.0056
## 10 0.9437 nan 0.2000 -0.0003
## 20 0.8515 nan 0.2000 0.0005
## 40 0.7617 nan 0.2000 -0.0013
## 60 0.7075 nan 0.2000 -0.0001
## 80 0.6624 nan 0.2000 -0.0004
## 100 0.6198 nan 0.2000 -0.0026
## 120 0.5815 nan 0.2000 -0.0040
## 140 0.5484 nan 0.2000 -0.0037
## 160 0.5220 nan 0.2000 -0.0006
## 180 0.4963 nan 0.2000 -0.0027
## 200 0.4751 nan 0.2000 -0.0024
## 220 0.4540 nan 0.2000 -0.0026
## 240 0.4294 nan 0.2000 -0.0038
## 260 0.4127 nan 0.2000 -0.0016
## 280 0.3957 nan 0.2000 -0.0026
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## 320 0.3598 nan 0.2000 -0.0021
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## 380 0.3162 nan 0.2000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2093 nan 0.2000 0.0351
## 2 1.1421 nan 0.2000 0.0275
## 3 1.1011 nan 0.2000 0.0212
## 4 1.0518 nan 0.2000 0.0186
## 5 1.0215 nan 0.2000 0.0088
## 6 0.9971 nan 0.2000 0.0100
## 7 0.9791 nan 0.2000 0.0047
## 8 0.9603 nan 0.2000 0.0080
## 9 0.9459 nan 0.2000 0.0046
## 10 0.9314 nan 0.2000 0.0046
## 20 0.8476 nan 0.2000 -0.0035
## 40 0.7758 nan 0.2000 -0.0024
## 60 0.7240 nan 0.2000 -0.0024
## 80 0.6748 nan 0.2000 -0.0017
## 100 0.6400 nan 0.2000 -0.0002
## 120 0.6100 nan 0.2000 -0.0021
## 140 0.5792 nan 0.2000 0.0004
## 160 0.5504 nan 0.2000 -0.0021
## 180 0.5251 nan 0.2000 -0.0040
## 200 0.5062 nan 0.2000 -0.0010
## 220 0.4855 nan 0.2000 -0.0055
## 240 0.4671 nan 0.2000 -0.0031
## 260 0.4500 nan 0.2000 -0.0028
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2027 nan 0.2000 0.0507
## 2 1.1131 nan 0.2000 0.0358
## 3 1.0656 nan 0.2000 0.0175
## 4 1.0204 nan 0.2000 0.0190
## 5 0.9870 nan 0.2000 0.0128
## 6 0.9580 nan 0.2000 0.0055
## 7 0.9405 nan 0.2000 0.0019
## 8 0.9252 nan 0.2000 0.0017
## 9 0.9038 nan 0.2000 0.0060
## 10 0.8907 nan 0.2000 0.0018
## 20 0.8015 nan 0.2000 -0.0025
## 40 0.7123 nan 0.2000 -0.0081
## 60 0.6329 nan 0.2000 -0.0025
## 80 0.5707 nan 0.2000 -0.0082
## 100 0.5114 nan 0.2000 -0.0042
## 120 0.4633 nan 0.2000 -0.0028
## 140 0.4302 nan 0.2000 -0.0015
## 160 0.3904 nan 0.2000 -0.0010
## 180 0.3612 nan 0.2000 -0.0021
## 200 0.3315 nan 0.2000 -0.0033
## 220 0.3057 nan 0.2000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1944 nan 0.2000 0.0452
## 2 1.1193 nan 0.2000 0.0344
## 3 1.0561 nan 0.2000 0.0228
## 4 1.0080 nan 0.2000 0.0177
## 5 0.9744 nan 0.2000 0.0113
## 6 0.9526 nan 0.2000 0.0061
## 7 0.9323 nan 0.2000 0.0063
## 8 0.9125 nan 0.2000 0.0043
## 9 0.8947 nan 0.2000 0.0024
## 10 0.8830 nan 0.2000 0.0010
## 20 0.8008 nan 0.2000 0.0011
## 40 0.7014 nan 0.2000 -0.0020
## 60 0.6268 nan 0.2000 -0.0051
## 80 0.5634 nan 0.2000 -0.0013
## 100 0.5051 nan 0.2000 -0.0044
## 120 0.4599 nan 0.2000 -0.0019
## 140 0.4206 nan 0.2000 -0.0017
## 160 0.3854 nan 0.2000 -0.0010
## 180 0.3526 nan 0.2000 -0.0017
## 200 0.3293 nan 0.2000 -0.0017
## 220 0.3030 nan 0.2000 -0.0031
## 240 0.2785 nan 0.2000 -0.0010
## 260 0.2596 nan 0.2000 -0.0010
## 280 0.2422 nan 0.2000 -0.0013
## 300 0.2261 nan 0.2000 -0.0008
## 320 0.2104 nan 0.2000 -0.0016
## 340 0.1940 nan 0.2000 -0.0023
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## 380 0.1699 nan 0.2000 -0.0011
## 400 0.1598 nan 0.2000 -0.0007
## 420 0.1490 nan 0.2000 -0.0005
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## 480 0.1214 nan 0.2000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1942 nan 0.2000 0.0482
## 2 1.1146 nan 0.2000 0.0354
## 3 1.0596 nan 0.2000 0.0236
## 4 1.0188 nan 0.2000 0.0123
## 5 0.9852 nan 0.2000 0.0129
## 6 0.9541 nan 0.2000 0.0081
## 7 0.9364 nan 0.2000 0.0032
## 8 0.9157 nan 0.2000 0.0031
## 9 0.8981 nan 0.2000 0.0030
## 10 0.8873 nan 0.2000 0.0007
## 20 0.7911 nan 0.2000 0.0010
## 40 0.6833 nan 0.2000 -0.0001
## 60 0.6192 nan 0.2000 -0.0073
## 80 0.5585 nan 0.2000 -0.0015
## 100 0.5055 nan 0.2000 -0.0024
## 120 0.4693 nan 0.2000 -0.0045
## 140 0.4330 nan 0.2000 -0.0020
## 160 0.3976 nan 0.2000 0.0003
## 180 0.3648 nan 0.2000 -0.0020
## 200 0.3325 nan 0.2000 -0.0014
## 220 0.3023 nan 0.2000 -0.0013
## 240 0.2766 nan 0.2000 -0.0005
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## 280 0.2415 nan 0.2000 -0.0020
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## 320 0.2063 nan 0.2000 -0.0012
## 340 0.1926 nan 0.2000 -0.0018
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## 380 0.1677 nan 0.2000 -0.0013
## 400 0.1574 nan 0.2000 -0.0008
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## 440 0.1381 nan 0.2000 -0.0013
## 460 0.1304 nan 0.2000 -0.0013
## 480 0.1235 nan 0.2000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1982 nan 0.3000 0.0501
## 2 1.1541 nan 0.3000 0.0209
## 3 1.0987 nan 0.3000 0.0250
## 4 1.0595 nan 0.3000 0.0141
## 5 1.0261 nan 0.3000 0.0145
## 6 1.0050 nan 0.3000 0.0095
## 7 0.9858 nan 0.3000 0.0060
## 8 0.9750 nan 0.3000 -0.0002
## 9 0.9559 nan 0.3000 0.0052
## 10 0.9404 nan 0.3000 0.0015
## 20 0.8856 nan 0.3000 -0.0010
## 40 0.8185 nan 0.3000 -0.0022
## 60 0.7890 nan 0.3000 -0.0027
## 80 0.7676 nan 0.3000 -0.0058
## 100 0.7523 nan 0.3000 -0.0024
## 120 0.7317 nan 0.3000 -0.0011
## 140 0.7148 nan 0.3000 -0.0026
## 160 0.6981 nan 0.3000 -0.0010
## 180 0.6825 nan 0.3000 -0.0007
## 200 0.6719 nan 0.3000 -0.0017
## 220 0.6602 nan 0.3000 -0.0021
## 240 0.6520 nan 0.3000 -0.0030
## 260 0.6437 nan 0.3000 -0.0041
## 280 0.6328 nan 0.3000 -0.0018
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## 320 0.6165 nan 0.3000 -0.0037
## 340 0.6108 nan 0.3000 -0.0019
## 360 0.6018 nan 0.3000 -0.0011
## 380 0.5971 nan 0.3000 -0.0028
## 400 0.5893 nan 0.3000 -0.0022
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## 460 0.5672 nan 0.3000 -0.0033
## 480 0.5651 nan 0.3000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2043 nan 0.3000 0.0440
## 2 1.1540 nan 0.3000 0.0204
## 3 1.1123 nan 0.3000 0.0140
## 4 1.0665 nan 0.3000 0.0195
## 5 1.0328 nan 0.3000 0.0158
## 6 1.0120 nan 0.3000 0.0066
## 7 1.0041 nan 0.3000 -0.0015
## 8 0.9824 nan 0.3000 0.0094
## 9 0.9639 nan 0.3000 0.0038
## 10 0.9539 nan 0.3000 0.0020
## 20 0.8746 nan 0.3000 -0.0017
## 40 0.8215 nan 0.3000 -0.0047
## 60 0.7918 nan 0.3000 -0.0061
## 80 0.7644 nan 0.3000 -0.0037
## 100 0.7462 nan 0.3000 -0.0024
## 120 0.7279 nan 0.3000 -0.0014
## 140 0.7152 nan 0.3000 -0.0024
## 160 0.6972 nan 0.3000 -0.0022
## 180 0.6906 nan 0.3000 -0.0033
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## 220 0.6658 nan 0.3000 -0.0022
## 240 0.6551 nan 0.3000 -0.0040
## 260 0.6470 nan 0.3000 -0.0031
## 280 0.6417 nan 0.3000 -0.0026
## 300 0.6335 nan 0.3000 -0.0038
## 320 0.6239 nan 0.3000 -0.0055
## 340 0.6130 nan 0.3000 -0.0023
## 360 0.6063 nan 0.3000 -0.0028
## 380 0.6018 nan 0.3000 -0.0013
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## 460 0.5728 nan 0.3000 -0.0020
## 480 0.5682 nan 0.3000 -0.0015
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2058 nan 0.3000 0.0485
## 2 1.1502 nan 0.3000 0.0266
## 3 1.1018 nan 0.3000 0.0125
## 4 1.0580 nan 0.3000 0.0149
## 5 1.0267 nan 0.3000 0.0154
## 6 1.0063 nan 0.3000 0.0055
## 7 0.9846 nan 0.3000 0.0059
## 8 0.9693 nan 0.3000 0.0023
## 9 0.9592 nan 0.3000 0.0027
## 10 0.9506 nan 0.3000 0.0020
## 20 0.8814 nan 0.3000 -0.0032
## 40 0.8221 nan 0.3000 -0.0030
## 60 0.7936 nan 0.3000 -0.0017
## 80 0.7658 nan 0.3000 -0.0033
## 100 0.7494 nan 0.3000 -0.0027
## 120 0.7308 nan 0.3000 -0.0009
## 140 0.7201 nan 0.3000 -0.0040
## 160 0.7035 nan 0.3000 -0.0045
## 180 0.6883 nan 0.3000 -0.0014
## 200 0.6763 nan 0.3000 -0.0014
## 220 0.6599 nan 0.3000 -0.0033
## 240 0.6492 nan 0.3000 -0.0017
## 260 0.6408 nan 0.3000 -0.0029
## 280 0.6309 nan 0.3000 -0.0033
## 300 0.6258 nan 0.3000 -0.0043
## 320 0.6139 nan 0.3000 -0.0027
## 340 0.6080 nan 0.3000 -0.0025
## 360 0.6040 nan 0.3000 -0.0038
## 380 0.6000 nan 0.3000 -0.0012
## 400 0.5938 nan 0.3000 -0.0034
## 420 0.5828 nan 0.3000 -0.0022
## 440 0.5746 nan 0.3000 -0.0012
## 460 0.5673 nan 0.3000 -0.0020
## 480 0.5623 nan 0.3000 -0.0014
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1671 nan 0.3000 0.0586
## 2 1.1024 nan 0.3000 0.0264
## 3 1.0565 nan 0.3000 0.0177
## 4 1.0112 nan 0.3000 0.0202
## 5 0.9794 nan 0.3000 0.0129
## 6 0.9537 nan 0.3000 0.0009
## 7 0.9341 nan 0.3000 0.0065
## 8 0.9163 nan 0.3000 0.0046
## 9 0.9004 nan 0.3000 0.0009
## 10 0.8870 nan 0.3000 0.0021
## 20 0.8100 nan 0.3000 -0.0088
## 40 0.7405 nan 0.3000 -0.0041
## 60 0.6715 nan 0.3000 -0.0048
## 80 0.5972 nan 0.3000 -0.0061
## 100 0.5531 nan 0.3000 -0.0039
## 120 0.5170 nan 0.3000 -0.0009
## 140 0.4757 nan 0.3000 -0.0049
## 160 0.4527 nan 0.3000 -0.0019
## 180 0.4221 nan 0.3000 -0.0020
## 200 0.3881 nan 0.3000 -0.0019
## 220 0.3660 nan 0.3000 -0.0029
## 240 0.3434 nan 0.3000 -0.0037
## 260 0.3190 nan 0.3000 -0.0022
## 280 0.2980 nan 0.3000 -0.0013
## 300 0.2788 nan 0.3000 -0.0019
## 320 0.2610 nan 0.3000 -0.0039
## 340 0.2478 nan 0.3000 -0.0030
## 360 0.2316 nan 0.3000 -0.0012
## 380 0.2151 nan 0.3000 -0.0003
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## 420 0.1943 nan 0.3000 -0.0021
## 440 0.1846 nan 0.3000 -0.0017
## 460 0.1769 nan 0.3000 -0.0014
## 480 0.1672 nan 0.3000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1846 nan 0.3000 0.0536
## 2 1.1057 nan 0.3000 0.0268
## 3 1.0491 nan 0.3000 0.0251
## 4 1.0071 nan 0.3000 0.0126
## 5 0.9858 nan 0.3000 -0.0010
## 6 0.9618 nan 0.3000 0.0070
## 7 0.9422 nan 0.3000 0.0041
## 8 0.9261 nan 0.3000 0.0011
## 9 0.9001 nan 0.3000 0.0063
## 10 0.8911 nan 0.3000 -0.0032
## 20 0.8095 nan 0.3000 -0.0028
## 40 0.7320 nan 0.3000 -0.0058
## 60 0.6663 nan 0.3000 -0.0020
## 80 0.6037 nan 0.3000 -0.0019
## 100 0.5520 nan 0.3000 0.0006
## 120 0.5173 nan 0.3000 -0.0055
## 140 0.4885 nan 0.3000 -0.0035
## 160 0.4562 nan 0.3000 -0.0032
## 180 0.4226 nan 0.3000 -0.0047
## 200 0.3931 nan 0.3000 -0.0051
## 220 0.3695 nan 0.3000 -0.0044
## 240 0.3502 nan 0.3000 -0.0036
## 260 0.3272 nan 0.3000 -0.0010
## 280 0.3104 nan 0.3000 -0.0017
## 300 0.2911 nan 0.3000 -0.0008
## 320 0.2738 nan 0.3000 -0.0019
## 340 0.2552 nan 0.3000 -0.0022
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## 380 0.2222 nan 0.3000 -0.0003
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## 460 0.1781 nan 0.3000 -0.0025
## 480 0.1704 nan 0.3000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1795 nan 0.3000 0.0500
## 2 1.0978 nan 0.3000 0.0342
## 3 1.0462 nan 0.3000 0.0229
## 4 0.9999 nan 0.3000 0.0198
## 5 0.9649 nan 0.3000 0.0070
## 6 0.9416 nan 0.3000 0.0073
## 7 0.9241 nan 0.3000 0.0053
## 8 0.9064 nan 0.3000 0.0023
## 9 0.9005 nan 0.3000 -0.0024
## 10 0.8910 nan 0.3000 -0.0071
## 20 0.8058 nan 0.3000 -0.0039
## 40 0.7259 nan 0.3000 -0.0079
## 60 0.6717 nan 0.3000 -0.0018
## 80 0.6343 nan 0.3000 -0.0013
## 100 0.5837 nan 0.3000 -0.0070
## 120 0.5432 nan 0.3000 -0.0012
## 140 0.5061 nan 0.3000 -0.0006
## 160 0.4676 nan 0.3000 -0.0032
## 180 0.4338 nan 0.3000 -0.0025
## 200 0.4020 nan 0.3000 -0.0016
## 220 0.3766 nan 0.3000 -0.0029
## 240 0.3593 nan 0.3000 -0.0042
## 260 0.3444 nan 0.3000 -0.0033
## 280 0.3206 nan 0.3000 -0.0048
## 300 0.2953 nan 0.3000 -0.0029
## 320 0.2791 nan 0.3000 -0.0019
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## 360 0.2475 nan 0.3000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1473 nan 0.3000 0.0715
## 2 1.0600 nan 0.3000 0.0363
## 3 0.9908 nan 0.3000 0.0241
## 4 0.9531 nan 0.3000 0.0149
## 5 0.9228 nan 0.3000 0.0027
## 6 0.8990 nan 0.3000 0.0017
## 7 0.8800 nan 0.3000 -0.0020
## 8 0.8621 nan 0.3000 0.0022
## 9 0.8488 nan 0.3000 -0.0002
## 10 0.8352 nan 0.3000 0.0000
## 20 0.7534 nan 0.3000 -0.0060
## 40 0.6653 nan 0.3000 -0.0063
## 60 0.5649 nan 0.3000 -0.0045
## 80 0.4740 nan 0.3000 -0.0026
## 100 0.4171 nan 0.3000 -0.0041
## 120 0.3785 nan 0.3000 -0.0034
## 140 2.8451 nan 0.3000 -0.0023
## 160 2.8160 nan 0.3000 -0.0045
## 180 2.7975 nan 0.3000 -0.0020
## 200 2.7782 nan 0.3000 -0.0022
## 220 2.7596 nan 0.3000 -0.0016
## 240 2.7431 nan 0.3000 -0.0011
## 260 2.7302 nan 0.3000 -0.0008
## 280 2.7113 nan 0.3000 -0.0003
## 300 2.7033 nan 0.3000 -0.0010
## 320 2.6963 nan 0.3000 -0.0007
## 340 2.6856 nan 0.3000 -0.0009
## 360 2.6779 nan 0.3000 -0.0019
## 380 2.6711 nan 0.3000 -0.0017
## 400 2.6586 nan 0.3000 -0.0003
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## 440 2.6471 nan 0.3000 -0.0012
## 460 2.6370 nan 0.3000 -0.0014
## 480 2.6288 nan 0.3000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1390 nan 0.3000 0.0647
## 2 1.0475 nan 0.3000 0.0420
## 3 0.9974 nan 0.3000 0.0170
## 4 0.9510 nan 0.3000 0.0134
## 5 0.9172 nan 0.3000 0.0027
## 6 0.8963 nan 0.3000 0.0009
## 7 0.8774 nan 0.3000 -0.0005
## 8 0.8687 nan 0.3000 -0.0059
## 9 0.8604 nan 0.3000 -0.0028
## 10 0.8541 nan 0.3000 -0.0058
## 20 0.7578 nan 0.3000 -0.0054
## 40 0.6394 nan 0.3000 -0.0032
## 60 0.5584 nan 0.3000 -0.0035
## 80 0.4871 nan 0.3000 -0.0021
## 100 0.4240 nan 0.3000 -0.0018
## 120 0.3826 nan 0.3000 -0.0030
## 140 0.3538 nan 0.3000 -0.0015
## 160 0.2956 nan 0.3000 -0.0012
## 180 0.2642 nan 0.3000 -0.0018
## 200 0.2340 nan 0.3000 -0.0011
## 220 0.2058 nan 0.3000 -0.0011
## 240 0.1827 nan 0.3000 -0.0007
## 260 0.1659 nan 0.3000 -0.0011
## 280 0.1505 nan 0.3000 -0.0025
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## 320 0.1271 nan 0.3000 -0.0004
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## 360 0.1043 nan 0.3000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1570 nan 0.3000 0.0603
## 2 1.0774 nan 0.3000 0.0317
## 3 1.0119 nan 0.3000 0.0256
## 4 0.9772 nan 0.3000 0.0030
## 5 0.9451 nan 0.3000 0.0070
## 6 0.9264 nan 0.3000 -0.0010
## 7 0.8890 nan 0.3000 0.0015
## 8 0.8759 nan 0.3000 -0.0047
## 9 0.8611 nan 0.3000 -0.0058
## 10 0.8515 nan 0.3000 -0.0082
## 20 0.7578 nan 0.3000 -0.0053
## 40 0.6427 nan 0.3000 -0.0070
## 60 0.5541 nan 0.3000 -0.0031
## 80 0.4851 nan 0.3000 -0.0043
## 100 0.4334 nan 0.3000 -0.0047
## 120 0.3815 nan 0.3000 -0.0010
## 140 0.3262 nan 0.3000 -0.0011
## 160 0.2918 nan 0.3000 -0.0030
## 180 0.2615 nan 0.3000 -0.0010
## 200 0.2346 nan 0.3000 -0.0037
## 220 0.2155 nan 0.3000 -0.0024
## 240 0.1932 nan 0.3000 -0.0029
## 260 0.1728 nan 0.3000 -0.0004
## 280 0.1548 nan 0.3000 -0.0011
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## 320 0.1299 nan 0.3000 -0.0023
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## 380 0.0944 nan 0.3000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1478 nan 0.5000 0.0494
## 2 1.0780 nan 0.5000 0.0307
## 3 1.0336 nan 0.5000 0.0204
## 4 0.9860 nan 0.5000 0.0218
## 5 0.9594 nan 0.5000 0.0110
## 6 0.9520 nan 0.5000 -0.0046
## 7 0.9347 nan 0.5000 0.0017
## 8 0.9198 nan 0.5000 -0.0004
## 9 0.9113 nan 0.5000 -0.0011
## 10 0.9050 nan 0.5000 -0.0050
## 20 0.8510 nan 0.5000 -0.0044
## 40 0.7897 nan 0.5000 -0.0031
## 60 0.7570 nan 0.5000 -0.0046
## 80 0.7387 nan 0.5000 -0.0030
## 100 0.7089 nan 0.5000 -0.0055
## 120 0.6803 nan 0.5000 -0.0032
## 140 0.6632 nan 0.5000 0.0007
## 160 0.6573 nan 0.5000 -0.0056
## 180 0.6442 nan 0.5000 -0.0030
## 200 0.6313 nan 0.5000 -0.0049
## 220 0.6171 nan 0.5000 -0.0070
## 240 0.6061 nan 0.5000 -0.0012
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## 280 0.5742 nan 0.5000 -0.0031
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## 320 0.5558 nan 0.5000 -0.0043
## 340 0.5448 nan 0.5000 -0.0063
## 360 0.5346 nan 0.5000 -0.0039
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1506 nan 0.5000 0.0489
## 2 1.0872 nan 0.5000 0.0272
## 3 1.0386 nan 0.5000 0.0240
## 4 0.9928 nan 0.5000 0.0179
## 5 0.9766 nan 0.5000 0.0025
## 6 0.9642 nan 0.5000 -0.0030
## 7 0.9453 nan 0.5000 0.0013
## 8 0.9317 nan 0.5000 0.0057
## 9 0.9107 nan 0.5000 0.0021
## 10 0.9076 nan 0.5000 -0.0059
## 20 0.8406 nan 0.5000 -0.0093
## 40 0.8084 nan 0.5000 -0.0071
## 60 0.7698 nan 0.5000 -0.0116
## 80 0.7476 nan 0.5000 -0.0125
## 100 0.7088 nan 0.5000 -0.0028
## 120 0.6984 nan 0.5000 -0.0035
## 140 0.6659 nan 0.5000 -0.0022
## 160 0.6587 nan 0.5000 -0.0044
## 180 0.6525 nan 0.5000 -0.0021
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## 220 0.6051 nan 0.5000 -0.0026
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## 280 0.5726 nan 0.5000 -0.0027
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## 320 0.5569 nan 0.5000 -0.0032
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1602 nan 0.5000 0.0668
## 2 1.0976 nan 0.5000 0.0309
## 3 1.0479 nan 0.5000 0.0148
## 4 0.9998 nan 0.5000 0.0208
## 5 0.9734 nan 0.5000 0.0090
## 6 0.9420 nan 0.5000 0.0114
## 7 0.9275 nan 0.5000 0.0032
## 8 0.9134 nan 0.5000 0.0039
## 9 0.9056 nan 0.5000 -0.0035
## 10 0.8946 nan 0.5000 -0.0039
## 20 0.8405 nan 0.5000 -0.0047
## 40 0.7891 nan 0.5000 -0.0018
## 60 0.7597 nan 0.5000 -0.0021
## 80 0.7295 nan 0.5000 -0.0020
## 100 0.7184 nan 0.5000 -0.0040
## 120 0.6968 nan 0.5000 -0.0107
## 140 0.6745 nan 0.5000 -0.0019
## 160 0.6649 nan 0.5000 -0.0062
## 180 0.6462 nan 0.5000 -0.0080
## 200 0.6363 nan 0.5000 -0.0063
## 220 0.6200 nan 0.5000 -0.0054
## 240 0.6041 nan 0.5000 -0.0069
## 260 0.5911 nan 0.5000 -0.0093
## 280 0.5731 nan 0.5000 -0.0053
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## 320 0.5573 nan 0.5000 -0.0066
## 340 0.5550 nan 0.5000 -0.0018
## 360 inf nan 0.5000 nan
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## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1274 nan 0.5000 0.0704
## 2 1.0336 nan 0.5000 0.0413
## 3 0.9897 nan 0.5000 0.0161
## 4 0.9577 nan 0.5000 0.0044
## 5 0.9160 nan 0.5000 0.0179
## 6 0.8922 nan 0.5000 0.0036
## 7 0.8867 nan 0.5000 -0.0144
## 8 0.8694 nan 0.5000 -0.0033
## 9 0.8786 nan 0.5000 -0.0236
## 10 0.8565 nan 0.5000 0.0017
## 20 0.7921 nan 0.5000 -0.0104
## 40 0.6825 nan 0.5000 -0.0129
## 60 0.6104 nan 0.5000 -0.0112
## 80 0.5439 nan 0.5000 -0.0089
## 100 0.4773 nan 0.5000 -0.0056
## 120 0.4338 nan 0.5000 -0.0033
## 140 0.3813 nan 0.5000 -0.0028
## 160 0.3388 nan 0.5000 -0.0100
## 180 0.3134 nan 0.5000 -0.0074
## 200 0.2864 nan 0.5000 -0.0082
## 220 0.2650 nan 0.5000 -0.0008
## 240 0.2466 nan 0.5000 -0.0014
## 260 0.2149 nan 0.5000 -0.0037
## 280 0.1910 nan 0.5000 -0.0039
## 300 0.1714 nan 0.5000 -0.0015
## 320 0.1617 nan 0.5000 -0.0025
## 340 0.1475 nan 0.5000 -0.0002
## 360 0.1340 nan 0.5000 -0.0015
## 380 0.1238 nan 0.5000 -0.0004
## 400 0.1128 nan 0.5000 -0.0013
## 420 0.1035 nan 0.5000 -0.0010
## 440 0.0968 nan 0.5000 -0.0004
## 460 0.0911 nan 0.5000 -0.0013
## 480 0.0844 nan 0.5000 -0.0014
## 500 0.0763 nan 0.5000 -0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1131 nan 0.5000 0.0816
## 2 1.0314 nan 0.5000 0.0330
## 3 0.9630 nan 0.5000 0.0292
## 4 0.9231 nan 0.5000 0.0160
## 5 0.9067 nan 0.5000 0.0041
## 6 0.8946 nan 0.5000 -0.0088
## 7 0.8807 nan 0.5000 -0.0019
## 8 0.8707 nan 0.5000 -0.0076
## 9 0.8587 nan 0.5000 -0.0076
## 10 0.8464 nan 0.5000 -0.0036
## 20 0.7887 nan 0.5000 -0.0134
## 40 0.6840 nan 0.5000 -0.0045
## 60 0.6297 nan 0.5000 -0.0027
## 80 0.5491 nan 0.5000 -0.0098
## 100 0.4846 nan 0.5000 -0.0073
## 120 0.4374 nan 0.5000 -0.0089
## 140 0.4190 nan 0.5000 -0.0073
## 160 0.3573 nan 0.5000 -0.0035
## 180 0.3416 nan 0.5000 -0.0045
## 200 23.4553 nan 0.5000 -0.0006
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1150 nan 0.5000 0.0732
## 2 1.0424 nan 0.5000 0.0275
## 3 0.9814 nan 0.5000 0.0258
## 4 0.9350 nan 0.5000 0.0192
## 5 0.9070 nan 0.5000 0.0093
## 6 0.8862 nan 0.5000 0.0041
## 7 0.8646 nan 0.5000 0.0040
## 8 0.8608 nan 0.5000 -0.0143
## 9 0.8560 nan 0.5000 -0.0180
## 10 0.8407 nan 0.5000 -0.0020
## 20 0.7565 nan 0.5000 -0.0054
## 40 0.6966 nan 0.5000 -0.0090
## 60 0.6140 nan 0.5000 -0.0042
## 80 0.5407 nan 0.5000 -0.0044
## 100 0.4711 nan 0.5000 -0.0003
## 120 0.4140 nan 0.5000 -0.0022
## 140 0.3711 nan 0.5000 -0.0023
## 160 0.3344 nan 0.5000 -0.0048
## 180 0.3022 nan 0.5000 -0.0049
## 200 0.2656 nan 0.5000 -0.0037
## 220 0.2358 nan 0.5000 -0.0068
## 240 0.2179 nan 0.5000 -0.0055
## 260 0.1931 nan 0.5000 -0.0031
## 280 0.1705 nan 0.5000 -0.0010
## 300 0.1527 nan 0.5000 -0.0026
## 320 0.1355 nan 0.5000 -0.0013
## 340 0.1269 nan 0.5000 -0.0023
## 360 0.1149 nan 0.5000 -0.0006
## 380 0.1087 nan 0.5000 -0.0024
## 400 0.1023 nan 0.5000 -0.0013
## 420 0.0924 nan 0.5000 -0.0007
## 440 0.0880 nan 0.5000 -0.0016
## 460 0.0806 nan 0.5000 -0.0012
## 480 0.0763 nan 0.5000 -0.0008
## 500 0.0710 nan 0.5000 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0756 nan 0.5000 0.0886
## 2 0.9822 nan 0.5000 0.0403
## 3 0.9137 nan 0.5000 0.0267
## 4 0.8925 nan 0.5000 -0.0073
## 5 0.8699 nan 0.5000 -0.0064
## 6 0.8608 nan 0.5000 -0.0087
## 7 0.8307 nan 0.5000 0.0035
## 8 0.8216 nan 0.5000 -0.0068
## 9 0.7961 nan 0.5000 0.0024
## 10 0.7848 nan 0.5000 -0.0057
## 20 0.6840 nan 0.5000 -0.0065
## 40 0.5528 nan 0.5000 -0.0213
## 60 0.4569 nan 0.5000 -0.0048
## 80 0.4536 nan 0.5000 -0.0062
## 100 22630244148.0991 nan 0.5000 -0.0021
## 120 22630244148.0591 nan 0.5000 -0.0069
## 140 22630244148.0099 nan 0.5000 -0.0017
## 160 22631440123.1145 nan 0.5000 -0.0034
## 180 22680938572.3939 nan 0.5000 0.0001
## 200 22680965263.3868 nan 0.5000 -0.0025
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0775 nan 0.5000 0.0949
## 2 1.0063 nan 0.5000 0.0155
## 3 0.9610 nan 0.5000 0.0013
## 4 0.9170 nan 0.5000 0.0074
## 5 0.8895 nan 0.5000 -0.0025
## 6 0.8661 nan 0.5000 -0.0056
## 7 0.8323 nan 0.5000 0.0076
## 8 0.8131 nan 0.5000 -0.0098
## 9 0.8062 nan 0.5000 -0.0114
## 10 0.7904 nan 0.5000 -0.0059
## 20 0.7022 nan 0.5000 -0.0089
## 40 0.5425 nan 0.5000 -0.0140
## 60 0.4699 nan 0.5000 -0.0128
## 80 0.3981 nan 0.5000 -0.0065
## 100 0.3264 nan 0.5000 -0.0004
## 120 0.2719 nan 0.5000 -0.0067
## 140 0.2222 nan 0.5000 -0.0048
## 160 0.1865 nan 0.5000 -0.0019
## 180 0.1570 nan 0.5000 -0.0006
## 200 0.1323 nan 0.5000 -0.0010
## 220 0.1183 nan 0.5000 -0.0013
## 240 0.0983 nan 0.5000 -0.0013
## 260 0.0874 nan 0.5000 -0.0009
## 280 0.0752 nan 0.5000 -0.0012
## 300 0.0657 nan 0.5000 -0.0000
## 320 0.0566 nan 0.5000 -0.0009
## 340 0.0510 nan 0.5000 -0.0009
## 360 0.0455 nan 0.5000 -0.0008
## 380 0.0409 nan 0.5000 -0.0005
## 400 0.0358 nan 0.5000 -0.0004
## 420 0.0321 nan 0.5000 -0.0004
## 440 0.0282 nan 0.5000 -0.0005
## 460 0.0249 nan 0.5000 -0.0004
## 480 0.0227 nan 0.5000 -0.0004
## 500 0.0192 nan 0.5000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0827 nan 0.5000 0.0827
## 2 1.0068 nan 0.5000 0.0220
## 3 0.9590 nan 0.5000 0.0036
## 4 0.9296 nan 0.5000 0.0040
## 5 0.9214 nan 0.5000 -0.0188
## 6 0.8886 nan 0.5000 0.0029
## 7 0.8834 nan 0.5000 -0.0197
## 8 0.8677 nan 0.5000 -0.0169
## 9 0.8465 nan 0.5000 -0.0010
## 10 0.8472 nan 0.5000 -0.0176
## 20 0.7527 nan 0.5000 -0.0291
## 40 0.5742 nan 0.5000 -0.0036
## 60 0.4589 nan 0.5000 -0.0076
## 80 0.3763 nan 0.5000 -0.0100
## 100 0.3305 nan 0.5000 -0.0050
## 120 0.2829 nan 0.5000 -0.0174
## 140 0.2301 nan 0.5000 -0.0052
## 160 0.1882 nan 0.5000 -0.0045
## 180 0.1602 nan 0.5000 -0.0040
## 200 0.1390 nan 0.5000 -0.0025
## 220 0.1204 nan 0.5000 -0.0022
## 240 0.1064 nan 0.5000 -0.0014
## 260 0.0923 nan 0.5000 -0.0029
## 280 0.0794 nan 0.5000 -0.0010
## 300 0.0689 nan 0.5000 -0.0024
## 320 0.0600 nan 0.5000 -0.0007
## 340 0.0530 nan 0.5000 -0.0011
## 360 0.0454 nan 0.5000 -0.0007
## 380 0.0379 nan 0.5000 -0.0004
## 400 0.0329 nan 0.5000 -0.0003
## 420 0.0289 nan 0.5000 -0.0006
## 440 0.0259 nan 0.5000 -0.0002
## 460 0.0232 nan 0.5000 -0.0003
## 480 0.0204 nan 0.5000 -0.0004
## 500 0.0184 nan 0.5000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1415 nan 1.0000 0.0477
## 2 1.0819 nan 1.0000 0.0192
## 3 1.0137 nan 1.0000 0.0228
## 4 0.9767 nan 1.0000 -0.0033
## 5 0.9807 nan 1.0000 -0.0202
## 6 0.9685 nan 1.0000 -0.0047
## 7 0.9523 nan 1.0000 -0.0152
## 8 0.9413 nan 1.0000 0.0016
## 9 0.9426 nan 1.0000 -0.0128
## 10 0.9359 nan 1.0000 -0.0055
## 20 0.9211 nan 1.0000 -0.0370
## 40 2.1122 nan 1.0000 -0.0171
## 60 2.4327 nan 1.0000 -0.0058
## 80 3.9531 nan 1.0000 -0.0007
## 100 3.9017 nan 1.0000 -0.0271
## 120 34.0067 nan 1.0000 -30.2730
## 140 234630371324689.1875 nan 1.0000 -0.0275
## 160 234630371324689.4688 nan 1.0000 -0.1714
## 180 234630371324689.1875 nan 1.0000 0.0003
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1148 nan 1.0000 0.0774
## 2 1.0441 nan 1.0000 0.0199
## 3 0.9822 nan 1.0000 0.0318
## 4 0.9885 nan 1.0000 -0.0263
## 5 0.9632 nan 1.0000 0.0053
## 6 0.9607 nan 1.0000 -0.0098
## 7 0.9602 nan 1.0000 -0.0223
## 8 0.9775 nan 1.0000 -0.0394
## 9 0.9280 nan 1.0000 0.0148
## 10 0.9351 nan 1.0000 -0.0295
## 20 0.8712 nan 1.0000 -0.0185
## 40 0.8049 nan 1.0000 0.0110
## 60 0.7838 nan 1.0000 -0.0018
## 80 0.8474 nan 1.0000 -0.0444
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1215 nan 1.0000 0.0606
## 2 1.0560 nan 1.0000 0.0116
## 3 1.0045 nan 1.0000 0.0146
## 4 0.9922 nan 1.0000 -0.0017
## 5 0.9730 nan 1.0000 0.0012
## 6 0.9553 nan 1.0000 -0.0012
## 7 0.9387 nan 1.0000 -0.0043
## 8 0.9260 nan 1.0000 -0.0117
## 9 0.8917 nan 1.0000 0.0127
## 10 0.8950 nan 1.0000 -0.0140
## 20 0.8816 nan 1.0000 -0.0260
## 40 5704.2166 nan 1.0000 -0.0143
## 60 5721.8659 nan 1.0000 -0.0129
## 80 5721.8614 nan 1.0000 -0.0042
## 100 5721.8882 nan 1.0000 -0.0615
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 -0.0022
## 160 inf nan 1.0000 0.0023
## 180 inf nan 1.0000 0.0000
## 200 343685242.5997 nan 1.0000 -0.0011
## 220 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0585 nan 1.0000 0.0827
## 2 1.0099 nan 1.0000 -0.0055
## 3 0.9683 nan 1.0000 -0.0000
## 4 0.9592 nan 1.0000 -0.0165
## 5 0.9449 nan 1.0000 -0.0102
## 6 0.9414 nan 1.0000 -0.0286
## 7 0.9300 nan 1.0000 -0.0290
## 8 0.9339 nan 1.0000 -0.0385
## 9 0.8871 nan 1.0000 0.0098
## 10 0.9135 nan 1.0000 -0.0599
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0654 nan 1.0000 0.0694
## 2 0.9956 nan 1.0000 0.0128
## 3 0.9669 nan 1.0000 -0.0087
## 4 0.9978 nan 1.0000 -0.0806
## 5 1.0003 nan 1.0000 -0.0247
## 6 1.0069 nan 1.0000 -0.0363
## 7 1.0178 nan 1.0000 -0.0537
## 8 0.9954 nan 1.0000 -0.0089
## 9 0.9820 nan 1.0000 -0.0032
## 10 0.9988 nan 1.0000 -0.0316
## 20 2.2338 nan 1.0000 0.3198
## 40 1.7560 nan 1.0000 -0.0233
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0600 nan 1.0000 0.0985
## 2 0.9304 nan 1.0000 0.0380
## 3 0.9134 nan 1.0000 -0.0063
## 4 0.9274 nan 1.0000 -0.0275
## 5 0.9218 nan 1.0000 -0.0085
## 6 0.9142 nan 1.0000 -0.0192
## 7 1.1203 nan 1.0000 -0.1336
## 8 1.1182 nan 1.0000 -0.0187
## 9 1.1151 nan 1.0000 -0.0190
## 10 1.1011 nan 1.0000 -0.0212
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0199 nan 1.0000 0.1042
## 2 0.9746 nan 1.0000 -0.0148
## 3 0.9244 nan 1.0000 -0.0149
## 4 0.8752 nan 1.0000 0.0047
## 5 0.9299 nan 1.0000 -0.1092
## 6 0.8455 nan 1.0000 0.0259
## 7 0.8487 nan 1.0000 -0.0310
## 8 0.8439 nan 1.0000 -0.0217
## 9 0.8338 nan 1.0000 -0.0169
## 10 0.8282 nan 1.0000 -0.0273
## 20 0.7515 nan 1.0000 -0.0412
## 40 0.9360 nan 1.0000 -0.0119
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0681 nan 1.0000 0.0708
## 2 1.0003 nan 1.0000 -0.0005
## 3 0.9393 nan 1.0000 0.0063
## 4 0.9153 nan 1.0000 -0.0206
## 5 0.8680 nan 1.0000 -0.0134
## 6 0.8388 nan 1.0000 0.0018
## 7 0.8251 nan 1.0000 -0.0111
## 8 0.8178 nan 1.0000 -0.0284
## 9 1.0269 nan 1.0000 -0.2314
## 10 5235298466.0151 nan 1.0000 -5227733004.9460
## 20 5235999052.3480 nan 1.0000 -0.0220
## 40 5235999052.0172 nan 1.0000 -0.0381
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 117335725592.7703 nan 1.0000 -0.0273
## 140 117480139077.8864 nan 1.0000 -0.0049
## 160 117480139081.7836 nan 1.0000 0.0070
## 180 117480139081.7830 nan 1.0000 -0.0131
## 200 117480139081.7092 nan 1.0000 -0.0095
## 220 117480139081.6291 nan 1.0000 0.0064
## 240 117480139081.5282 nan 1.0000 0.0041
## 260 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0298 nan 1.0000 0.1343
## 2 0.9483 nan 1.0000 0.0097
## 3 0.9262 nan 1.0000 -0.0258
## 4 0.8939 nan 1.0000 -0.0017
## 5 0.9006 nan 1.0000 -0.0307
## 6 0.9028 nan 1.0000 -0.0265
## 7 0.9125 nan 1.0000 -0.0519
## 8 0.9228 nan 1.0000 -0.0527
## 9 0.9025 nan 1.0000 -0.0576
## 10 0.9227 nan 1.0000 -0.0776
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
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## 3 1.2923 nan 0.0010 0.0002
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## 6 1.2911 nan 0.0010 0.0002
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## 10 1.2896 nan 0.0010 0.0001
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## 40 1.2788 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
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## 3 1.2923 nan 0.0010 0.0002
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## 6 1.2912 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## 3 1.2920 nan 0.0010 0.0002
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## 5 1.2910 nan 0.0010 0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## 3 1.2920 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
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## 3 1.2920 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
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## 400 1.1318 nan 0.0010 0.0001
## 420 1.1255 nan 0.0010 0.0002
## 440 1.1198 nan 0.0010 0.0001
## 460 1.1142 nan 0.0010 0.0001
## 480 1.1088 nan 0.0010 0.0001
## 500 1.1033 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0003
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0003
## 5 1.2907 nan 0.0010 0.0002
## 6 1.2902 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0003
## 8 1.2891 nan 0.0010 0.0002
## 9 1.2885 nan 0.0010 0.0003
## 10 1.2880 nan 0.0010 0.0003
## 20 1.2826 nan 0.0010 0.0002
## 40 1.2720 nan 0.0010 0.0002
## 60 1.2621 nan 0.0010 0.0002
## 80 1.2526 nan 0.0010 0.0002
## 100 1.2433 nan 0.0010 0.0002
## 120 1.2343 nan 0.0010 0.0002
## 140 1.2254 nan 0.0010 0.0002
## 160 1.2166 nan 0.0010 0.0002
## 180 1.2084 nan 0.0010 0.0002
## 200 1.2004 nan 0.0010 0.0002
## 220 1.1923 nan 0.0010 0.0002
## 240 1.1846 nan 0.0010 0.0001
## 260 1.1774 nan 0.0010 0.0002
## 280 1.1703 nan 0.0010 0.0001
## 300 1.1635 nan 0.0010 0.0001
## 320 1.1567 nan 0.0010 0.0001
## 340 1.1502 nan 0.0010 0.0002
## 360 1.1438 nan 0.0010 0.0001
## 380 1.1374 nan 0.0010 0.0002
## 400 1.1313 nan 0.0010 0.0001
## 420 1.1254 nan 0.0010 0.0001
## 440 1.1198 nan 0.0010 0.0001
## 460 1.1143 nan 0.0010 0.0001
## 480 1.1088 nan 0.0010 0.0001
## 500 1.1034 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2636 nan 0.1000 0.0143
## 2 1.2303 nan 0.1000 0.0143
## 3 1.2036 nan 0.1000 0.0080
## 4 1.1788 nan 0.1000 0.0112
## 5 1.1578 nan 0.1000 0.0076
## 6 1.1398 nan 0.1000 0.0066
## 7 1.1243 nan 0.1000 0.0057
## 8 1.1099 nan 0.1000 0.0047
## 9 1.0993 nan 0.1000 0.0035
## 10 1.0876 nan 0.1000 0.0052
## 20 1.0009 nan 0.1000 0.0031
## 40 0.9215 nan 0.1000 0.0007
## 60 0.8785 nan 0.1000 -0.0006
## 80 0.8502 nan 0.1000 -0.0004
## 100 0.8311 nan 0.1000 -0.0009
## 120 0.8186 nan 0.1000 -0.0004
## 140 0.8045 nan 0.1000 -0.0003
## 160 0.7949 nan 0.1000 -0.0005
## 180 0.7861 nan 0.1000 -0.0011
## 200 0.7785 nan 0.1000 -0.0005
## 220 0.7690 nan 0.1000 -0.0009
## 240 0.7641 nan 0.1000 -0.0004
## 260 0.7590 nan 0.1000 -0.0006
## 280 0.7528 nan 0.1000 -0.0004
## 300 0.7454 nan 0.1000 -0.0008
## 320 0.7410 nan 0.1000 -0.0016
## 340 0.7328 nan 0.1000 -0.0006
## 360 0.7285 nan 0.1000 -0.0016
## 380 0.7243 nan 0.1000 -0.0006
## 400 0.7182 nan 0.1000 -0.0011
## 420 0.7133 nan 0.1000 -0.0010
## 440 0.7096 nan 0.1000 -0.0007
## 460 0.7032 nan 0.1000 -0.0001
## 480 0.6986 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2568 nan 0.1000 0.0172
## 2 1.2290 nan 0.1000 0.0142
## 3 1.2106 nan 0.1000 0.0058
## 4 1.1859 nan 0.1000 0.0119
## 5 1.1627 nan 0.1000 0.0091
## 6 1.1470 nan 0.1000 0.0062
## 7 1.1315 nan 0.1000 0.0065
## 8 1.1160 nan 0.1000 0.0057
## 9 1.1041 nan 0.1000 0.0045
## 10 1.0934 nan 0.1000 0.0045
## 20 1.0069 nan 0.1000 0.0032
## 40 0.9210 nan 0.1000 -0.0004
## 60 0.8795 nan 0.1000 -0.0003
## 80 0.8532 nan 0.1000 -0.0012
## 100 0.8323 nan 0.1000 -0.0012
## 120 0.8175 nan 0.1000 -0.0011
## 140 0.8051 nan 0.1000 -0.0009
## 160 0.7939 nan 0.1000 -0.0016
## 180 0.7855 nan 0.1000 -0.0014
## 200 0.7720 nan 0.1000 0.0000
## 220 0.7645 nan 0.1000 -0.0006
## 240 0.7577 nan 0.1000 -0.0006
## 260 0.7528 nan 0.1000 -0.0010
## 280 0.7473 nan 0.1000 -0.0009
## 300 0.7413 nan 0.1000 -0.0006
## 320 0.7349 nan 0.1000 -0.0005
## 340 0.7302 nan 0.1000 -0.0008
## 360 0.7245 nan 0.1000 -0.0015
## 380 0.7173 nan 0.1000 -0.0009
## 400 0.7112 nan 0.1000 -0.0006
## 420 0.7048 nan 0.1000 -0.0006
## 440 0.7016 nan 0.1000 -0.0010
## 460 0.6985 nan 0.1000 -0.0014
## 480 0.6939 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2596 nan 0.1000 0.0141
## 2 1.2318 nan 0.1000 0.0153
## 3 1.2099 nan 0.1000 0.0106
## 4 1.1839 nan 0.1000 0.0092
## 5 1.1634 nan 0.1000 0.0082
## 6 1.1478 nan 0.1000 0.0061
## 7 1.1332 nan 0.1000 0.0058
## 8 1.1187 nan 0.1000 0.0060
## 9 1.1041 nan 0.1000 0.0062
## 10 1.0918 nan 0.1000 0.0044
## 20 1.0026 nan 0.1000 0.0027
## 40 0.9202 nan 0.1000 0.0001
## 60 0.8788 nan 0.1000 -0.0007
## 80 0.8502 nan 0.1000 -0.0011
## 100 0.8298 nan 0.1000 -0.0006
## 120 0.8141 nan 0.1000 -0.0003
## 140 0.8042 nan 0.1000 -0.0006
## 160 0.7964 nan 0.1000 -0.0003
## 180 0.7881 nan 0.1000 -0.0004
## 200 0.7821 nan 0.1000 -0.0001
## 220 0.7761 nan 0.1000 -0.0010
## 240 0.7710 nan 0.1000 -0.0007
## 260 0.7645 nan 0.1000 -0.0003
## 280 0.7569 nan 0.1000 -0.0010
## 300 0.7500 nan 0.1000 -0.0005
## 320 0.7423 nan 0.1000 -0.0000
## 340 0.7366 nan 0.1000 -0.0012
## 360 0.7295 nan 0.1000 -0.0008
## 380 0.7251 nan 0.1000 -0.0008
## 400 0.7200 nan 0.1000 -0.0011
## 420 0.7164 nan 0.1000 -0.0003
## 440 0.7111 nan 0.1000 -0.0008
## 460 0.7075 nan 0.1000 -0.0005
## 480 0.7015 nan 0.1000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2503 nan 0.1000 0.0181
## 2 1.2106 nan 0.1000 0.0194
## 3 1.1796 nan 0.1000 0.0143
## 4 1.1501 nan 0.1000 0.0125
## 5 1.1273 nan 0.1000 0.0090
## 6 1.1076 nan 0.1000 0.0098
## 7 1.0853 nan 0.1000 0.0080
## 8 1.0660 nan 0.1000 0.0078
## 9 1.0483 nan 0.1000 0.0070
## 10 1.0324 nan 0.1000 0.0054
## 20 0.9372 nan 0.1000 0.0003
## 40 0.8475 nan 0.1000 -0.0006
## 60 0.7999 nan 0.1000 -0.0009
## 80 0.7624 nan 0.1000 -0.0011
## 100 0.7357 nan 0.1000 -0.0002
## 120 0.7081 nan 0.1000 -0.0002
## 140 0.6860 nan 0.1000 -0.0009
## 160 0.6660 nan 0.1000 -0.0021
## 180 0.6451 nan 0.1000 -0.0007
## 200 0.6263 nan 0.1000 -0.0005
## 220 0.6070 nan 0.1000 -0.0015
## 240 0.5912 nan 0.1000 -0.0014
## 260 0.5725 nan 0.1000 -0.0006
## 280 0.5585 nan 0.1000 -0.0004
## 300 0.5411 nan 0.1000 -0.0008
## 320 0.5297 nan 0.1000 -0.0016
## 340 0.5177 nan 0.1000 -0.0008
## 360 0.5052 nan 0.1000 -0.0015
## 380 0.4934 nan 0.1000 -0.0010
## 400 0.4827 nan 0.1000 -0.0003
## 420 0.4690 nan 0.1000 -0.0008
## 440 0.4581 nan 0.1000 -0.0006
## 460 0.4469 nan 0.1000 -0.0005
## 480 0.4369 nan 0.1000 -0.0012
## 500 0.4274 nan 0.1000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2443 nan 0.1000 0.0226
## 2 1.2091 nan 0.1000 0.0171
## 3 1.1811 nan 0.1000 0.0134
## 4 1.1521 nan 0.1000 0.0132
## 5 1.1271 nan 0.1000 0.0105
## 6 1.1031 nan 0.1000 0.0112
## 7 1.0860 nan 0.1000 0.0052
## 8 1.0704 nan 0.1000 0.0068
## 9 1.0533 nan 0.1000 0.0067
## 10 1.0418 nan 0.1000 0.0041
## 20 0.9386 nan 0.1000 0.0012
## 40 0.8486 nan 0.1000 -0.0016
## 60 0.8007 nan 0.1000 -0.0017
## 80 0.7675 nan 0.1000 -0.0007
## 100 0.7319 nan 0.1000 -0.0022
## 120 0.7111 nan 0.1000 -0.0022
## 140 0.6901 nan 0.1000 -0.0012
## 160 0.6721 nan 0.1000 -0.0022
## 180 0.6533 nan 0.1000 -0.0011
## 200 0.6330 nan 0.1000 -0.0007
## 220 0.6171 nan 0.1000 -0.0016
## 240 0.6003 nan 0.1000 -0.0009
## 260 0.5840 nan 0.1000 -0.0014
## 280 0.5657 nan 0.1000 -0.0013
## 300 0.5499 nan 0.1000 -0.0006
## 320 0.5367 nan 0.1000 -0.0011
## 340 0.5249 nan 0.1000 -0.0011
## 360 0.5110 nan 0.1000 -0.0009
## 380 0.4993 nan 0.1000 -0.0005
## 400 0.4856 nan 0.1000 -0.0006
## 420 0.4720 nan 0.1000 -0.0005
## 440 0.4629 nan 0.1000 -0.0010
## 460 0.4525 nan 0.1000 -0.0006
## 480 0.4441 nan 0.1000 -0.0015
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2477 nan 0.1000 0.0224
## 2 1.2166 nan 0.1000 0.0151
## 3 1.1802 nan 0.1000 0.0137
## 4 1.1509 nan 0.1000 0.0110
## 5 1.1266 nan 0.1000 0.0086
## 6 1.1033 nan 0.1000 0.0110
## 7 1.0815 nan 0.1000 0.0093
## 8 1.0667 nan 0.1000 0.0045
## 9 1.0498 nan 0.1000 0.0062
## 10 1.0346 nan 0.1000 0.0066
## 20 0.9378 nan 0.1000 0.0026
## 40 0.8500 nan 0.1000 -0.0010
## 60 0.8025 nan 0.1000 -0.0022
## 80 0.7691 nan 0.1000 -0.0007
## 100 0.7406 nan 0.1000 -0.0011
## 120 0.7159 nan 0.1000 -0.0012
## 140 0.6914 nan 0.1000 -0.0020
## 160 0.6717 nan 0.1000 -0.0009
## 180 0.6546 nan 0.1000 -0.0014
## 200 0.6376 nan 0.1000 -0.0013
## 220 0.6228 nan 0.1000 -0.0019
## 240 0.6084 nan 0.1000 -0.0011
## 260 0.5905 nan 0.1000 -0.0007
## 280 0.5720 nan 0.1000 -0.0008
## 300 0.5575 nan 0.1000 -0.0010
## 320 0.5448 nan 0.1000 -0.0010
## 340 0.5307 nan 0.1000 -0.0018
## 360 0.5184 nan 0.1000 -0.0016
## 380 0.5071 nan 0.1000 -0.0012
## 400 0.4953 nan 0.1000 -0.0018
## 420 0.4840 nan 0.1000 -0.0007
## 440 0.4727 nan 0.1000 -0.0010
## 460 0.4633 nan 0.1000 -0.0010
## 480 0.4562 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2436 nan 0.1000 0.0182
## 2 1.2015 nan 0.1000 0.0178
## 3 1.1620 nan 0.1000 0.0141
## 4 1.1282 nan 0.1000 0.0163
## 5 1.1027 nan 0.1000 0.0089
## 6 1.0787 nan 0.1000 0.0098
## 7 1.0554 nan 0.1000 0.0096
## 8 1.0353 nan 0.1000 0.0064
## 9 1.0140 nan 0.1000 0.0078
## 10 0.9953 nan 0.1000 0.0068
## 20 0.8915 nan 0.1000 0.0009
## 40 0.7945 nan 0.1000 -0.0020
## 60 0.7302 nan 0.1000 -0.0015
## 80 0.6883 nan 0.1000 -0.0021
## 100 0.6490 nan 0.1000 -0.0014
## 120 0.6211 nan 0.1000 -0.0022
## 140 0.5886 nan 0.1000 -0.0019
## 160 0.5555 nan 0.1000 -0.0012
## 180 0.5330 nan 0.1000 -0.0020
## 200 0.5091 nan 0.1000 -0.0005
## 220 0.4897 nan 0.1000 -0.0004
## 240 0.4701 nan 0.1000 -0.0004
## 260 0.4482 nan 0.1000 -0.0005
## 280 0.4282 nan 0.1000 -0.0006
## 300 0.4101 nan 0.1000 -0.0007
## 320 0.3939 nan 0.1000 -0.0005
## 340 0.3766 nan 0.1000 -0.0008
## 360 0.3601 nan 0.1000 -0.0007
## 380 0.3484 nan 0.1000 -0.0009
## 400 0.3357 nan 0.1000 -0.0004
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## 460 0.3015 nan 0.1000 -0.0003
## 480 0.2916 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2428 nan 0.1000 0.0240
## 2 1.1950 nan 0.1000 0.0214
## 3 1.1590 nan 0.1000 0.0157
## 4 1.1236 nan 0.1000 0.0156
## 5 1.0951 nan 0.1000 0.0099
## 6 1.0729 nan 0.1000 0.0076
## 7 1.0512 nan 0.1000 0.0071
## 8 1.0313 nan 0.1000 0.0051
## 9 1.0147 nan 0.1000 0.0056
## 10 0.9967 nan 0.1000 0.0068
## 20 0.8927 nan 0.1000 0.0017
## 40 0.8004 nan 0.1000 -0.0001
## 60 0.7435 nan 0.1000 -0.0017
## 80 0.6949 nan 0.1000 -0.0017
## 100 0.6591 nan 0.1000 -0.0008
## 120 0.6193 nan 0.1000 -0.0005
## 140 0.5863 nan 0.1000 -0.0006
## 160 0.5613 nan 0.1000 -0.0014
## 180 0.5347 nan 0.1000 -0.0000
## 200 0.5115 nan 0.1000 -0.0014
## 220 0.4878 nan 0.1000 -0.0014
## 240 0.4670 nan 0.1000 -0.0009
## 260 0.4492 nan 0.1000 -0.0006
## 280 0.4286 nan 0.1000 -0.0010
## 300 0.4096 nan 0.1000 -0.0010
## 320 0.3940 nan 0.1000 -0.0006
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## 380 0.3473 nan 0.1000 -0.0006
## 400 0.3360 nan 0.1000 -0.0014
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## 460 0.3010 nan 0.1000 -0.0010
## 480 0.2904 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2414 nan 0.1000 0.0237
## 2 1.2022 nan 0.1000 0.0161
## 3 1.1651 nan 0.1000 0.0173
## 4 1.1299 nan 0.1000 0.0154
## 5 1.1013 nan 0.1000 0.0110
## 6 1.0780 nan 0.1000 0.0094
## 7 1.0584 nan 0.1000 0.0075
## 8 1.0386 nan 0.1000 0.0079
## 9 1.0206 nan 0.1000 0.0045
## 10 1.0029 nan 0.1000 0.0048
## 20 0.9035 nan 0.1000 0.0020
## 40 0.8004 nan 0.1000 -0.0013
## 60 0.7423 nan 0.1000 -0.0016
## 80 0.6962 nan 0.1000 -0.0007
## 100 0.6555 nan 0.1000 -0.0027
## 120 0.6196 nan 0.1000 -0.0003
## 140 0.5847 nan 0.1000 -0.0005
## 160 0.5548 nan 0.1000 -0.0009
## 180 0.5234 nan 0.1000 -0.0007
## 200 0.4999 nan 0.1000 -0.0014
## 220 0.4794 nan 0.1000 -0.0006
## 240 0.4613 nan 0.1000 -0.0018
## 260 0.4433 nan 0.1000 -0.0017
## 280 0.4219 nan 0.1000 -0.0014
## 300 0.4051 nan 0.1000 -0.0014
## 320 0.3852 nan 0.1000 -0.0008
## 340 0.3674 nan 0.1000 -0.0005
## 360 0.3527 nan 0.1000 -0.0006
## 380 0.3368 nan 0.1000 -0.0009
## 400 0.3195 nan 0.1000 -0.0011
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## 460 0.2860 nan 0.1000 -0.0007
## 480 0.2731 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2231 nan 0.2000 0.0321
## 2 1.1789 nan 0.2000 0.0192
## 3 1.1476 nan 0.2000 0.0130
## 4 1.1147 nan 0.2000 0.0128
## 5 1.0821 nan 0.2000 0.0108
## 6 1.0639 nan 0.2000 0.0060
## 7 1.0465 nan 0.2000 0.0075
## 8 1.0273 nan 0.2000 0.0070
## 9 1.0144 nan 0.2000 0.0050
## 10 1.0035 nan 0.2000 0.0037
## 20 0.9206 nan 0.2000 -0.0008
## 40 0.8535 nan 0.2000 -0.0031
## 60 0.8182 nan 0.2000 -0.0007
## 80 0.7962 nan 0.2000 -0.0012
## 100 0.7804 nan 0.2000 -0.0056
## 120 0.7642 nan 0.2000 -0.0020
## 140 0.7517 nan 0.2000 -0.0033
## 160 0.7403 nan 0.2000 -0.0018
## 180 0.7309 nan 0.2000 -0.0018
## 200 0.7217 nan 0.2000 -0.0018
## 220 0.7129 nan 0.2000 -0.0007
## 240 0.7017 nan 0.2000 -0.0010
## 260 0.6940 nan 0.2000 -0.0009
## 280 0.6832 nan 0.2000 -0.0011
## 300 0.6696 nan 0.2000 -0.0006
## 320 0.6619 nan 0.2000 -0.0022
## 340 0.6533 nan 0.2000 -0.0017
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## 380 0.6413 nan 0.2000 -0.0044
## 400 0.6351 nan 0.2000 -0.0013
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## 480 0.6137 nan 0.2000 -0.0017
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2244 nan 0.2000 0.0330
## 2 1.1875 nan 0.2000 0.0136
## 3 1.1502 nan 0.2000 0.0192
## 4 1.1157 nan 0.2000 0.0151
## 5 1.0948 nan 0.2000 0.0078
## 6 1.0706 nan 0.2000 0.0085
## 7 1.0529 nan 0.2000 0.0043
## 8 1.0336 nan 0.2000 0.0103
## 9 1.0224 nan 0.2000 0.0013
## 10 1.0058 nan 0.2000 0.0067
## 20 0.9182 nan 0.2000 0.0025
## 40 0.8595 nan 0.2000 -0.0006
## 60 0.8371 nan 0.2000 -0.0048
## 80 0.8116 nan 0.2000 -0.0006
## 100 0.7875 nan 0.2000 -0.0030
## 120 0.7710 nan 0.2000 -0.0013
## 140 0.7529 nan 0.2000 -0.0028
## 160 0.7425 nan 0.2000 -0.0039
## 180 0.7312 nan 0.2000 -0.0001
## 200 0.7210 nan 0.2000 -0.0025
## 220 0.7118 nan 0.2000 -0.0008
## 240 0.7029 nan 0.2000 -0.0007
## 260 0.6938 nan 0.2000 -0.0025
## 280 0.6863 nan 0.2000 -0.0013
## 300 0.6811 nan 0.2000 -0.0009
## 320 0.6767 nan 0.2000 -0.0014
## 340 0.6697 nan 0.2000 -0.0020
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## 380 0.6565 nan 0.2000 -0.0038
## 400 0.6522 nan 0.2000 -0.0026
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## 460 0.6360 nan 0.2000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2202 nan 0.2000 0.0307
## 2 1.1777 nan 0.2000 0.0192
## 3 1.1475 nan 0.2000 0.0135
## 4 1.1157 nan 0.2000 0.0136
## 5 1.0921 nan 0.2000 0.0091
## 6 1.0711 nan 0.2000 0.0091
## 7 1.0519 nan 0.2000 0.0056
## 8 1.0320 nan 0.2000 0.0074
## 9 1.0249 nan 0.2000 -0.0004
## 10 1.0026 nan 0.2000 0.0058
## 20 0.9195 nan 0.2000 0.0031
## 40 0.8552 nan 0.2000 -0.0014
## 60 0.8257 nan 0.2000 -0.0011
## 80 0.7995 nan 0.2000 -0.0018
## 100 0.7804 nan 0.2000 -0.0022
## 120 0.7665 nan 0.2000 -0.0016
## 140 0.7546 nan 0.2000 -0.0011
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## 180 0.7292 nan 0.2000 -0.0013
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## 220 0.7094 nan 0.2000 -0.0016
## 240 0.7005 nan 0.2000 -0.0014
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## 340 0.6586 nan 0.2000 -0.0014
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## 380 0.6454 nan 0.2000 -0.0027
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## 460 0.6211 nan 0.2000 -0.0037
## 480 0.6145 nan 0.2000 -0.0015
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2117 nan 0.2000 0.0323
## 2 1.1524 nan 0.2000 0.0222
## 3 1.1053 nan 0.2000 0.0224
## 4 1.0589 nan 0.2000 0.0178
## 5 1.0290 nan 0.2000 0.0069
## 6 1.0058 nan 0.2000 0.0077
## 7 0.9780 nan 0.2000 0.0092
## 8 0.9642 nan 0.2000 -0.0000
## 9 0.9452 nan 0.2000 0.0041
## 10 0.9323 nan 0.2000 0.0005
## 20 0.8536 nan 0.2000 0.0019
## 40 0.7761 nan 0.2000 -0.0020
## 60 0.7201 nan 0.2000 -0.0040
## 80 0.6813 nan 0.2000 -0.0040
## 100 0.6434 nan 0.2000 -0.0022
## 120 0.6094 nan 0.2000 -0.0020
## 140 0.5819 nan 0.2000 -0.0039
## 160 0.5462 nan 0.2000 -0.0015
## 180 0.5171 nan 0.2000 -0.0037
## 200 0.4943 nan 0.2000 -0.0010
## 220 0.4715 nan 0.2000 -0.0040
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## 460 0.2798 nan 0.2000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2036 nan 0.2000 0.0434
## 2 1.1468 nan 0.2000 0.0227
## 3 1.0990 nan 0.2000 0.0193
## 4 1.0647 nan 0.2000 0.0132
## 5 1.0295 nan 0.2000 0.0132
## 6 1.0025 nan 0.2000 0.0082
## 7 0.9797 nan 0.2000 0.0094
## 8 0.9635 nan 0.2000 0.0081
## 9 0.9482 nan 0.2000 0.0053
## 10 0.9403 nan 0.2000 -0.0013
## 20 0.8458 nan 0.2000 -0.0005
## 40 0.7759 nan 0.2000 -0.0028
## 60 0.7235 nan 0.2000 -0.0083
## 80 0.6753 nan 0.2000 -0.0029
## 100 0.6356 nan 0.2000 -0.0023
## 120 0.6034 nan 0.2000 -0.0009
## 140 0.5726 nan 0.2000 -0.0035
## 160 0.5430 nan 0.2000 -0.0015
## 180 0.5178 nan 0.2000 -0.0015
## 200 0.4943 nan 0.2000 -0.0027
## 220 0.4647 nan 0.2000 -0.0017
## 240 0.4467 nan 0.2000 -0.0015
## 260 0.4300 nan 0.2000 -0.0009
## 280 0.4146 nan 0.2000 -0.0044
## 300 0.3933 nan 0.2000 -0.0008
## 320 0.3751 nan 0.2000 -0.0018
## 340 0.3557 nan 0.2000 -0.0005
## 360 0.3413 nan 0.2000 -0.0008
## 380 0.3268 nan 0.2000 -0.0017
## 400 0.3171 nan 0.2000 -0.0017
## 420 0.3061 nan 0.2000 -0.0004
## 440 0.2932 nan 0.2000 -0.0014
## 460 0.2837 nan 0.2000 -0.0012
## 480 0.2729 nan 0.2000 -0.0015
## 500 0.2618 nan 0.2000 -0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2009 nan 0.2000 0.0409
## 2 1.1381 nan 0.2000 0.0247
## 3 1.0947 nan 0.2000 0.0170
## 4 1.0574 nan 0.2000 0.0181
## 5 1.0292 nan 0.2000 0.0130
## 6 1.0021 nan 0.2000 0.0047
## 7 0.9824 nan 0.2000 0.0057
## 8 0.9623 nan 0.2000 0.0060
## 9 0.9432 nan 0.2000 0.0037
## 10 0.9285 nan 0.2000 0.0031
## 20 0.8455 nan 0.2000 -0.0023
## 40 0.7656 nan 0.2000 -0.0028
## 60 0.7189 nan 0.2000 -0.0033
## 80 0.6771 nan 0.2000 -0.0009
## 100 0.6379 nan 0.2000 -0.0012
## 120 0.6110 nan 0.2000 -0.0013
## 140 0.5860 nan 0.2000 -0.0021
## 160 0.5582 nan 0.2000 -0.0055
## 180 0.5335 nan 0.2000 -0.0046
## 200 0.5092 nan 0.2000 -0.0018
## 220 0.4915 nan 0.2000 -0.0017
## 240 0.4682 nan 0.2000 -0.0013
## 260 0.4429 nan 0.2000 -0.0015
## 280 0.4214 nan 0.2000 -0.0017
## 300 0.4024 nan 0.2000 -0.0016
## 320 0.3842 nan 0.2000 -0.0011
## 340 0.3720 nan 0.2000 -0.0005
## 360 0.3600 nan 0.2000 -0.0021
## 380 0.3458 nan 0.2000 -0.0024
## 400 0.3336 nan 0.2000 -0.0012
## 420 0.3206 nan 0.2000 -0.0016
## 440 0.3097 nan 0.2000 -0.0020
## 460 0.3006 nan 0.2000 -0.0009
## 480 0.2890 nan 0.2000 -0.0010
## 500 0.2763 nan 0.2000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2033 nan 0.2000 0.0379
## 2 1.1364 nan 0.2000 0.0286
## 3 1.0921 nan 0.2000 0.0157
## 4 1.0468 nan 0.2000 0.0141
## 5 1.0227 nan 0.2000 0.0026
## 6 0.9955 nan 0.2000 0.0047
## 7 0.9726 nan 0.2000 0.0033
## 8 0.9523 nan 0.2000 0.0055
## 9 0.9362 nan 0.2000 0.0022
## 10 0.9163 nan 0.2000 0.0053
## 20 0.8069 nan 0.2000 -0.0018
## 40 0.7127 nan 0.2000 -0.0024
## 60 0.6299 nan 0.2000 -0.0000
## 80 0.5783 nan 0.2000 -0.0016
## 100 0.5229 nan 0.2000 -0.0019
## 120 0.4786 nan 0.2000 -0.0011
## 140 0.4350 nan 0.2000 -0.0044
## 160 0.3948 nan 0.2000 -0.0019
## 180 0.3620 nan 0.2000 -0.0032
## 200 0.3344 nan 0.2000 -0.0011
## 220 0.3112 nan 0.2000 -0.0016
## 240 0.2930 nan 0.2000 -0.0010
## 260 0.2709 nan 0.2000 -0.0009
## 280 0.2543 nan 0.2000 -0.0020
## 300 0.2376 nan 0.2000 -0.0011
## 320 0.2220 nan 0.2000 -0.0016
## 340 0.2073 nan 0.2000 -0.0005
## 360 0.1908 nan 0.2000 -0.0010
## 380 0.1814 nan 0.2000 -0.0004
## 400 0.1701 nan 0.2000 -0.0002
## 420 0.1589 nan 0.2000 -0.0002
## 440 0.1484 nan 0.2000 -0.0006
## 460 0.1399 nan 0.2000 -0.0002
## 480 0.1338 nan 0.2000 -0.0009
## 500 0.1264 nan 0.2000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2041 nan 0.2000 0.0287
## 2 1.1281 nan 0.2000 0.0316
## 3 1.0802 nan 0.2000 0.0116
## 4 1.0378 nan 0.2000 0.0153
## 5 1.0011 nan 0.2000 0.0079
## 6 0.9719 nan 0.2000 0.0090
## 7 0.9601 nan 0.2000 -0.0063
## 8 0.9380 nan 0.2000 0.0040
## 9 0.9252 nan 0.2000 -0.0010
## 10 0.9122 nan 0.2000 -0.0000
## 20 0.8162 nan 0.2000 -0.0009
## 40 0.7026 nan 0.2000 -0.0016
## 60 0.6411 nan 0.2000 -0.0027
## 80 0.5877 nan 0.2000 -0.0020
## 100 0.5356 nan 0.2000 -0.0007
## 120 0.4883 nan 0.2000 -0.0025
## 140 0.4536 nan 0.2000 -0.0023
## 160 0.4267 nan 0.2000 -0.0008
## 180 0.3880 nan 0.2000 -0.0008
## 200 0.3556 nan 0.2000 -0.0009
## 220 0.3263 nan 0.2000 -0.0019
## 240 0.3012 nan 0.2000 -0.0019
## 260 0.2810 nan 0.2000 -0.0009
## 280 0.2617 nan 0.2000 -0.0006
## 300 0.2443 nan 0.2000 -0.0023
## 320 0.2258 nan 0.2000 -0.0004
## 340 0.2142 nan 0.2000 -0.0013
## 360 0.1997 nan 0.2000 -0.0015
## 380 0.1851 nan 0.2000 -0.0005
## 400 0.1740 nan 0.2000 -0.0005
## 420 0.1642 nan 0.2000 -0.0009
## 440 0.1532 nan 0.2000 -0.0007
## 460 0.1439 nan 0.2000 -0.0004
## 480 0.1355 nan 0.2000 -0.0008
## 500 0.1295 nan 0.2000 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2034 nan 0.2000 0.0395
## 2 1.1215 nan 0.2000 0.0379
## 3 1.0763 nan 0.2000 0.0164
## 4 1.0385 nan 0.2000 0.0134
## 5 1.0096 nan 0.2000 0.0125
## 6 0.9886 nan 0.2000 0.0031
## 7 0.9668 nan 0.2000 0.0069
## 8 0.9472 nan 0.2000 0.0066
## 9 0.9262 nan 0.2000 0.0063
## 10 0.9148 nan 0.2000 -0.0010
## 20 0.8111 nan 0.2000 -0.0016
## 40 0.7064 nan 0.2000 -0.0016
## 60 0.6415 nan 0.2000 -0.0078
## 80 0.5790 nan 0.2000 -0.0042
## 100 0.5307 nan 0.2000 -0.0001
## 120 0.4912 nan 0.2000 -0.0026
## 140 0.4541 nan 0.2000 -0.0003
## 160 0.4210 nan 0.2000 -0.0038
## 180 0.3898 nan 0.2000 -0.0021
## 200 0.3573 nan 0.2000 -0.0020
## 220 0.3268 nan 0.2000 -0.0023
## 240 0.3052 nan 0.2000 -0.0028
## 260 0.2830 nan 0.2000 -0.0014
## 280 0.2591 nan 0.2000 -0.0008
## 300 0.2414 nan 0.2000 -0.0005
## 320 0.2216 nan 0.2000 -0.0007
## 340 0.2037 nan 0.2000 -0.0012
## 360 0.1882 nan 0.2000 -0.0022
## 380 0.1774 nan 0.2000 -0.0009
## 400 0.1680 nan 0.2000 -0.0013
## 420 0.1622 nan 0.2000 -0.0024
## 440 0.1513 nan 0.2000 -0.0011
## 460 0.1416 nan 0.2000 -0.0007
## 480 0.1334 nan 0.2000 -0.0001
## 500 0.1262 nan 0.2000 -0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2063 nan 0.3000 0.0481
## 2 1.1493 nan 0.3000 0.0244
## 3 1.1092 nan 0.3000 0.0151
## 4 1.0795 nan 0.3000 0.0122
## 5 1.0415 nan 0.3000 0.0146
## 6 1.0224 nan 0.3000 0.0082
## 7 0.9993 nan 0.3000 0.0091
## 8 0.9781 nan 0.3000 0.0047
## 9 0.9674 nan 0.3000 0.0052
## 10 0.9534 nan 0.3000 -0.0001
## 20 0.9021 nan 0.3000 -0.0056
## 40 0.8341 nan 0.3000 -0.0004
## 60 0.8015 nan 0.3000 -0.0032
## 80 0.7663 nan 0.3000 -0.0047
## 100 0.7444 nan 0.3000 -0.0019
## 120 0.7305 nan 0.3000 0.0000
## 140 0.7157 nan 0.3000 -0.0033
## 160 0.7050 nan 0.3000 -0.0036
## 180 0.6953 nan 0.3000 -0.0029
## 200 0.6842 nan 0.3000 -0.0035
## 220 0.6723 nan 0.3000 -0.0055
## 240 0.6573 nan 0.3000 -0.0002
## 260 0.6482 nan 0.3000 -0.0046
## 280 0.6368 nan 0.3000 -0.0039
## 300 0.6287 nan 0.3000 -0.0030
## 320 0.6221 nan 0.3000 -0.0024
## 340 0.6104 nan 0.3000 -0.0017
## 360 0.6039 nan 0.3000 -0.0016
## 380 0.6036 nan 0.3000 -0.0034
## 400 0.5912 nan 0.3000 -0.0035
## 420 0.5845 nan 0.3000 -0.0027
## 440 0.5759 nan 0.3000 -0.0015
## 460 0.5716 nan 0.3000 -0.0011
## 480 0.5637 nan 0.3000 -0.0011
## 500 0.5561 nan 0.3000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2008 nan 0.3000 0.0443
## 2 1.1491 nan 0.3000 0.0218
## 3 1.1078 nan 0.3000 0.0145
## 4 1.0715 nan 0.3000 0.0187
## 5 1.0362 nan 0.3000 0.0151
## 6 1.0146 nan 0.3000 0.0036
## 7 0.9946 nan 0.3000 0.0068
## 8 0.9833 nan 0.3000 0.0014
## 9 0.9648 nan 0.3000 0.0034
## 10 0.9547 nan 0.3000 0.0007
## 20 0.8822 nan 0.3000 -0.0011
## 40 0.8323 nan 0.3000 -0.0041
## 60 0.7947 nan 0.3000 -0.0066
## 80 0.7781 nan 0.3000 -0.0022
## 100 0.7588 nan 0.3000 -0.0021
## 120 0.7398 nan 0.3000 -0.0013
## 140 0.7262 nan 0.3000 0.0001
## 160 0.7183 nan 0.3000 -0.0028
## 180 0.7047 nan 0.3000 -0.0037
## 200 0.6948 nan 0.3000 -0.0018
## 220 0.6831 nan 0.3000 -0.0029
## 240 0.6743 nan 0.3000 -0.0049
## 260 0.6594 nan 0.3000 -0.0024
## 280 0.6529 nan 0.3000 -0.0032
## 300 0.6432 nan 0.3000 -0.0003
## 320 0.6381 nan 0.3000 -0.0016
## 340 0.6239 nan 0.3000 -0.0019
## 360 0.6138 nan 0.3000 -0.0011
## 380 0.6086 nan 0.3000 -0.0002
## 400 0.6005 nan 0.3000 -0.0039
## 420 0.5904 nan 0.3000 -0.0008
## 440 0.5833 nan 0.3000 -0.0015
## 460 0.5781 nan 0.3000 -0.0025
## 480 0.5731 nan 0.3000 -0.0033
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1953 nan 0.3000 0.0459
## 2 1.1417 nan 0.3000 0.0236
## 3 1.1007 nan 0.3000 0.0116
## 4 1.0628 nan 0.3000 0.0173
## 5 1.0406 nan 0.3000 0.0080
## 6 1.0056 nan 0.3000 0.0122
## 7 0.9879 nan 0.3000 0.0086
## 8 0.9753 nan 0.3000 0.0035
## 9 0.9602 nan 0.3000 0.0050
## 10 0.9492 nan 0.3000 0.0015
## 20 0.8863 nan 0.3000 -0.0027
## 40 0.8295 nan 0.3000 -0.0037
## 60 0.7898 nan 0.3000 -0.0005
## 80 0.7656 nan 0.3000 0.0011
## 100 0.7529 nan 0.3000 -0.0018
## 120 0.7347 nan 0.3000 -0.0064
## 140 0.7212 nan 0.3000 -0.0021
## 160 0.7079 nan 0.3000 -0.0041
## 180 0.6926 nan 0.3000 -0.0035
## 200 0.6864 nan 0.3000 -0.0024
## 220 0.6738 nan 0.3000 -0.0028
## 240 0.6597 nan 0.3000 -0.0035
## 260 0.6543 nan 0.3000 -0.0020
## 280 0.6435 nan 0.3000 0.0000
## 300 0.6374 nan 0.3000 -0.0023
## 320 0.6259 nan 0.3000 -0.0016
## 340 0.6209 nan 0.3000 -0.0019
## 360 0.6097 nan 0.3000 -0.0018
## 380 0.5973 nan 0.3000 -0.0034
## 400 0.5918 nan 0.3000 -0.0006
## 420 0.5862 nan 0.3000 -0.0018
## 440 0.5784 nan 0.3000 -0.0032
## 460 0.5707 nan 0.3000 -0.0039
## 480 0.5668 nan 0.3000 -0.0032
## 500 0.5599 nan 0.3000 -0.0034
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1702 nan 0.3000 0.0459
## 2 1.1001 nan 0.3000 0.0356
## 3 1.0564 nan 0.3000 0.0170
## 4 1.0093 nan 0.3000 0.0223
## 5 0.9822 nan 0.3000 0.0064
## 6 0.9582 nan 0.3000 0.0059
## 7 0.9360 nan 0.3000 0.0037
## 8 0.9168 nan 0.3000 0.0033
## 9 0.9076 nan 0.3000 0.0006
## 10 0.8928 nan 0.3000 0.0018
## 20 0.8197 nan 0.3000 -0.0059
## 40 0.7423 nan 0.3000 -0.0029
## 60 0.6721 nan 0.3000 -0.0066
## 80 0.6215 nan 0.3000 -0.0053
## 100 0.5580 nan 0.3000 -0.0053
## 120 0.5282 nan 0.3000 -0.0036
## 140 0.5004 nan 0.3000 -0.0020
## 160 0.4593 nan 0.3000 -0.0001
## 180 0.4325 nan 0.3000 -0.0087
## 200 0.6840 nan 0.3000 -0.0018
## 220 0.6643 nan 0.3000 -0.0024
## 240 0.6332 nan 0.3000 -0.0007
## 260 0.6228 nan 0.3000 -0.0039
## 280 0.6098 nan 0.3000 -0.0005
## 300 0.5870 nan 0.3000 -0.0013
## 320 0.5811 nan 0.3000 -0.0015
## 340 0.5692 nan 0.3000 -0.0055
## 360 0.5554 nan 0.3000 -0.0039
## 380 0.5480 nan 0.3000 -0.0020
## 400 0.5363 nan 0.3000 -0.0015
## 420 0.5192 nan 0.3000 -0.0028
## 440 0.5096 nan 0.3000 -0.0027
## 460 0.5013 nan 0.3000 -0.0008
## 480 0.4951 nan 0.3000 -0.0021
## 500 0.4870 nan 0.3000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1682 nan 0.3000 0.0562
## 2 1.1065 nan 0.3000 0.0233
## 3 1.0521 nan 0.3000 0.0212
## 4 1.0131 nan 0.3000 0.0125
## 5 0.9809 nan 0.3000 0.0101
## 6 0.9593 nan 0.3000 0.0058
## 7 0.9360 nan 0.3000 0.0033
## 8 0.9255 nan 0.3000 -0.0031
## 9 0.9056 nan 0.3000 0.0060
## 10 0.8957 nan 0.3000 -0.0058
## 20 0.7905 nan 0.3000 0.0011
## 40 0.7280 nan 0.3000 -0.0034
## 60 0.6800 nan 0.3000 -0.0051
## 80 0.6295 nan 0.3000 -0.0057
## 100 0.5786 nan 0.3000 -0.0006
## 120 0.5372 nan 0.3000 -0.0031
## 140 0.5094 nan 0.3000 -0.0079
## 160 0.4804 nan 0.3000 -0.0038
## 180 0.4438 nan 0.3000 -0.0022
## 200 0.4105 nan 0.3000 -0.0013
## 220 0.3847 nan 0.3000 -0.0032
## 240 0.3583 nan 0.3000 -0.0028
## 260 0.3344 nan 0.3000 -0.0015
## 280 0.3128 nan 0.3000 -0.0026
## 300 inf nan 0.3000 nan
## 320 inf nan 0.3000 nan
## 340 inf nan 0.3000 nan
## 360 inf nan 0.3000 nan
## 380 inf nan 0.3000 -0.0018
## 400 inf nan 0.3000 nan
## 420 inf nan 0.3000 nan
## 440 inf nan 0.3000 nan
## 460 inf nan 0.3000 nan
## 480 inf nan 0.3000 nan
## 500 inf nan 0.3000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1695 nan 0.3000 0.0575
## 2 1.1086 nan 0.3000 0.0226
## 3 1.0545 nan 0.3000 0.0216
## 4 1.0098 nan 0.3000 0.0201
## 5 0.9813 nan 0.3000 0.0059
## 6 0.9569 nan 0.3000 0.0069
## 7 0.9322 nan 0.3000 0.0044
## 8 0.9160 nan 0.3000 0.0006
## 9 0.9049 nan 0.3000 -0.0017
## 10 0.8885 nan 0.3000 -0.0039
## 20 0.8181 nan 0.3000 -0.0012
## 40 0.7395 nan 0.3000 -0.0038
## 60 0.6851 nan 0.3000 -0.0016
## 80 0.6338 nan 0.3000 -0.0021
## 100 0.5891 nan 0.3000 -0.0043
## 120 0.5512 nan 0.3000 -0.0036
## 140 0.5185 nan 0.3000 -0.0033
## 160 0.4880 nan 0.3000 -0.0037
## 180 0.4612 nan 0.3000 -0.0055
## 200 0.4319 nan 0.3000 -0.0033
## 220 0.4022 nan 0.3000 -0.0049
## 240 0.3745 nan 0.3000 -0.0019
## 260 0.3529 nan 0.3000 -0.0018
## 280 0.3342 nan 0.3000 -0.0015
## 300 0.3195 nan 0.3000 -0.0035
## 320 0.2985 nan 0.3000 -0.0010
## 340 0.2827 nan 0.3000 -0.0021
## 360 0.2694 nan 0.3000 -0.0016
## 380 0.2569 nan 0.3000 -0.0009
## 400 0.2426 nan 0.3000 -0.0022
## 420 0.2284 nan 0.3000 -0.0022
## 440 0.2146 nan 0.3000 -0.0020
## 460 0.2024 nan 0.3000 -0.0003
## 480 0.1915 nan 0.3000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1559 nan 0.3000 0.0665
## 2 1.0737 nan 0.3000 0.0385
## 3 1.0187 nan 0.3000 0.0236
## 4 0.9729 nan 0.3000 0.0122
## 5 0.9391 nan 0.3000 0.0088
## 6 0.9203 nan 0.3000 -0.0052
## 7 0.8928 nan 0.3000 0.0018
## 8 0.8735 nan 0.3000 -0.0009
## 9 0.8620 nan 0.3000 -0.0013
## 10 0.8426 nan 0.3000 0.0033
## 20 0.7722 nan 0.3000 -0.0031
## 40 inf nan 0.3000 nan
## 60 inf nan 0.3000 nan
## 80 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0030
## 100 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0024
## 120 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0011
## 140 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0023
## 160 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0035
## 180 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0024
## 200 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0037
## 220 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0002
## 240 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0008
## 260 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0018
## 280 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0014
## 300 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0008
## 320 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0024
## 340 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0015
## 360 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0007
## 380 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0007
## 400 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0002
## 420 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0007
## 440 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0013
## 460 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0004
## 480 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0011
## 500 4816822942941066000002440464682008040686240088440240862.0000 nan 0.3000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1527 nan 0.3000 0.0669
## 2 1.0563 nan 0.3000 0.0371
## 3 0.9993 nan 0.3000 0.0221
## 4 0.9707 nan 0.3000 -0.0000
## 5 0.9407 nan 0.3000 0.0099
## 6 0.9152 nan 0.3000 0.0076
## 7 0.8895 nan 0.3000 0.0014
## 8 0.8713 nan 0.3000 0.0018
## 9 0.8612 nan 0.3000 -0.0025
## 10 0.8498 nan 0.3000 -0.0055
## 20 0.7553 nan 0.3000 -0.0040
## 40 0.6434 nan 0.3000 -0.0093
## 60 0.5650 nan 0.3000 -0.0100
## 80 0.5068 nan 0.3000 -0.0039
## 100 0.4433 nan 0.3000 -0.0036
## 120 0.3994 nan 0.3000 -0.0025
## 140 0.3446 nan 0.3000 -0.0012
## 160 0.3122 nan 0.3000 -0.0012
## 180 0.2752 nan 0.3000 -0.0029
## 200 0.2447 nan 0.3000 -0.0017
## 220 0.2216 nan 0.3000 -0.0017
## 240 0.1984 nan 0.3000 -0.0008
## 260 0.1772 nan 0.3000 -0.0003
## 280 0.1627 nan 0.3000 -0.0011
## 300 0.1471 nan 0.3000 -0.0009
## 320 0.1322 nan 0.3000 -0.0008
## 340 0.1219 nan 0.3000 -0.0002
## 360 0.1145 nan 0.3000 -0.0014
## 380 0.1054 nan 0.3000 -0.0004
## 400 0.0960 nan 0.3000 -0.0008
## 420 0.0897 nan 0.3000 -0.0009
## 440 0.0836 nan 0.3000 -0.0007
## 460 0.0767 nan 0.3000 -0.0003
## 480 0.0702 nan 0.3000 -0.0003
## 500 0.0670 nan 0.3000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1545 nan 0.3000 0.0628
## 2 1.0765 nan 0.3000 0.0345
## 3 1.0043 nan 0.3000 0.0278
## 4 0.9761 nan 0.3000 0.0041
## 5 0.9471 nan 0.3000 0.0062
## 6 0.9278 nan 0.3000 0.0004
## 7 0.9006 nan 0.3000 0.0091
## 8 0.8775 nan 0.3000 0.0006
## 9 0.8640 nan 0.3000 -0.0010
## 10 0.8591 nan 0.3000 -0.0074
## 20 0.7613 nan 0.3000 -0.0032
## 40 0.6386 nan 0.3000 -0.0027
## 60 0.5517 nan 0.3000 -0.0010
## 80 0.4966 nan 0.3000 -0.0052
## 100 0.4354 nan 0.3000 -0.0033
## 120 0.3775 nan 0.3000 -0.0049
## 140 0.3253 nan 0.3000 -0.0008
## 160 0.2912 nan 0.3000 -0.0025
## 180 0.2616 nan 0.3000 -0.0019
## 200 0.2351 nan 0.3000 -0.0034
## 220 0.2122 nan 0.3000 -0.0025
## 240 0.1922 nan 0.3000 -0.0006
## 260 0.1777 nan 0.3000 -0.0022
## 280 0.1601 nan 0.3000 -0.0025
## 300 0.1479 nan 0.3000 -0.0013
## 320 0.1335 nan 0.3000 -0.0006
## 340 0.1198 nan 0.3000 -0.0001
## 360 0.1100 nan 0.3000 -0.0004
## 380 0.1009 nan 0.3000 -0.0002
## 400 0.0931 nan 0.3000 -0.0003
## 420 0.0836 nan 0.3000 -0.0012
## 440 0.0790 nan 0.3000 -0.0010
## 460 0.0731 nan 0.3000 -0.0005
## 480 0.0673 nan 0.3000 -0.0007
## 500 0.0608 nan 0.3000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1685 nan 0.5000 0.0586
## 2 1.1013 nan 0.5000 0.0292
## 3 1.0514 nan 0.5000 0.0207
## 4 1.0219 nan 0.5000 0.0034
## 5 0.9802 nan 0.5000 0.0148
## 6 0.9691 nan 0.5000 -0.0051
## 7 0.9530 nan 0.5000 0.0039
## 8 0.9374 nan 0.5000 0.0033
## 9 0.9170 nan 0.5000 0.0056
## 10 0.9015 nan 0.5000 0.0065
## 20 0.8587 nan 0.5000 -0.0010
## 40 0.7924 nan 0.5000 -0.0015
## 60 0.7469 nan 0.5000 -0.0017
## 80 0.7202 nan 0.5000 -0.0050
## 100 0.6915 nan 0.5000 -0.0020
## 120 0.6673 nan 0.5000 -0.0034
## 140 0.6556 nan 0.5000 -0.0070
## 160 0.6415 nan 0.5000 -0.0040
## 180 0.6229 nan 0.5000 -0.0015
## 200 0.6142 nan 0.5000 -0.0097
## 220 0.5981 nan 0.5000 -0.0087
## 240 0.5995 nan 0.5000 -0.0105
## 260 0.5664 nan 0.5000 -0.0014
## 280 0.5663 nan 0.5000 -0.0062
## 300 0.5556 nan 0.5000 -0.0041
## 320 0.5468 nan 0.5000 -0.0002
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1535 nan 0.5000 0.0695
## 2 1.0903 nan 0.5000 0.0214
## 3 1.0447 nan 0.5000 0.0195
## 4 0.9972 nan 0.5000 0.0224
## 5 0.9672 nan 0.5000 0.0030
## 6 0.9504 nan 0.5000 0.0031
## 7 0.9350 nan 0.5000 0.0046
## 8 0.9221 nan 0.5000 -0.0082
## 9 0.9129 nan 0.5000 -0.0045
## 10 0.9092 nan 0.5000 -0.0053
## 20 0.8465 nan 0.5000 -0.0033
## 40 0.8011 nan 0.5000 -0.0049
## 60 0.7727 nan 0.5000 -0.0081
## 80 0.7406 nan 0.5000 -0.0136
## 100 0.7169 nan 0.5000 -0.0042
## 120 0.6977 nan 0.5000 -0.0059
## 140 0.6824 nan 0.5000 -0.0012
## 160 0.6705 nan 0.5000 -0.0096
## 180 0.6432 nan 0.5000 -0.0024
## 200 0.6287 nan 0.5000 -0.0039
## 220 0.6167 nan 0.5000 -0.0020
## 240 0.6053 nan 0.5000 -0.0038
## 260 0.5863 nan 0.5000 -0.0061
## 280 0.5792 nan 0.5000 -0.0045
## 300 0.5761 nan 0.5000 -0.0038
## 320 0.5657 nan 0.5000 -0.0042
## 340 0.5544 nan 0.5000 -0.0024
## 360 0.5433 nan 0.5000 -0.0036
## 380 0.5381 nan 0.5000 -0.0062
## 400 0.5247 nan 0.5000 -0.0012
## 420 0.5220 nan 0.5000 -0.0026
## 440 0.5232 nan 0.5000 -0.0071
## 460 0.5109 nan 0.5000 -0.0004
## 480 0.4983 nan 0.5000 -0.0006
## 500 0.4939 nan 0.5000 -0.0010
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1453 nan 0.5000 0.0617
## 2 1.1116 nan 0.5000 -0.0024
## 3 1.0490 nan 0.5000 0.0182
## 4 1.0056 nan 0.5000 0.0118
## 5 0.9864 nan 0.5000 0.0029
## 6 0.9604 nan 0.5000 0.0108
## 7 0.9544 nan 0.5000 -0.0044
## 8 0.9487 nan 0.5000 -0.0077
## 9 0.9231 nan 0.5000 0.0122
## 10 0.9124 nan 0.5000 -0.0002
## 20 0.8637 nan 0.5000 -0.0013
## 40 0.8117 nan 0.5000 -0.0040
## 60 0.7697 nan 0.5000 -0.0055
## 80 0.7436 nan 0.5000 -0.0109
## 100 0.7406 nan 0.5000 -0.0049
## 120 0.7231 nan 0.5000 -0.0078
## 140 0.6965 nan 0.5000 -0.0039
## 160 0.6760 nan 0.5000 -0.0003
## 180 0.6604 nan 0.5000 -0.0012
## 200 0.6509 nan 0.5000 -0.0003
## 220 0.6342 nan 0.5000 -0.0061
## 240 0.6219 nan 0.5000 -0.0020
## 260 0.5997 nan 0.5000 -0.0004
## 280 0.5988 nan 0.5000 -0.0016
## 300 0.5833 nan 0.5000 -0.0025
## 320 0.5847 nan 0.5000 -0.0106
## 340 0.5699 nan 0.5000 -0.0033
## 360 0.5600 nan 0.5000 -0.0003
## 380 0.5489 nan 0.5000 -0.0035
## 400 0.5418 nan 0.5000 -0.0047
## 420 0.5359 nan 0.5000 -0.0024
## 440 0.5247 nan 0.5000 -0.0059
## 460 0.5136 nan 0.5000 -0.0025
## 480 0.5042 nan 0.5000 -0.0036
## 500 0.4951 nan 0.5000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1251 nan 0.5000 0.0783
## 2 1.0449 nan 0.5000 0.0302
## 3 0.9814 nan 0.5000 0.0244
## 4 0.9390 nan 0.5000 0.0193
## 5 0.9148 nan 0.5000 0.0018
## 6 0.9037 nan 0.5000 -0.0015
## 7 0.8959 nan 0.5000 -0.0095
## 8 0.8813 nan 0.5000 -0.0053
## 9 0.8630 nan 0.5000 -0.0041
## 10 0.8348 nan 0.5000 0.0096
## 20 0.7297 nan 0.5000 -0.0064
## 40 0.6648 nan 0.5000 -0.0113
## 60 0.6727 nan 0.5000 -0.0127
## 80 0.5453 nan 0.5000 -0.0024
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1198 nan 0.5000 0.0825
## 2 1.0378 nan 0.5000 0.0395
## 3 0.9808 nan 0.5000 0.0226
## 4 0.9438 nan 0.5000 0.0108
## 5 0.9164 nan 0.5000 0.0041
## 6 0.9028 nan 0.5000 -0.0057
## 7 0.8893 nan 0.5000 -0.0048
## 8 0.8834 nan 0.5000 -0.0227
## 9 0.8725 nan 0.5000 -0.0105
## 10 0.8619 nan 0.5000 -0.0090
## 20 0.7783 nan 0.5000 -0.0062
## 40 0.6738 nan 0.5000 -0.0053
## 60 0.5990 nan 0.5000 -0.0072
## 80 0.5377 nan 0.5000 -0.0038
## 100 0.4969 nan 0.5000 -0.0032
## 120 0.4541 nan 0.5000 -0.0119
## 140 0.3931 nan 0.5000 -0.0094
## 160 0.3573 nan 0.5000 -0.0019
## 180 0.3250 nan 0.5000 -0.0036
## 200 0.2877 nan 0.5000 0.0002
## 220 0.2640 nan 0.5000 -0.0022
## 240 0.2448 nan 0.5000 -0.0014
## 260 0.2218 nan 0.5000 -0.0041
## 280 0.2040 nan 0.5000 -0.0014
## 300 0.1861 nan 0.5000 -0.0008
## 320 0.1741 nan 0.5000 -0.0017
## 340 0.1582 nan 0.5000 -0.0014
## 360 0.1437 nan 0.5000 -0.0025
## 380 0.1329 nan 0.5000 -0.0031
## 400 0.1212 nan 0.5000 -0.0023
## 420 0.1103 nan 0.5000 -0.0008
## 440 0.1038 nan 0.5000 -0.0013
## 460 0.0955 nan 0.5000 -0.0022
## 480 0.0889 nan 0.5000 -0.0006
## 500 0.0837 nan 0.5000 -0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1158 nan 0.5000 0.0931
## 2 1.0521 nan 0.5000 0.0265
## 3 0.9876 nan 0.5000 0.0150
## 4 0.9563 nan 0.5000 0.0003
## 5 0.9407 nan 0.5000 -0.0085
## 6 0.9100 nan 0.5000 0.0037
## 7 0.9003 nan 0.5000 -0.0041
## 8 0.8881 nan 0.5000 -0.0019
## 9 0.8840 nan 0.5000 -0.0120
## 10 0.8701 nan 0.5000 -0.0052
## 20 0.7958 nan 0.5000 -0.0167
## 40 0.6896 nan 0.5000 -0.0112
## 60 0.6251 nan 0.5000 -0.0108
## 80 0.5655 nan 0.5000 -0.0068
## 100 0.5229 nan 0.5000 0.0009
## 120 0.4739 nan 0.5000 -0.0047
## 140 0.4396 nan 0.5000 -0.0080
## 160 0.3999 nan 0.5000 -0.0004
## 180 0.3573 nan 0.5000 -0.0014
## 200 0.3242 nan 0.5000 -0.0074
## 220 0.2951 nan 0.5000 -0.0039
## 240 0.2779 nan 0.5000 -0.0001
## 260 0.2420 nan 0.5000 -0.0021
## 280 0.2145 nan 0.5000 -0.0025
## 300 0.1998 nan 0.5000 -0.0046
## 320 0.1767 nan 0.5000 -0.0009
## 340 0.1599 nan 0.5000 -0.0003
## 360 0.1532 nan 0.5000 -0.0033
## 380 0.1375 nan 0.5000 -0.0002
## 400 0.1255 nan 0.5000 -0.0012
## 420 0.1178 nan 0.5000 -0.0013
## 440 0.1079 nan 0.5000 -0.0019
## 460 0.0970 nan 0.5000 -0.0005
## 480 0.0887 nan 0.5000 -0.0013
## 500 0.0831 nan 0.5000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1063 nan 0.5000 0.0818
## 2 1.0261 nan 0.5000 0.0160
## 3 0.9550 nan 0.5000 0.0152
## 4 0.9226 nan 0.5000 0.0022
## 5 0.8906 nan 0.5000 0.0032
## 6 0.8669 nan 0.5000 0.0030
## 7 0.8668 nan 0.5000 -0.0278
## 8 0.8581 nan 0.5000 -0.0188
## 9 0.8440 nan 0.5000 -0.0116
## 10 0.8131 nan 0.5000 0.0016
## 20 0.7325 nan 0.5000 -0.0186
## 40 0.6022 nan 0.5000 -0.0044
## 60 0.5319 nan 0.5000 -0.0086
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
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## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0730 nan 0.5000 0.0819
## 2 1.0010 nan 0.5000 0.0111
## 3 0.9121 nan 0.5000 0.0181
## 4 0.9038 nan 0.5000 -0.0168
## 5 0.8745 nan 0.5000 0.0028
## 6 0.8527 nan 0.5000 0.0051
## 7 0.8357 nan 0.5000 0.0009
## 8 0.8261 nan 0.5000 -0.0123
## 9 0.8199 nan 0.5000 -0.0097
## 10 0.7976 nan 0.5000 -0.0044
## 20 0.7292 nan 0.5000 -0.0078
## 40 0.5870 nan 0.5000 -0.0134
## 60 407344394661.0875 nan 0.5000 -0.0019
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
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## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0957 nan 0.5000 0.0813
## 2 1.0052 nan 0.5000 0.0161
## 3 0.9406 nan 0.5000 0.0216
## 4 0.9176 nan 0.5000 -0.0001
## 5 0.9071 nan 0.5000 -0.0173
## 6 0.8870 nan 0.5000 -0.0131
## 7 0.8680 nan 0.5000 -0.0086
## 8 0.8591 nan 0.5000 -0.0112
## 9 0.8365 nan 0.5000 -0.0010
## 10 0.8185 nan 0.5000 -0.0058
## 20 0.7311 nan 0.5000 -0.0060
## 40 0.5910 nan 0.5000 -0.0026
## 60 0.4811 nan 0.5000 -0.0044
## 80 0.4069 nan 0.5000 -0.0090
## 100 0.3285 nan 0.5000 -0.0055
## 120 0.2737 nan 0.5000 -0.0065
## 140 0.2249 nan 0.5000 -0.0030
## 160 0.1841 nan 0.5000 -0.0039
## 180 0.1596 nan 0.5000 -0.0042
## 200 0.1354 nan 0.5000 -0.0028
## 220 0.1167 nan 0.5000 -0.0006
## 240 0.0997 nan 0.5000 -0.0009
## 260 0.0854 nan 0.5000 -0.0006
## 280 0.0755 nan 0.5000 -0.0021
## 300 0.0674 nan 0.5000 -0.0009
## 320 0.0573 nan 0.5000 -0.0001
## 340 0.0513 nan 0.5000 -0.0011
## 360 0.0450 nan 0.5000 -0.0006
## 380 0.0389 nan 0.5000 -0.0005
## 400 0.0341 nan 0.5000 -0.0003
## 420 0.0314 nan 0.5000 -0.0006
## 440 0.0278 nan 0.5000 -0.0009
## 460 0.0245 nan 0.5000 -0.0003
## 480 0.0222 nan 0.5000 -0.0005
## 500 0.0195 nan 0.5000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1334 nan 1.0000 0.0694
## 2 1.0983 nan 1.0000 -0.0032
## 3 1.0335 nan 1.0000 0.0267
## 4 0.9905 nan 1.0000 0.0194
## 5 0.9739 nan 1.0000 0.0055
## 6 0.9778 nan 1.0000 -0.0223
## 7 0.9728 nan 1.0000 -0.0223
## 8 0.9668 nan 1.0000 -0.0236
## 9 0.9607 nan 1.0000 -0.0088
## 10 0.9697 nan 1.0000 -0.0207
## 20 1.1315 nan 1.0000 -0.0161
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 -0.0078
## 100 inf nan 1.0000 -0.0010
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 -0.0099
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1450 nan 1.0000 0.0443
## 2 1.0776 nan 1.0000 0.0132
## 3 1.0358 nan 1.0000 -0.0026
## 4 1.0009 nan 1.0000 -0.0092
## 5 0.9812 nan 1.0000 0.0016
## 6 0.9841 nan 1.0000 -0.0162
## 7 1.0140 nan 1.0000 -0.0470
## 8 1.0198 nan 1.0000 -0.0162
## 9 1.0405 nan 1.0000 -0.0376
## 10 1.0207 nan 1.0000 0.0065
## 20 1.2843 nan 1.0000 0.0034
## 40 1.2386 nan 1.0000 -0.0366
## 60 1.2134 nan 1.0000 0.0157
## 80 1461871472425.3103 nan 1.0000 0.0006
## 100 1461871472425.2849 nan 1.0000 0.0009
## 120 1461871857369.6980 nan 1.0000 -0.0008
## 140 1461871857369.6956 nan 1.0000 -0.0001
## 160 1461871857369.6995 nan 1.0000 0.0002
## 180 1461871857369.7068 nan 1.0000 -0.0087
## 200 1461871857369.6909 nan 1.0000 -0.0042
## 220 1461871857369.6875 nan 1.0000 -0.0007
## 240 1461871857369.6899 nan 1.0000 0.0001
## 260 1461871857369.6860 nan 1.0000 -0.0000
## 280 1461871857369.6870 nan 1.0000 0.0023
## 300 1461871857369.6865 nan 1.0000 -0.0001
## 320 1461871857369.6882 nan 1.0000 -0.0008
## 340 1461871857369.6843 nan 1.0000 0.0006
## 360 1461871857369.6821 nan 1.0000 -0.0027
## 380 1461871857369.6833 nan 1.0000 -0.0024
## 400 1461871857369.6848 nan 1.0000 0.0013
## 420 1461871857369.6821 nan 1.0000 -0.0003
## 440 1461871857369.6785 nan 1.0000 -0.0029
## 460 1461871857369.6755 nan 1.0000 -0.0003
## 480 1461871857369.6714 nan 1.0000 0.0003
## 500 1461871857369.6658 nan 1.0000 -0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1122 nan 1.0000 0.0965
## 2 1.0409 nan 1.0000 0.0181
## 3 0.9839 nan 1.0000 0.0204
## 4 0.9768 nan 1.0000 -0.0202
## 5 0.9702 nan 1.0000 -0.0192
## 6 0.9793 nan 1.0000 -0.0311
## 7 0.9585 nan 1.0000 0.0041
## 8 0.9474 nan 1.0000 -0.0115
## 9 0.9553 nan 1.0000 -0.0287
## 10 0.9529 nan 1.0000 -0.0205
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 -0.0197
## 100 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 -0.0179
## 120 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 -0.0225
## 140 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 -0.0048
## 160 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 -0.0159
## 180 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 -0.0089
## 200 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 0.0064
## 220 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 0.0030
## 240 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 0.0058
## 260 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 0.0001
## 280 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 -0.0129
## 300 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 0.0000
## 320 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 0.0004
## 340 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 0.0090
## 360 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 0.0001
## 380 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 -0.0004
## 400 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 -0.0019
## 420 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 0.0003
## 440 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 -0.0000
## 460 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 0.0002
## 480 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 -0.0027
## 500 39232177900660099028224660086400662640224482660824886024048442888.0000 nan 1.0000 0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0640 nan 1.0000 0.0995
## 2 0.9910 nan 1.0000 0.0168
## 3 0.9302 nan 1.0000 -0.0018
## 4 0.9233 nan 1.0000 -0.0115
## 5 0.9080 nan 1.0000 -0.0105
## 6 0.8849 nan 1.0000 0.0011
## 7 0.8734 nan 1.0000 -0.0147
## 8 0.8647 nan 1.0000 -0.0135
## 9 0.8968 nan 1.0000 -0.0521
## 10 0.9287 nan 1.0000 -0.0615
## 20 0.8730 nan 1.0000 -0.0140
## 40 inf nan 1.0000 -0.0308
## 60 inf nan 1.0000 -0.0082
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0792 nan 1.0000 0.0563
## 2 1.0098 nan 1.0000 0.0120
## 3 0.9799 nan 1.0000 -0.0146
## 4 0.9649 nan 1.0000 -0.0053
## 5 0.9487 nan 1.0000 -0.0135
## 6 0.9541 nan 1.0000 -0.0283
## 7 0.9684 nan 1.0000 -0.0386
## 8 0.9858 nan 1.0000 -0.0628
## 9 0.9399 nan 1.0000 -0.0016
## 10 0.9121 nan 1.0000 0.0019
## 20 0.8301 nan 1.0000 -0.0104
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
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## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0742 nan 1.0000 0.1102
## 2 1.0083 nan 1.0000 0.0042
## 3 0.9814 nan 1.0000 -0.0137
## 4 0.9587 nan 1.0000 -0.0263
## 5 0.9616 nan 1.0000 -0.0317
## 6 0.9704 nan 1.0000 -0.0461
## 7 1.1103 nan 1.0000 -0.0187
## 8 1.1248 nan 1.0000 -0.0453
## 9 1.1136 nan 1.0000 -0.0254
## 10 1.1055 nan 1.0000 -0.0370
## 20 1.1120 nan 1.0000 -0.0423
## 40 0.9533 nan 1.0000 -0.0261
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0254 nan 1.0000 0.1011
## 2 0.9255 nan 1.0000 0.0283
## 3 0.9190 nan 1.0000 -0.0486
## 4 0.9279 nan 1.0000 -0.0443
## 5 0.9323 nan 1.0000 -0.0600
## 6 0.9159 nan 1.0000 -0.0221
## 7 0.9258 nan 1.0000 -0.0479
## 8 0.9476 nan 1.0000 -0.0780
## 9 0.9338 nan 1.0000 -0.0446
## 10 0.8995 nan 1.0000 0.0067
## 20 1.9946 nan 1.0000 -0.0029
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0721 nan 1.0000 0.0396
## 2 0.9659 nan 1.0000 0.0381
## 3 0.9408 nan 1.0000 -0.0235
## 4 0.9142 nan 1.0000 -0.0159
## 5 0.9361 nan 1.0000 -0.0532
## 6 0.9285 nan 1.0000 -0.0312
## 7 0.9241 nan 1.0000 -0.0287
## 8 0.9093 nan 1.0000 -0.0339
## 9 0.9195 nan 1.0000 -0.0623
## 10 0.8832 nan 1.0000 -0.0292
## 20 4.0569 nan 1.0000 -0.0439
## 40 inf nan 1.0000 5.9278
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0244 nan 1.0000 0.1165
## 2 0.9273 nan 1.0000 0.0384
## 3 0.9195 nan 1.0000 -0.0247
## 4 0.9001 nan 1.0000 -0.0195
## 5 0.8857 nan 1.0000 -0.0086
## 6 0.8847 nan 1.0000 -0.0405
## 7 0.8517 nan 1.0000 0.0059
## 8 0.8445 nan 1.0000 -0.0235
## 9 0.8167 nan 1.0000 0.0022
## 10 0.8069 nan 1.0000 -0.0286
## 20 0.8208 nan 1.0000 -0.0995
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 280 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
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## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
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## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
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## 240 1.2934 nan 0.0000 0.0000
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## 280 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
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## 280 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
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## 240 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0001
## 6 1.2912 nan 0.0010 0.0001
## 7 1.2909 nan 0.0010 0.0002
## 8 1.2905 nan 0.0010 0.0002
## 9 1.2902 nan 0.0010 0.0002
## 10 1.2899 nan 0.0010 0.0002
## 20 1.2864 nan 0.0010 0.0002
## 40 1.2798 nan 0.0010 0.0001
## 60 1.2734 nan 0.0010 0.0001
## 80 1.2672 nan 0.0010 0.0001
## 100 1.2614 nan 0.0010 0.0001
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## 140 1.2499 nan 0.0010 0.0001
## 160 1.2445 nan 0.0010 0.0001
## 180 1.2391 nan 0.0010 0.0001
## 200 1.2339 nan 0.0010 0.0001
## 220 1.2288 nan 0.0010 0.0001
## 240 1.2238 nan 0.0010 0.0001
## 260 1.2191 nan 0.0010 0.0001
## 280 1.2145 nan 0.0010 0.0001
## 300 1.2101 nan 0.0010 0.0001
## 320 1.2056 nan 0.0010 0.0001
## 340 1.2015 nan 0.0010 0.0001
## 360 1.1975 nan 0.0010 0.0001
## 380 1.1936 nan 0.0010 0.0001
## 400 1.1898 nan 0.0010 0.0001
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## 460 1.1787 nan 0.0010 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2927 nan 0.0010 0.0002
## 3 1.2924 nan 0.0010 0.0001
## 4 1.2920 nan 0.0010 0.0001
## 5 1.2917 nan 0.0010 0.0002
## 6 1.2914 nan 0.0010 0.0002
## 7 1.2910 nan 0.0010 0.0001
## 8 1.2906 nan 0.0010 0.0002
## 9 1.2903 nan 0.0010 0.0002
## 10 1.2899 nan 0.0010 0.0001
## 20 1.2866 nan 0.0010 0.0001
## 40 1.2801 nan 0.0010 0.0001
## 60 1.2738 nan 0.0010 0.0001
## 80 1.2675 nan 0.0010 0.0001
## 100 1.2616 nan 0.0010 0.0001
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## 140 1.2501 nan 0.0010 0.0001
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## 180 1.2395 nan 0.0010 0.0001
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## 260 1.2197 nan 0.0010 0.0001
## 280 1.2151 nan 0.0010 0.0001
## 300 1.2105 nan 0.0010 0.0001
## 320 1.2062 nan 0.0010 0.0001
## 340 1.2019 nan 0.0010 0.0001
## 360 1.1979 nan 0.0010 0.0001
## 380 1.1939 nan 0.0010 0.0001
## 400 1.1900 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2927 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2913 nan 0.0010 0.0002
## 7 1.2909 nan 0.0010 0.0002
## 8 1.2906 nan 0.0010 0.0002
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## 10 1.2898 nan 0.0010 0.0002
## 20 1.2863 nan 0.0010 0.0001
## 40 1.2795 nan 0.0010 0.0001
## 60 1.2731 nan 0.0010 0.0001
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## 260 1.2190 nan 0.0010 0.0001
## 280 1.2146 nan 0.0010 0.0001
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## 320 1.2058 nan 0.0010 0.0001
## 340 1.2015 nan 0.0010 0.0001
## 360 1.1976 nan 0.0010 0.0001
## 380 1.1936 nan 0.0010 0.0001
## 400 1.1896 nan 0.0010 0.0001
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## 460 1.1785 nan 0.0010 0.0001
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## 500 1.1714 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2906 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
## 9 1.2892 nan 0.0010 0.0002
## 10 1.2888 nan 0.0010 0.0002
## 20 1.2842 nan 0.0010 0.0002
## 40 1.2754 nan 0.0010 0.0002
## 60 1.2668 nan 0.0010 0.0002
## 80 1.2586 nan 0.0010 0.0002
## 100 1.2507 nan 0.0010 0.0002
## 120 1.2429 nan 0.0010 0.0002
## 140 1.2353 nan 0.0010 0.0002
## 160 1.2281 nan 0.0010 0.0002
## 180 1.2211 nan 0.0010 0.0002
## 200 1.2143 nan 0.0010 0.0002
## 220 1.2076 nan 0.0010 0.0001
## 240 1.2010 nan 0.0010 0.0002
## 260 1.1948 nan 0.0010 0.0001
## 280 1.1888 nan 0.0010 0.0001
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## 320 1.1768 nan 0.0010 0.0001
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## 380 1.1604 nan 0.0010 0.0001
## 400 1.1554 nan 0.0010 0.0001
## 420 1.1502 nan 0.0010 0.0001
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## 480 1.1358 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2911 nan 0.0010 0.0002
## 6 1.2906 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
## 9 1.2892 nan 0.0010 0.0002
## 10 1.2887 nan 0.0010 0.0002
## 20 1.2843 nan 0.0010 0.0002
## 40 1.2755 nan 0.0010 0.0001
## 60 1.2671 nan 0.0010 0.0002
## 80 1.2589 nan 0.0010 0.0002
## 100 1.2510 nan 0.0010 0.0002
## 120 1.2430 nan 0.0010 0.0002
## 140 1.2355 nan 0.0010 0.0002
## 160 1.2284 nan 0.0010 0.0002
## 180 1.2215 nan 0.0010 0.0002
## 200 1.2145 nan 0.0010 0.0002
## 220 1.2078 nan 0.0010 0.0001
## 240 1.2013 nan 0.0010 0.0001
## 260 1.1951 nan 0.0010 0.0001
## 280 1.1890 nan 0.0010 0.0001
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## 320 1.1773 nan 0.0010 0.0001
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## 380 1.1610 nan 0.0010 0.0001
## 400 1.1557 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2921 nan 0.0010 0.0002
## 4 1.2916 nan 0.0010 0.0002
## 5 1.2911 nan 0.0010 0.0002
## 6 1.2907 nan 0.0010 0.0002
## 7 1.2902 nan 0.0010 0.0002
## 8 1.2897 nan 0.0010 0.0002
## 9 1.2893 nan 0.0010 0.0002
## 10 1.2889 nan 0.0010 0.0002
## 20 1.2844 nan 0.0010 0.0002
## 40 1.2757 nan 0.0010 0.0002
## 60 1.2675 nan 0.0010 0.0002
## 80 1.2591 nan 0.0010 0.0002
## 100 1.2511 nan 0.0010 0.0002
## 120 1.2431 nan 0.0010 0.0002
## 140 1.2356 nan 0.0010 0.0002
## 160 1.2284 nan 0.0010 0.0001
## 180 1.2214 nan 0.0010 0.0002
## 200 1.2148 nan 0.0010 0.0002
## 220 1.2080 nan 0.0010 0.0002
## 240 1.2015 nan 0.0010 0.0001
## 260 1.1951 nan 0.0010 0.0001
## 280 1.1889 nan 0.0010 0.0001
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## 320 1.1770 nan 0.0010 0.0001
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## 360 1.1662 nan 0.0010 0.0001
## 380 1.1608 nan 0.0010 0.0001
## 400 1.1555 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2907 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0003
## 8 1.2892 nan 0.0010 0.0002
## 9 1.2886 nan 0.0010 0.0003
## 10 1.2880 nan 0.0010 0.0002
## 20 1.2829 nan 0.0010 0.0001
## 40 1.2729 nan 0.0010 0.0002
## 60 1.2629 nan 0.0010 0.0002
## 80 1.2532 nan 0.0010 0.0002
## 100 1.2441 nan 0.0010 0.0002
## 120 1.2353 nan 0.0010 0.0002
## 140 1.2266 nan 0.0010 0.0002
## 160 1.2181 nan 0.0010 0.0002
## 180 1.2099 nan 0.0010 0.0002
## 200 1.2019 nan 0.0010 0.0002
## 220 1.1943 nan 0.0010 0.0001
## 240 1.1869 nan 0.0010 0.0002
## 260 1.1797 nan 0.0010 0.0002
## 280 1.1725 nan 0.0010 0.0001
## 300 1.1658 nan 0.0010 0.0002
## 320 1.1592 nan 0.0010 0.0001
## 340 1.1526 nan 0.0010 0.0001
## 360 1.1462 nan 0.0010 0.0001
## 380 1.1401 nan 0.0010 0.0001
## 400 1.1343 nan 0.0010 0.0001
## 420 1.1286 nan 0.0010 0.0001
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## 460 1.1173 nan 0.0010 0.0001
## 480 1.1120 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0003
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2913 nan 0.0010 0.0002
## 5 1.2908 nan 0.0010 0.0002
## 6 1.2903 nan 0.0010 0.0002
## 7 1.2898 nan 0.0010 0.0003
## 8 1.2892 nan 0.0010 0.0003
## 9 1.2887 nan 0.0010 0.0002
## 10 1.2882 nan 0.0010 0.0002
## 20 1.2828 nan 0.0010 0.0002
## 40 1.2727 nan 0.0010 0.0002
## 60 1.2627 nan 0.0010 0.0002
## 80 1.2530 nan 0.0010 0.0002
## 100 1.2438 nan 0.0010 0.0002
## 120 1.2347 nan 0.0010 0.0002
## 140 1.2262 nan 0.0010 0.0002
## 160 1.2180 nan 0.0010 0.0001
## 180 1.2099 nan 0.0010 0.0002
## 200 1.2023 nan 0.0010 0.0001
## 220 1.1946 nan 0.0010 0.0002
## 240 1.1872 nan 0.0010 0.0002
## 260 1.1799 nan 0.0010 0.0001
## 280 1.1727 nan 0.0010 0.0002
## 300 1.1658 nan 0.0010 0.0001
## 320 1.1592 nan 0.0010 0.0001
## 340 1.1529 nan 0.0010 0.0001
## 360 1.1465 nan 0.0010 0.0001
## 380 1.1404 nan 0.0010 0.0001
## 400 1.1342 nan 0.0010 0.0001
## 420 1.1285 nan 0.0010 0.0001
## 440 1.1229 nan 0.0010 0.0001
## 460 1.1174 nan 0.0010 0.0001
## 480 1.1119 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0003
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2908 nan 0.0010 0.0002
## 6 1.2902 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0002
## 8 1.2891 nan 0.0010 0.0002
## 9 1.2885 nan 0.0010 0.0003
## 10 1.2880 nan 0.0010 0.0002
## 20 1.2830 nan 0.0010 0.0002
## 40 1.2728 nan 0.0010 0.0002
## 60 1.2630 nan 0.0010 0.0002
## 80 1.2534 nan 0.0010 0.0002
## 100 1.2440 nan 0.0010 0.0002
## 120 1.2353 nan 0.0010 0.0002
## 140 1.2265 nan 0.0010 0.0001
## 160 1.2183 nan 0.0010 0.0002
## 180 1.2100 nan 0.0010 0.0001
## 200 1.2020 nan 0.0010 0.0002
## 220 1.1942 nan 0.0010 0.0002
## 240 1.1867 nan 0.0010 0.0002
## 260 1.1791 nan 0.0010 0.0001
## 280 1.1720 nan 0.0010 0.0001
## 300 1.1652 nan 0.0010 0.0001
## 320 1.1587 nan 0.0010 0.0001
## 340 1.1521 nan 0.0010 0.0001
## 360 1.1459 nan 0.0010 0.0001
## 380 1.1399 nan 0.0010 0.0001
## 400 1.1338 nan 0.0010 0.0001
## 420 1.1278 nan 0.0010 0.0001
## 440 1.1221 nan 0.0010 0.0001
## 460 1.1167 nan 0.0010 0.0001
## 480 1.1113 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2587 nan 0.1000 0.0155
## 2 1.2313 nan 0.1000 0.0104
## 3 1.2089 nan 0.1000 0.0109
## 4 1.1884 nan 0.1000 0.0073
## 5 1.1695 nan 0.1000 0.0068
## 6 1.1532 nan 0.1000 0.0063
## 7 1.1373 nan 0.1000 0.0049
## 8 1.1221 nan 0.1000 0.0065
## 9 1.1088 nan 0.1000 0.0052
## 10 1.1023 nan 0.1000 0.0010
## 20 1.0138 nan 0.1000 0.0038
## 40 0.9352 nan 0.1000 -0.0011
## 60 0.8999 nan 0.1000 0.0001
## 80 0.8724 nan 0.1000 -0.0014
## 100 0.8548 nan 0.1000 -0.0000
## 120 0.8360 nan 0.1000 -0.0003
## 140 0.8219 nan 0.1000 -0.0009
## 160 0.8109 nan 0.1000 -0.0004
## 180 0.8019 nan 0.1000 -0.0004
## 200 0.7928 nan 0.1000 -0.0007
## 220 0.7863 nan 0.1000 -0.0007
## 240 0.7812 nan 0.1000 -0.0011
## 260 0.7727 nan 0.1000 -0.0015
## 280 0.7639 nan 0.1000 -0.0005
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## 320 0.7537 nan 0.1000 -0.0007
## 340 0.7474 nan 0.1000 -0.0008
## 360 0.7411 nan 0.1000 -0.0007
## 380 0.7341 nan 0.1000 -0.0013
## 400 0.7299 nan 0.1000 -0.0009
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## 460 0.7169 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2641 nan 0.1000 0.0150
## 2 1.2386 nan 0.1000 0.0128
## 3 1.2159 nan 0.1000 0.0112
## 4 1.1930 nan 0.1000 0.0085
## 5 1.1748 nan 0.1000 0.0079
## 6 1.1571 nan 0.1000 0.0062
## 7 1.1421 nan 0.1000 0.0052
## 8 1.1267 nan 0.1000 0.0055
## 9 1.1135 nan 0.1000 0.0046
## 10 1.1006 nan 0.1000 0.0036
## 20 1.0145 nan 0.1000 0.0022
## 40 0.9355 nan 0.1000 0.0013
## 60 0.8985 nan 0.1000 -0.0011
## 80 0.8692 nan 0.1000 -0.0005
## 100 0.8478 nan 0.1000 -0.0015
## 120 0.8316 nan 0.1000 -0.0012
## 140 0.8210 nan 0.1000 -0.0015
## 160 0.8106 nan 0.1000 -0.0010
## 180 0.8014 nan 0.1000 -0.0015
## 200 0.7928 nan 0.1000 -0.0008
## 220 0.7852 nan 0.1000 -0.0009
## 240 0.7790 nan 0.1000 -0.0003
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## 280 0.7597 nan 0.1000 -0.0009
## 300 0.7531 nan 0.1000 -0.0007
## 320 0.7464 nan 0.1000 -0.0017
## 340 0.7409 nan 0.1000 -0.0007
## 360 0.7364 nan 0.1000 -0.0006
## 380 0.7330 nan 0.1000 -0.0006
## 400 0.7275 nan 0.1000 -0.0004
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## 460 0.7152 nan 0.1000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2599 nan 0.1000 0.0147
## 2 1.2345 nan 0.1000 0.0127
## 3 1.2113 nan 0.1000 0.0119
## 4 1.1895 nan 0.1000 0.0090
## 5 1.1699 nan 0.1000 0.0089
## 6 1.1546 nan 0.1000 0.0063
## 7 1.1385 nan 0.1000 0.0054
## 8 1.1241 nan 0.1000 0.0063
## 9 1.1127 nan 0.1000 0.0043
## 10 1.0980 nan 0.1000 0.0058
## 20 1.0086 nan 0.1000 0.0028
## 40 0.9299 nan 0.1000 0.0004
## 60 0.8936 nan 0.1000 -0.0017
## 80 0.8670 nan 0.1000 -0.0001
## 100 0.8499 nan 0.1000 -0.0016
## 120 0.8403 nan 0.1000 -0.0006
## 140 0.8282 nan 0.1000 -0.0011
## 160 0.8174 nan 0.1000 -0.0014
## 180 0.8065 nan 0.1000 -0.0007
## 200 0.7966 nan 0.1000 -0.0004
## 220 0.7891 nan 0.1000 -0.0000
## 240 0.7837 nan 0.1000 -0.0014
## 260 0.7776 nan 0.1000 -0.0005
## 280 0.7716 nan 0.1000 -0.0003
## 300 0.7645 nan 0.1000 -0.0010
## 320 0.7591 nan 0.1000 -0.0008
## 340 0.7517 nan 0.1000 -0.0007
## 360 0.7457 nan 0.1000 -0.0017
## 380 0.7419 nan 0.1000 -0.0012
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## 460 0.7191 nan 0.1000 -0.0010
## 480 0.7131 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2483 nan 0.1000 0.0210
## 2 1.2079 nan 0.1000 0.0171
## 3 1.1735 nan 0.1000 0.0137
## 4 1.1435 nan 0.1000 0.0116
## 5 1.1245 nan 0.1000 0.0062
## 6 1.1040 nan 0.1000 0.0069
## 7 1.0859 nan 0.1000 0.0069
## 8 1.0657 nan 0.1000 0.0070
## 9 1.0498 nan 0.1000 0.0073
## 10 1.0344 nan 0.1000 0.0052
## 20 0.9466 nan 0.1000 0.0006
## 40 0.8623 nan 0.1000 -0.0008
## 60 0.8167 nan 0.1000 0.0004
## 80 0.7832 nan 0.1000 0.0002
## 100 0.7563 nan 0.1000 -0.0018
## 120 0.7318 nan 0.1000 -0.0019
## 140 0.7009 nan 0.1000 -0.0014
## 160 0.6808 nan 0.1000 -0.0019
## 180 0.6581 nan 0.1000 -0.0008
## 200 0.6405 nan 0.1000 -0.0006
## 220 0.6195 nan 0.1000 -0.0005
## 240 0.6024 nan 0.1000 -0.0008
## 260 0.5874 nan 0.1000 -0.0008
## 280 0.5745 nan 0.1000 -0.0015
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## 320 0.5395 nan 0.1000 -0.0007
## 340 0.5262 nan 0.1000 -0.0011
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## 380 0.5001 nan 0.1000 -0.0006
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## 480 0.4422 nan 0.1000 -0.0016
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2496 nan 0.1000 0.0221
## 2 1.2066 nan 0.1000 0.0167
## 3 1.1719 nan 0.1000 0.0142
## 4 1.1446 nan 0.1000 0.0117
## 5 1.1207 nan 0.1000 0.0107
## 6 1.0999 nan 0.1000 0.0073
## 7 1.0792 nan 0.1000 0.0078
## 8 1.0629 nan 0.1000 0.0034
## 9 1.0451 nan 0.1000 0.0054
## 10 1.0303 nan 0.1000 0.0037
## 20 0.9415 nan 0.1000 0.0017
## 40 0.8555 nan 0.1000 -0.0011
## 60 0.8011 nan 0.1000 -0.0004
## 80 0.7689 nan 0.1000 -0.0009
## 100 0.7428 nan 0.1000 -0.0003
## 120 0.7191 nan 0.1000 -0.0000
## 140 0.6961 nan 0.1000 -0.0008
## 160 0.6728 nan 0.1000 -0.0011
## 180 0.6536 nan 0.1000 -0.0006
## 200 0.6345 nan 0.1000 -0.0010
## 220 0.6164 nan 0.1000 -0.0004
## 240 0.5978 nan 0.1000 -0.0011
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## 280 0.5690 nan 0.1000 -0.0009
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## 320 0.5464 nan 0.1000 -0.0010
## 340 0.5327 nan 0.1000 -0.0006
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## 380 0.5069 nan 0.1000 -0.0008
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## 460 0.4629 nan 0.1000 -0.0009
## 480 0.4526 nan 0.1000 -0.0014
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2530 nan 0.1000 0.0176
## 2 1.2134 nan 0.1000 0.0157
## 3 1.1724 nan 0.1000 0.0136
## 4 1.1441 nan 0.1000 0.0105
## 5 1.1192 nan 0.1000 0.0099
## 6 1.0977 nan 0.1000 0.0095
## 7 1.0824 nan 0.1000 0.0059
## 8 1.0634 nan 0.1000 0.0051
## 9 1.0495 nan 0.1000 0.0050
## 10 1.0366 nan 0.1000 0.0042
## 20 0.9408 nan 0.1000 0.0020
## 40 0.8643 nan 0.1000 -0.0011
## 60 0.8207 nan 0.1000 0.0002
## 80 0.7821 nan 0.1000 -0.0007
## 100 0.7551 nan 0.1000 -0.0013
## 120 0.7274 nan 0.1000 0.0009
## 140 0.7083 nan 0.1000 -0.0021
## 160 0.6853 nan 0.1000 -0.0007
## 180 0.6709 nan 0.1000 -0.0024
## 200 0.6519 nan 0.1000 -0.0003
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## 240 0.6173 nan 0.1000 -0.0009
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## 280 0.5875 nan 0.1000 -0.0010
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## 320 0.5636 nan 0.1000 -0.0002
## 340 0.5485 nan 0.1000 -0.0016
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## 380 0.5244 nan 0.1000 -0.0013
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2438 nan 0.1000 0.0204
## 2 1.2009 nan 0.1000 0.0174
## 3 1.1630 nan 0.1000 0.0152
## 4 1.1259 nan 0.1000 0.0171
## 5 1.0981 nan 0.1000 0.0095
## 6 1.0754 nan 0.1000 0.0069
## 7 1.0557 nan 0.1000 0.0080
## 8 1.0413 nan 0.1000 0.0029
## 9 1.0228 nan 0.1000 0.0075
## 10 1.0098 nan 0.1000 0.0042
## 20 0.8996 nan 0.1000 0.0014
## 40 0.8041 nan 0.1000 0.0004
## 60 0.7460 nan 0.1000 -0.0010
## 80 0.7057 nan 0.1000 -0.0014
## 100 0.6645 nan 0.1000 -0.0022
## 120 0.6283 nan 0.1000 -0.0013
## 140 0.5975 nan 0.1000 -0.0006
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## 180 0.5434 nan 0.1000 -0.0013
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## 320 0.4049 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2459 nan 0.1000 0.0182
## 2 1.1952 nan 0.1000 0.0210
## 3 1.1596 nan 0.1000 0.0147
## 4 1.1262 nan 0.1000 0.0159
## 5 1.1021 nan 0.1000 0.0095
## 6 1.0797 nan 0.1000 0.0071
## 7 1.0575 nan 0.1000 0.0074
## 8 1.0404 nan 0.1000 0.0048
## 9 1.0238 nan 0.1000 0.0045
## 10 1.0104 nan 0.1000 0.0045
## 20 0.9042 nan 0.1000 0.0020
## 40 0.8058 nan 0.1000 -0.0013
## 60 0.7465 nan 0.1000 -0.0017
## 80 0.7066 nan 0.1000 -0.0016
## 100 0.6663 nan 0.1000 -0.0021
## 120 0.6302 nan 0.1000 -0.0011
## 140 0.6018 nan 0.1000 -0.0008
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## 180 0.5432 nan 0.1000 -0.0014
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## 240 0.4709 nan 0.1000 -0.0010
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## 280 0.4283 nan 0.1000 -0.0006
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## 320 0.3899 nan 0.1000 -0.0014
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## 380 0.3447 nan 0.1000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2427 nan 0.1000 0.0247
## 2 1.2042 nan 0.1000 0.0163
## 3 1.1739 nan 0.1000 0.0114
## 4 1.1415 nan 0.1000 0.0148
## 5 1.1111 nan 0.1000 0.0144
## 6 1.0812 nan 0.1000 0.0133
## 7 1.0620 nan 0.1000 0.0055
## 8 1.0405 nan 0.1000 0.0083
## 9 1.0256 nan 0.1000 0.0056
## 10 1.0099 nan 0.1000 0.0046
## 20 0.9013 nan 0.1000 0.0017
## 40 0.8004 nan 0.1000 -0.0013
## 60 0.7427 nan 0.1000 -0.0010
## 80 0.7036 nan 0.1000 -0.0022
## 100 0.6688 nan 0.1000 -0.0021
## 120 0.6396 nan 0.1000 -0.0034
## 140 0.6038 nan 0.1000 -0.0003
## 160 0.5798 nan 0.1000 -0.0019
## 180 0.5511 nan 0.1000 -0.0009
## 200 0.5283 nan 0.1000 -0.0008
## 220 0.5020 nan 0.1000 -0.0014
## 240 0.4780 nan 0.1000 -0.0010
## 260 0.4545 nan 0.1000 -0.0012
## 280 0.4367 nan 0.1000 -0.0016
## 300 0.4180 nan 0.1000 -0.0007
## 320 0.4020 nan 0.1000 -0.0010
## 340 0.3846 nan 0.1000 -0.0008
## 360 0.3688 nan 0.1000 -0.0014
## 380 0.3559 nan 0.1000 -0.0004
## 400 0.3420 nan 0.1000 -0.0007
## 420 0.3275 nan 0.1000 -0.0009
## 440 0.3179 nan 0.1000 -0.0010
## 460 0.3060 nan 0.1000 -0.0007
## 480 0.2932 nan 0.1000 -0.0017
## 500 0.2822 nan 0.1000 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2331 nan 0.2000 0.0272
## 2 1.1816 nan 0.2000 0.0219
## 3 1.1533 nan 0.2000 0.0123
## 4 1.1239 nan 0.2000 0.0129
## 5 1.0993 nan 0.2000 0.0122
## 6 1.0730 nan 0.2000 0.0060
## 7 1.0565 nan 0.2000 0.0050
## 8 1.0391 nan 0.2000 0.0066
## 9 1.0248 nan 0.2000 0.0061
## 10 1.0150 nan 0.2000 0.0018
## 20 0.9436 nan 0.2000 0.0012
## 40 0.8751 nan 0.2000 -0.0006
## 60 0.8395 nan 0.2000 -0.0023
## 80 0.8151 nan 0.2000 -0.0026
## 100 0.7949 nan 0.2000 -0.0022
## 120 0.7779 nan 0.2000 -0.0000
## 140 0.7655 nan 0.2000 -0.0027
## 160 0.7511 nan 0.2000 -0.0025
## 180 0.7401 nan 0.2000 -0.0024
## 200 0.7342 nan 0.2000 -0.0040
## 220 0.7226 nan 0.2000 -0.0024
## 240 0.7130 nan 0.2000 -0.0017
## 260 0.7061 nan 0.2000 -0.0023
## 280 0.7024 nan 0.2000 -0.0011
## 300 0.6901 nan 0.2000 -0.0019
## 320 0.6815 nan 0.2000 -0.0035
## 340 0.6758 nan 0.2000 -0.0020
## 360 0.6718 nan 0.2000 -0.0028
## 380 0.6620 nan 0.2000 -0.0026
## 400 0.6571 nan 0.2000 -0.0004
## 420 0.6525 nan 0.2000 -0.0024
## 440 0.6502 nan 0.2000 -0.0024
## 460 0.6418 nan 0.2000 -0.0032
## 480 0.6351 nan 0.2000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2281 nan 0.2000 0.0275
## 2 1.1896 nan 0.2000 0.0213
## 3 1.1568 nan 0.2000 0.0136
## 4 1.1269 nan 0.2000 0.0134
## 5 1.1017 nan 0.2000 0.0108
## 6 1.0805 nan 0.2000 0.0060
## 7 1.0560 nan 0.2000 0.0111
## 8 1.0426 nan 0.2000 0.0028
## 9 1.0333 nan 0.2000 0.0017
## 10 1.0185 nan 0.2000 0.0067
## 20 0.9397 nan 0.2000 -0.0007
## 40 0.8709 nan 0.2000 -0.0008
## 60 0.8310 nan 0.2000 -0.0010
## 80 0.8089 nan 0.2000 -0.0023
## 100 0.7890 nan 0.2000 -0.0012
## 120 0.7780 nan 0.2000 -0.0013
## 140 0.7664 nan 0.2000 -0.0020
## 160 0.7529 nan 0.2000 -0.0005
## 180 0.7427 nan 0.2000 -0.0004
## 200 0.7334 nan 0.2000 -0.0016
## 220 0.7237 nan 0.2000 -0.0022
## 240 0.7182 nan 0.2000 -0.0006
## 260 0.7116 nan 0.2000 -0.0013
## 280 0.6998 nan 0.2000 -0.0020
## 300 0.6931 nan 0.2000 -0.0013
## 320 0.6870 nan 0.2000 -0.0015
## 340 0.6829 nan 0.2000 -0.0010
## 360 0.6742 nan 0.2000 -0.0014
## 380 0.6634 nan 0.2000 -0.0006
## 400 0.6621 nan 0.2000 -0.0021
## 420 0.6553 nan 0.2000 -0.0015
## 440 0.6489 nan 0.2000 -0.0004
## 460 0.6406 nan 0.2000 -0.0006
## 480 0.6369 nan 0.2000 -0.0018
## 500 0.6325 nan 0.2000 -0.0039
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2236 nan 0.2000 0.0236
## 2 1.1738 nan 0.2000 0.0251
## 3 1.1404 nan 0.2000 0.0158
## 4 1.1122 nan 0.2000 0.0100
## 5 1.0879 nan 0.2000 0.0074
## 6 1.0633 nan 0.2000 0.0120
## 7 1.0481 nan 0.2000 0.0061
## 8 1.0358 nan 0.2000 0.0051
## 9 1.0218 nan 0.2000 0.0036
## 10 1.0123 nan 0.2000 0.0029
## 20 0.9387 nan 0.2000 -0.0004
## 40 0.8722 nan 0.2000 -0.0024
## 60 0.8395 nan 0.2000 -0.0015
## 80 0.8176 nan 0.2000 -0.0005
## 100 0.8047 nan 0.2000 -0.0006
## 120 0.7931 nan 0.2000 -0.0010
## 140 0.7725 nan 0.2000 -0.0015
## 160 0.7573 nan 0.2000 -0.0008
## 180 0.7471 nan 0.2000 -0.0014
## 200 0.7389 nan 0.2000 -0.0010
## 220 0.7271 nan 0.2000 -0.0023
## 240 0.7174 nan 0.2000 -0.0008
## 260 0.7123 nan 0.2000 -0.0037
## 280 0.7004 nan 0.2000 -0.0017
## 300 0.6933 nan 0.2000 -0.0007
## 320 0.6870 nan 0.2000 -0.0041
## 340 0.6745 nan 0.2000 -0.0014
## 360 0.6686 nan 0.2000 -0.0013
## 380 0.6607 nan 0.2000 -0.0010
## 400 0.6529 nan 0.2000 -0.0014
## 420 0.6448 nan 0.2000 -0.0021
## 440 0.6383 nan 0.2000 -0.0011
## 460 0.6336 nan 0.2000 -0.0002
## 480 0.6288 nan 0.2000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2122 nan 0.2000 0.0350
## 2 1.1510 nan 0.2000 0.0291
## 3 1.1041 nan 0.2000 0.0151
## 4 1.0682 nan 0.2000 0.0131
## 5 1.0414 nan 0.2000 0.0098
## 6 1.0104 nan 0.2000 0.0107
## 7 0.9865 nan 0.2000 0.0064
## 8 0.9693 nan 0.2000 0.0075
## 9 0.9585 nan 0.2000 0.0001
## 10 0.9430 nan 0.2000 0.0024
## 20 0.8632 nan 0.2000 -0.0006
## 40 0.7796 nan 0.2000 -0.0012
## 60 0.7151 nan 0.2000 -0.0019
## 80 0.6786 nan 0.2000 -0.0004
## 100 0.6387 nan 0.2000 -0.0022
## 120 0.6012 nan 0.2000 -0.0025
## 140 0.5633 nan 0.2000 -0.0009
## 160 0.5328 nan 0.2000 -0.0023
## 180 0.5064 nan 0.2000 -0.0009
## 200 0.4805 nan 0.2000 -0.0009
## 220 0.4573 nan 0.2000 -0.0021
## 240 0.4417 nan 0.2000 -0.0042
## 260 0.4155 nan 0.2000 -0.0017
## 280 0.4010 nan 0.2000 -0.0019
## 300 0.3817 nan 0.2000 -0.0018
## 320 0.3636 nan 0.2000 -0.0002
## 340 0.3455 nan 0.2000 -0.0013
## 360 0.3354 nan 0.2000 -0.0013
## 380 0.3226 nan 0.2000 -0.0010
## 400 0.3096 nan 0.2000 -0.0012
## 420 0.2988 nan 0.2000 -0.0006
## 440 0.2889 nan 0.2000 -0.0018
## 460 0.2786 nan 0.2000 -0.0013
## 480 0.2698 nan 0.2000 -0.0013
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2081 nan 0.2000 0.0378
## 2 1.1570 nan 0.2000 0.0223
## 3 1.1145 nan 0.2000 0.0176
## 4 1.0715 nan 0.2000 0.0184
## 5 1.0371 nan 0.2000 0.0128
## 6 1.0169 nan 0.2000 0.0027
## 7 0.9948 nan 0.2000 0.0073
## 8 0.9752 nan 0.2000 0.0066
## 9 0.9577 nan 0.2000 0.0065
## 10 0.9460 nan 0.2000 0.0036
## 20 0.8665 nan 0.2000 -0.0020
## 40 0.7844 nan 0.2000 -0.0019
## 60 0.7286 nan 0.2000 -0.0022
## 80 0.6890 nan 0.2000 -0.0022
## 100 0.6494 nan 0.2000 -0.0017
## 120 0.6147 nan 0.2000 -0.0014
## 140 0.5766 nan 0.2000 -0.0011
## 160 0.5434 nan 0.2000 -0.0011
## 180 0.5238 nan 0.2000 -0.0017
## 200 0.5005 nan 0.2000 -0.0049
## 220 0.4790 nan 0.2000 -0.0014
## 240 0.4584 nan 0.2000 -0.0012
## 260 0.4401 nan 0.2000 -0.0015
## 280 0.4157 nan 0.2000 -0.0026
## 300 0.3976 nan 0.2000 -0.0022
## 320 0.3810 nan 0.2000 -0.0021
## 340 0.3659 nan 0.2000 -0.0044
## 360 0.3496 nan 0.2000 -0.0014
## 380 0.3359 nan 0.2000 -0.0000
## 400 0.3219 nan 0.2000 -0.0006
## 420 0.3079 nan 0.2000 -0.0009
## 440 0.2958 nan 0.2000 -0.0010
## 460 0.2876 nan 0.2000 -0.0010
## 480 0.2771 nan 0.2000 0.0001
## 500 0.2651 nan 0.2000 -0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2109 nan 0.2000 0.0344
## 2 1.1494 nan 0.2000 0.0244
## 3 1.0971 nan 0.2000 0.0222
## 4 1.0634 nan 0.2000 0.0124
## 5 1.0289 nan 0.2000 0.0114
## 6 1.0055 nan 0.2000 0.0076
## 7 0.9826 nan 0.2000 0.0059
## 8 0.9696 nan 0.2000 0.0030
## 9 0.9572 nan 0.2000 0.0011
## 10 0.9384 nan 0.2000 0.0044
## 20 0.8602 nan 0.2000 -0.0018
## 40 0.7876 nan 0.2000 -0.0041
## 60 0.7457 nan 0.2000 0.0004
## 80 0.7068 nan 0.2000 -0.0026
## 100 0.6578 nan 0.2000 -0.0043
## 120 0.6173 nan 0.2000 -0.0031
## 140 0.5866 nan 0.2000 -0.0006
## 160 0.5592 nan 0.2000 -0.0007
## 180 0.5297 nan 0.2000 -0.0049
## 200 0.5041 nan 0.2000 -0.0007
## 220 0.4805 nan 0.2000 -0.0011
## 240 0.4548 nan 0.2000 -0.0007
## 260 0.4376 nan 0.2000 -0.0018
## 280 0.4166 nan 0.2000 -0.0008
## 300 0.4006 nan 0.2000 -0.0010
## 320 0.3850 nan 0.2000 -0.0043
## 340 0.3685 nan 0.2000 -0.0034
## 360 0.3542 nan 0.2000 -0.0005
## 380 0.3420 nan 0.2000 -0.0017
## 400 0.3288 nan 0.2000 -0.0018
## 420 0.3157 nan 0.2000 -0.0016
## 440 0.3052 nan 0.2000 -0.0014
## 460 0.2983 nan 0.2000 -0.0015
## 480 0.2882 nan 0.2000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1995 nan 0.2000 0.0357
## 2 1.1451 nan 0.2000 0.0160
## 3 1.0935 nan 0.2000 0.0123
## 4 1.0492 nan 0.2000 0.0164
## 5 1.0108 nan 0.2000 0.0130
## 6 0.9864 nan 0.2000 0.0042
## 7 0.9655 nan 0.2000 0.0060
## 8 0.9456 nan 0.2000 0.0041
## 9 0.9318 nan 0.2000 0.0026
## 10 0.9164 nan 0.2000 0.0028
## 20 0.8073 nan 0.2000 0.0004
## 40 0.7009 nan 0.2000 -0.0014
## 60 0.6374 nan 0.2000 -0.0020
## 80 0.5911 nan 0.2000 -0.0028
## 100 0.5330 nan 0.2000 -0.0007
## 120 0.4806 nan 0.2000 -0.0003
## 140 0.4648 nan 0.2000 -0.0024
## 160 0.4107 nan 0.2000 -0.0019
## 180 0.3753 nan 0.2000 -0.0009
## 200 0.3461 nan 0.2000 -0.0028
## 220 0.3205 nan 0.2000 -0.0006
## 240 0.2952 nan 0.2000 -0.0010
## 260 0.2767 nan 0.2000 -0.0024
## 280 0.2573 nan 0.2000 -0.0018
## 300 0.2374 nan 0.2000 -0.0015
## 320 0.2197 nan 0.2000 -0.0003
## 340 0.2050 nan 0.2000 -0.0012
## 360 0.1910 nan 0.2000 -0.0009
## 380 0.1763 nan 0.2000 -0.0012
## 400 0.1630 nan 0.2000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2040 nan 0.2000 0.0409
## 2 1.1334 nan 0.2000 0.0260
## 3 1.0788 nan 0.2000 0.0187
## 4 1.0352 nan 0.2000 0.0143
## 5 1.0111 nan 0.2000 0.0104
## 6 0.9880 nan 0.2000 0.0043
## 7 0.9586 nan 0.2000 0.0132
## 8 0.9350 nan 0.2000 0.0024
## 9 0.9186 nan 0.2000 0.0014
## 10 0.9012 nan 0.2000 0.0010
## 20 0.8168 nan 0.2000 -0.0037
## 40 0.7194 nan 0.2000 -0.0003
## 60 0.6364 nan 0.2000 -0.0020
## 80 0.5788 nan 0.2000 -0.0022
## 100 0.5265 nan 0.2000 -0.0020
## 120 0.4765 nan 0.2000 -0.0004
## 140 0.4353 nan 0.2000 -0.0022
## 160 0.4002 nan 0.2000 -0.0023
## 180 0.3685 nan 0.2000 -0.0026
## 200 0.3474 nan 0.2000 -0.0031
## 220 0.3216 nan 0.2000 -0.0008
## 240 0.3006 nan 0.2000 -0.0009
## 260 0.2741 nan 0.2000 -0.0024
## 280 0.2598 nan 0.2000 -0.0022
## 300 0.2423 nan 0.2000 -0.0013
## 320 0.2257 nan 0.2000 -0.0016
## 340 0.2104 nan 0.2000 -0.0020
## 360 0.1953 nan 0.2000 -0.0004
## 380 0.1829 nan 0.2000 -0.0006
## 400 0.1705 nan 0.2000 -0.0012
## 420 0.1584 nan 0.2000 -0.0012
## 440 0.1493 nan 0.2000 -0.0010
## 460 0.1395 nan 0.2000 -0.0003
## 480 0.1302 nan 0.2000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2110 nan 0.2000 0.0327
## 2 1.1390 nan 0.2000 0.0284
## 3 1.0842 nan 0.2000 0.0224
## 4 1.0413 nan 0.2000 0.0140
## 5 1.0021 nan 0.2000 0.0152
## 6 0.9763 nan 0.2000 0.0089
## 7 0.9537 nan 0.2000 0.0043
## 8 0.9347 nan 0.2000 0.0042
## 9 0.9231 nan 0.2000 -0.0015
## 10 0.9080 nan 0.2000 0.0037
## 20 0.8291 nan 0.2000 -0.0006
## 40 0.7279 nan 0.2000 -0.0025
## 60 0.6563 nan 0.2000 -0.0021
## 80 0.6027 nan 0.2000 -0.0037
## 100 0.5373 nan 0.2000 -0.0015
## 120 0.4893 nan 0.2000 -0.0004
## 140 0.4457 nan 0.2000 -0.0020
## 160 0.4049 nan 0.2000 0.0001
## 180 0.3789 nan 0.2000 -0.0021
## 200 0.3485 nan 0.2000 -0.0013
## 220 0.3191 nan 0.2000 -0.0028
## 240 0.3006 nan 0.2000 -0.0027
## 260 0.2799 nan 0.2000 -0.0004
## 280 0.2660 nan 0.2000 -0.0020
## 300 0.2489 nan 0.2000 -0.0009
## 320 0.2299 nan 0.2000 -0.0007
## 340 0.2135 nan 0.2000 -0.0006
## 360 0.1994 nan 0.2000 -0.0013
## 380 0.1863 nan 0.2000 -0.0014
## 400 0.1754 nan 0.2000 -0.0008
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## 460 0.1467 nan 0.2000 -0.0002
## 480 0.1387 nan 0.2000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2061 nan 0.3000 0.0399
## 2 1.1551 nan 0.3000 0.0181
## 3 1.1096 nan 0.3000 0.0207
## 4 1.0813 nan 0.3000 0.0085
## 5 1.0467 nan 0.3000 0.0147
## 6 1.0270 nan 0.3000 0.0086
## 7 1.0079 nan 0.3000 0.0077
## 8 0.9869 nan 0.3000 0.0058
## 9 0.9729 nan 0.3000 0.0028
## 10 0.9654 nan 0.3000 -0.0009
## 20 0.8921 nan 0.3000 0.0003
## 40 0.8310 nan 0.3000 0.0005
## 60 0.8046 nan 0.3000 -0.0014
## 80 0.7770 nan 0.3000 -0.0032
## 100 0.7653 nan 0.3000 -0.0034
## 120 0.7530 nan 0.3000 -0.0065
## 140 0.7436 nan 0.3000 -0.0007
## 160 0.7211 nan 0.3000 -0.0024
## 180 0.7083 nan 0.3000 -0.0019
## 200 0.6985 nan 0.3000 -0.0055
## 220 0.6889 nan 0.3000 -0.0009
## 240 0.6795 nan 0.3000 -0.0027
## 260 0.6734 nan 0.3000 -0.0030
## 280 0.6614 nan 0.3000 -0.0010
## 300 0.6483 nan 0.3000 -0.0027
## 320 0.6370 nan 0.3000 -0.0007
## 340 0.6290 nan 0.3000 -0.0024
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## 380 0.6137 nan 0.3000 -0.0025
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## 420 0.5997 nan 0.3000 -0.0006
## 440 0.5949 nan 0.3000 -0.0029
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## 480 0.5846 nan 0.3000 -0.0031
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1990 nan 0.3000 0.0411
## 2 1.1444 nan 0.3000 0.0218
## 3 1.1091 nan 0.3000 0.0129
## 4 1.0755 nan 0.3000 0.0152
## 5 1.0447 nan 0.3000 0.0151
## 6 1.0184 nan 0.3000 0.0061
## 7 1.0046 nan 0.3000 0.0037
## 8 0.9960 nan 0.3000 -0.0011
## 9 0.9843 nan 0.3000 0.0035
## 10 0.9674 nan 0.3000 0.0060
## 20 0.8951 nan 0.3000 -0.0016
## 40 0.8366 nan 0.3000 -0.0010
## 60 0.8066 nan 0.3000 -0.0014
## 80 0.7772 nan 0.3000 -0.0013
## 100 0.7579 nan 0.3000 -0.0005
## 120 0.7469 nan 0.3000 -0.0014
## 140 0.7255 nan 0.3000 -0.0017
## 160 0.7148 nan 0.3000 -0.0017
## 180 0.7001 nan 0.3000 0.0004
## 200 0.6897 nan 0.3000 -0.0022
## 220 0.6762 nan 0.3000 -0.0036
## 240 0.6656 nan 0.3000 -0.0023
## 260 0.6566 nan 0.3000 -0.0015
## 280 0.6510 nan 0.3000 -0.0039
## 300 0.6414 nan 0.3000 -0.0018
## 320 0.6284 nan 0.3000 -0.0036
## 340 0.6204 nan 0.3000 -0.0014
## 360 0.6153 nan 0.3000 -0.0032
## 380 0.6057 nan 0.3000 -0.0046
## 400 0.5981 nan 0.3000 -0.0028
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## 440 0.5879 nan 0.3000 -0.0027
## 460 0.5832 nan 0.3000 -0.0029
## 480 0.5781 nan 0.3000 -0.0029
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1944 nan 0.3000 0.0372
## 2 1.1495 nan 0.3000 0.0182
## 3 1.1109 nan 0.3000 0.0144
## 4 1.0750 nan 0.3000 0.0172
## 5 1.0425 nan 0.3000 0.0127
## 6 1.0150 nan 0.3000 0.0070
## 7 1.0017 nan 0.3000 0.0008
## 8 0.9912 nan 0.3000 0.0047
## 9 0.9791 nan 0.3000 0.0012
## 10 0.9653 nan 0.3000 0.0055
## 20 0.9046 nan 0.3000 0.0010
## 40 0.8403 nan 0.3000 -0.0016
## 60 0.8035 nan 0.3000 -0.0018
## 80 0.7815 nan 0.3000 -0.0019
## 100 0.7695 nan 0.3000 -0.0037
## 120 0.7491 nan 0.3000 -0.0068
## 140 0.7325 nan 0.3000 -0.0016
## 160 0.7144 nan 0.3000 -0.0034
## 180 0.7011 nan 0.3000 -0.0049
## 200 0.6886 nan 0.3000 -0.0055
## 220 0.6759 nan 0.3000 -0.0027
## 240 0.6676 nan 0.3000 -0.0015
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## 280 0.6468 nan 0.3000 -0.0006
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## 320 0.6363 nan 0.3000 -0.0021
## 340 0.6260 nan 0.3000 -0.0037
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## 380 0.6119 nan 0.3000 -0.0065
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## 480 0.5742 nan 0.3000 -0.0026
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1815 nan 0.3000 0.0564
## 2 1.1069 nan 0.3000 0.0338
## 3 1.0604 nan 0.3000 0.0144
## 4 1.0179 nan 0.3000 0.0164
## 5 0.9913 nan 0.3000 0.0085
## 6 0.9638 nan 0.3000 0.0077
## 7 0.9451 nan 0.3000 0.0024
## 8 0.9305 nan 0.3000 0.0007
## 9 0.9163 nan 0.3000 0.0021
## 10 0.9049 nan 0.3000 -0.0043
## 20 0.8208 nan 0.3000 -0.0043
## 40 0.7507 nan 0.3000 -0.0074
## 60 0.6852 nan 0.3000 -0.0099
## 80 0.6242 nan 0.3000 -0.0034
## 100 0.5828 nan 0.3000 -0.0023
## 120 0.5458 nan 0.3000 -0.0043
## 140 0.5161 nan 0.3000 -0.0086
## 160 0.4848 nan 0.3000 -0.0002
## 180 0.4634 nan 0.3000 -0.0055
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## 220 0.4016 nan 0.3000 -0.0003
## 240 0.3719 nan 0.3000 -0.0015
## 260 0.3536 nan 0.3000 -0.0028
## 280 0.3344 nan 0.3000 0.0001
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## 460 0.2098 nan 0.3000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1820 nan 0.3000 0.0480
## 2 1.1112 nan 0.3000 0.0309
## 3 1.0554 nan 0.3000 0.0252
## 4 1.0102 nan 0.3000 0.0182
## 5 0.9800 nan 0.3000 0.0053
## 6 0.9589 nan 0.3000 0.0048
## 7 0.9458 nan 0.3000 -0.0054
## 8 0.9251 nan 0.3000 0.0071
## 9 0.9144 nan 0.3000 -0.0026
## 10 0.8986 nan 0.3000 0.0006
## 20 0.8201 nan 0.3000 -0.0028
## 40 0.7272 nan 0.3000 -0.0018
## 60 0.6594 nan 0.3000 -0.0039
## 80 0.6080 nan 0.3000 -0.0034
## 100 0.5675 nan 0.3000 -0.0057
## 120 0.5320 nan 0.3000 -0.0035
## 140 0.4976 nan 0.3000 -0.0015
## 160 0.4666 nan 0.3000 -0.0021
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## 220 0.3873 nan 0.3000 -0.0025
## 240 0.3704 nan 0.3000 -0.0007
## 260 inf nan 0.3000 nan
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## 420 inf nan 0.3000 nan
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## 460 inf nan 0.3000 nan
## 480 inf nan 0.3000 nan
## 500 inf nan 0.3000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1784 nan 0.3000 0.0606
## 2 1.0945 nan 0.3000 0.0378
## 3 1.0434 nan 0.3000 0.0157
## 4 1.0110 nan 0.3000 0.0125
## 5 0.9845 nan 0.3000 0.0053
## 6 0.9682 nan 0.3000 0.0003
## 7 0.9461 nan 0.3000 0.0065
## 8 0.9268 nan 0.3000 0.0014
## 9 0.9154 nan 0.3000 -0.0027
## 10 0.8970 nan 0.3000 0.0068
## 20 0.8189 nan 0.3000 -0.0007
## 40 0.7285 nan 0.3000 -0.0040
## 60 0.6702 nan 0.3000 -0.0042
## 80 0.6096 nan 0.3000 -0.0021
## 100 0.5676 nan 0.3000 -0.0012
## 120 0.5283 nan 0.3000 -0.0028
## 140 0.5077 nan 0.3000 -0.0043
## 160 0.4651 nan 0.3000 -0.0011
## 180 0.4474 nan 0.3000 -0.0031
## 200 0.4256 nan 0.3000 -0.0033
## 220 0.3991 nan 0.3000 -0.0007
## 240 0.3771 nan 0.3000 -0.0034
## 260 0.3461 nan 0.3000 -0.0001
## 280 0.3328 nan 0.3000 -0.0041
## 300 0.3142 nan 0.3000 -0.0037
## 320 0.2998 nan 0.3000 -0.0017
## 340 0.2815 nan 0.3000 -0.0011
## 360 0.2634 nan 0.3000 -0.0014
## 380 0.2497 nan 0.3000 -0.0009
## 400 0.2347 nan 0.3000 -0.0030
## 420 0.2234 nan 0.3000 -0.0024
## 440 0.2109 nan 0.3000 -0.0010
## 460 0.1995 nan 0.3000 -0.0010
## 480 0.1905 nan 0.3000 -0.0020
## 500 0.1853 nan 0.3000 -0.0020
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1548 nan 0.3000 0.0525
## 2 1.0745 nan 0.3000 0.0293
## 3 1.0162 nan 0.3000 0.0179
## 4 0.9688 nan 0.3000 0.0178
## 5 0.9505 nan 0.3000 -0.0018
## 6 0.9282 nan 0.3000 -0.0014
## 7 0.9066 nan 0.3000 0.0011
## 8 0.8988 nan 0.3000 -0.0104
## 9 0.8832 nan 0.3000 0.0024
## 10 0.8719 nan 0.3000 -0.0022
## 20 0.7706 nan 0.3000 -0.0043
## 40 0.6526 nan 0.3000 -0.0011
## 60 0.5875 nan 0.3000 -0.0416
## 80 1.2417 nan 0.3000 -0.0052
## 100 1.2177 nan 0.3000 -0.0008
## 120 1.6391 nan 0.3000 -0.5285
## 140 1.5249 nan 0.3000 -0.0003
## 160 4073.1442 nan 0.3000 -0.0012
## 180 4073.0797 nan 0.3000 -0.0008
## 200 4073.0343 nan 0.3000 -0.0046
## 220 4072.9663 nan 0.3000 -0.0023
## 240 4072.6440 nan 0.3000 -0.0003
## 260 4072.6338 nan 0.3000 -0.0022
## 280 4072.5388 nan 0.3000 -0.0002
## 300 4072.5145 nan 0.3000 -0.0006
## 320 4102.3784 nan 0.3000 -0.0018
## 340 inf nan 0.3000 nan
## 360 inf nan 0.3000 nan
## 380 inf nan 0.3000 nan
## 400 inf nan 0.3000 nan
## 420 inf nan 0.3000 nan
## 440 inf nan 0.3000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1837 nan 0.3000 0.0568
## 2 1.0997 nan 0.3000 0.0267
## 3 1.0121 nan 0.3000 0.0339
## 4 0.9720 nan 0.3000 0.0096
## 5 0.9403 nan 0.3000 0.0023
## 6 0.9239 nan 0.3000 -0.0032
## 7 0.9093 nan 0.3000 0.0026
## 8 0.8932 nan 0.3000 0.0003
## 9 0.8765 nan 0.3000 0.0010
## 10 0.8594 nan 0.3000 0.0003
## 20 0.7593 nan 0.3000 0.0006
## 40 0.6635 nan 0.3000 -0.0089
## 60 0.5595 nan 0.3000 -0.0060
## 80 0.4968 nan 0.3000 -0.0039
## 100 0.4427 nan 0.3000 -0.0055
## 120 0.3924 nan 0.3000 -0.0010
## 140 0.3427 nan 0.3000 -0.0008
## 160 0.3038 nan 0.3000 -0.0027
## 180 0.2761 nan 0.3000 -0.0017
## 200 0.2475 nan 0.3000 -0.0022
## 220 0.2233 nan 0.3000 -0.0045
## 240 0.2002 nan 0.3000 -0.0013
## 260 0.1811 nan 0.3000 -0.0016
## 280 0.1643 nan 0.3000 -0.0012
## 300 0.1480 nan 0.3000 -0.0016
## 320 0.1344 nan 0.3000 -0.0015
## 340 0.1215 nan 0.3000 -0.0007
## 360 0.1103 nan 0.3000 -0.0015
## 380 0.1004 nan 0.3000 -0.0009
## 400 0.0929 nan 0.3000 -0.0021
## 420 0.0836 nan 0.3000 -0.0005
## 440 0.0775 nan 0.3000 -0.0002
## 460 0.0715 nan 0.3000 -0.0005
## 480 0.0661 nan 0.3000 -0.0007
## 500 0.0611 nan 0.3000 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1498 nan 0.3000 0.0607
## 2 1.0577 nan 0.3000 0.0430
## 3 1.0064 nan 0.3000 0.0179
## 4 0.9755 nan 0.3000 0.0041
## 5 0.9587 nan 0.3000 -0.0019
## 6 0.9323 nan 0.3000 0.0024
## 7 0.9089 nan 0.3000 -0.0002
## 8 0.8914 nan 0.3000 -0.0018
## 9 0.8792 nan 0.3000 -0.0021
## 10 0.8644 nan 0.3000 -0.0015
## 20 0.7669 nan 0.3000 -0.0011
## 40 0.6732 nan 0.3000 -0.0046
## 60 0.5966 nan 0.3000 -0.0072
## 80 0.5358 nan 0.3000 -0.0038
## 100 0.4658 nan 0.3000 -0.0026
## 120 0.4063 nan 0.3000 -0.0039
## 140 0.3678 nan 0.3000 -0.0040
## 160 0.3295 nan 0.3000 -0.0011
## 180 0.2909 nan 0.3000 -0.0045
## 200 0.2622 nan 0.3000 -0.0012
## 220 0.2355 nan 0.3000 -0.0016
## 240 0.2100 nan 0.3000 -0.0023
## 260 0.1858 nan 0.3000 -0.0028
## 280 0.1685 nan 0.3000 -0.0019
## 300 0.1555 nan 0.3000 -0.0013
## 320 0.1399 nan 0.3000 -0.0025
## 340 0.1278 nan 0.3000 -0.0009
## 360 0.1167 nan 0.3000 -0.0007
## 380 0.1081 nan 0.3000 -0.0008
## 400 0.1022 nan 0.3000 -0.0009
## 420 0.0915 nan 0.3000 -0.0017
## 440 0.0837 nan 0.3000 -0.0009
## 460 0.0759 nan 0.3000 -0.0005
## 480 0.0692 nan 0.3000 -0.0004
## 500 0.0629 nan 0.3000 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1592 nan 0.5000 0.0528
## 2 1.1033 nan 0.5000 0.0210
## 3 1.0483 nan 0.5000 0.0165
## 4 1.0160 nan 0.5000 0.0112
## 5 0.9957 nan 0.5000 0.0101
## 6 0.9670 nan 0.5000 0.0119
## 7 0.9588 nan 0.5000 -0.0005
## 8 0.9428 nan 0.5000 0.0023
## 9 0.9314 nan 0.5000 -0.0029
## 10 0.9236 nan 0.5000 -0.0014
## 20 0.8671 nan 0.5000 -0.0120
## 40 0.8256 nan 0.5000 -0.0133
## 60 0.7772 nan 0.5000 -0.0089
## 80 0.7487 nan 0.5000 -0.0098
## 100 0.7152 nan 0.5000 -0.0107
## 120 0.6875 nan 0.5000 -0.0060
## 140 0.6664 nan 0.5000 -0.0013
## 160 0.6544 nan 0.5000 -0.0066
## 180 0.6436 nan 0.5000 -0.0077
## 200 0.6280 nan 0.5000 -0.0016
## 220 0.6233 nan 0.5000 -0.0093
## 240 0.6077 nan 0.5000 -0.0060
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1661 nan 0.5000 0.0524
## 2 1.0944 nan 0.5000 0.0197
## 3 1.0461 nan 0.5000 0.0145
## 4 1.0019 nan 0.5000 0.0229
## 5 0.9794 nan 0.5000 -0.0021
## 6 0.9536 nan 0.5000 0.0036
## 7 0.9413 nan 0.5000 0.0018
## 8 0.9354 nan 0.5000 -0.0072
## 9 0.9407 nan 0.5000 -0.0154
## 10 0.9223 nan 0.5000 0.0040
## 20 0.8581 nan 0.5000 -0.0022
## 40 0.8059 nan 0.5000 -0.0077
## 60 0.7683 nan 0.5000 -0.0040
## 80 0.7360 nan 0.5000 -0.0041
## 100 0.7246 nan 0.5000 -0.0060
## 120 0.7090 nan 0.5000 -0.0132
## 140 0.6850 nan 0.5000 -0.0109
## 160 0.6643 nan 0.5000 -0.0077
## 180 0.6440 nan 0.5000 -0.0060
## 200 0.6331 nan 0.5000 -0.0067
## 220 0.6225 nan 0.5000 -0.0021
## 240 0.6074 nan 0.5000 -0.0040
## 260 0.5989 nan 0.5000 -0.0000
## 280 0.5859 nan 0.5000 -0.0084
## 300 0.5729 nan 0.5000 -0.0046
## 320 0.5674 nan 0.5000 -0.0010
## 340 0.5627 nan 0.5000 -0.0066
## 360 0.5568 nan 0.5000 -0.0053
## 380 0.5491 nan 0.5000 -0.0111
## 400 0.5453 nan 0.5000 -0.0045
## 420 0.5279 nan 0.5000 -0.0080
## 440 0.5254 nan 0.5000 -0.0061
## 460 0.5109 nan 0.5000 -0.0017
## 480 0.5115 nan 0.5000 -0.0062
## 500 0.4949 nan 0.5000 -0.0025
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1781 nan 0.5000 0.0558
## 2 1.1190 nan 0.5000 0.0179
## 3 1.0519 nan 0.5000 0.0296
## 4 1.0166 nan 0.5000 0.0190
## 5 0.9963 nan 0.5000 0.0078
## 6 0.9797 nan 0.5000 -0.0005
## 7 0.9687 nan 0.5000 0.0007
## 8 0.9510 nan 0.5000 0.0016
## 9 0.9417 nan 0.5000 -0.0025
## 10 0.9300 nan 0.5000 -0.0021
## 20 0.8678 nan 0.5000 -0.0028
## 40 0.8190 nan 0.5000 -0.0024
## 60 0.7899 nan 0.5000 -0.0099
## 80 0.7618 nan 0.5000 -0.0002
## 100 0.7360 nan 0.5000 -0.0042
## 120 0.7139 nan 0.5000 -0.0051
## 140 0.6990 nan 0.5000 -0.0025
## 160 0.6841 nan 0.5000 -0.0074
## 180 0.6670 nan 0.5000 0.0006
## 200 0.6636 nan 0.5000 -0.0094
## 220 0.6518 nan 0.5000 -0.0018
## 240 0.6326 nan 0.5000 -0.0021
## 260 0.6104 nan 0.5000 -0.0021
## 280 0.6013 nan 0.5000 -0.0045
## 300 0.5884 nan 0.5000 -0.0049
## 320 0.5733 nan 0.5000 -0.0037
## 340 0.5659 nan 0.5000 -0.0030
## 360 0.5605 nan 0.5000 -0.0017
## 380 0.5531 nan 0.5000 -0.0045
## 400 0.5483 nan 0.5000 -0.0034
## 420 0.5381 nan 0.5000 -0.0010
## 440 0.5369 nan 0.5000 -0.0073
## 460 0.5272 nan 0.5000 -0.0027
## 480 0.5225 nan 0.5000 -0.0023
## 500 0.5154 nan 0.5000 -0.0057
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1290 nan 0.5000 0.0747
## 2 1.0295 nan 0.5000 0.0442
## 3 0.9961 nan 0.5000 0.0010
## 4 0.9491 nan 0.5000 0.0177
## 5 0.9287 nan 0.5000 -0.0010
## 6 0.8981 nan 0.5000 -0.0030
## 7 0.8825 nan 0.5000 -0.0062
## 8 0.8703 nan 0.5000 -0.0027
## 9 0.8637 nan 0.5000 -0.0079
## 10 0.8490 nan 0.5000 0.0002
## 20 0.7858 nan 0.5000 -0.0125
## 40 inf nan 0.5000 -inf
## 60 inf nan 0.5000 nan
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1175 nan 0.5000 0.0770
## 2 1.0578 nan 0.5000 0.0030
## 3 1.0051 nan 0.5000 0.0269
## 4 0.9891 nan 0.5000 -0.0028
## 5 0.9635 nan 0.5000 -0.0008
## 6 0.9432 nan 0.5000 -0.0023
## 7 0.9298 nan 0.5000 -0.0048
## 8 0.9265 nan 0.5000 -0.0081
## 9 0.9074 nan 0.5000 0.0008
## 10 0.8932 nan 0.5000 -0.0006
## 20 0.8088 nan 0.5000 -0.0068
## 40 0.7246 nan 0.5000 -0.0044
## 60 0.6403 nan 0.5000 -0.0053
## 80 0.5809 nan 0.5000 -0.0013
## 100 0.5388 nan 0.5000 -0.0061
## 120 0.4813 nan 0.5000 -0.0014
## 140 0.4292 nan 0.5000 -0.0047
## 160 0.4009 nan 0.5000 -0.0084
## 180 0.3482 nan 0.5000 -0.0019
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
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## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1295 nan 0.5000 0.0692
## 2 1.0447 nan 0.5000 0.0322
## 3 0.9793 nan 0.5000 0.0183
## 4 0.9506 nan 0.5000 0.0038
## 5 0.9299 nan 0.5000 0.0008
## 6 0.9083 nan 0.5000 0.0009
## 7 0.8932 nan 0.5000 -0.0015
## 8 0.8860 nan 0.5000 -0.0091
## 9 0.8906 nan 0.5000 -0.0282
## 10 0.8795 nan 0.5000 -0.0152
## 20 0.8376 nan 0.5000 -0.0344
## 40 0.7189 nan 0.5000 -0.0036
## 60 0.6456 nan 0.5000 -0.0046
## 80 0.5767 nan 0.5000 -0.0068
## 100 0.5141 nan 0.5000 -0.0066
## 120 0.4715 nan 0.5000 -0.0009
## 140 0.4279 nan 0.5000 -0.0097
## 160 0.3782 nan 0.5000 -0.0021
## 180 0.3417 nan 0.5000 -0.0050
## 200 0.3014 nan 0.5000 -0.0058
## 220 0.2753 nan 0.5000 -0.0054
## 240 0.2528 nan 0.5000 -0.0061
## 260 0.2222 nan 0.5000 -0.0044
## 280 0.1985 nan 0.5000 -0.0013
## 300 0.1795 nan 0.5000 -0.0018
## 320 0.1684 nan 0.5000 -0.0026
## 340 0.1612 nan 0.5000 -0.0021
## 360 0.1477 nan 0.5000 -0.0009
## 380 0.1367 nan 0.5000 -0.0010
## 400 0.1296 nan 0.5000 -0.0043
## 420 0.1189 nan 0.5000 -0.0018
## 440 0.1102 nan 0.5000 -0.0011
## 460 0.0989 nan 0.5000 -0.0002
## 480 0.0924 nan 0.5000 -0.0019
## 500 0.0842 nan 0.5000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1039 nan 0.5000 0.0793
## 2 1.0285 nan 0.5000 0.0151
## 3 0.9526 nan 0.5000 0.0237
## 4 0.9125 nan 0.5000 0.0075
## 5 0.8765 nan 0.5000 -0.0059
## 6 0.8585 nan 0.5000 -0.0053
## 7 0.8543 nan 0.5000 -0.0127
## 8 0.8425 nan 0.5000 -0.0076
## 9 0.8167 nan 0.5000 0.0026
## 10 0.8121 nan 0.5000 -0.0200
## 20 0.6988 nan 0.5000 -0.0098
## 40 0.5468 nan 0.5000 -0.0056
## 60 0.4441 nan 0.5000 -0.0103
## 80 0.3935 nan 0.5000 -0.0037
## 100 0.2983 nan 0.5000 -0.0036
## 120 0.2505 nan 0.5000 -0.0027
## 140 0.2099 nan 0.5000 -0.0029
## 160 0.1705 nan 0.5000 -0.0018
## 180 0.1450 nan 0.5000 -0.0030
## 200 0.1238 nan 0.5000 -0.0010
## 220 0.1064 nan 0.5000 -0.0005
## 240 0.0934 nan 0.5000 -0.0007
## 260 0.0819 nan 0.5000 -0.0015
## 280 0.0738 nan 0.5000 -0.0013
## 300 0.0608 nan 0.5000 0.0001
## 320 0.0555 nan 0.5000 -0.0020
## 340 0.0473 nan 0.5000 -0.0003
## 360 0.0432 nan 0.5000 -0.0005
## 380 0.0386 nan 0.5000 -0.0006
## 400 0.0354 nan 0.5000 -0.0000
## 420 0.0324 nan 0.5000 -0.0007
## 440 0.0290 nan 0.5000 -0.0005
## 460 0.0269 nan 0.5000 -0.0006
## 480 0.0238 nan 0.5000 -0.0003
## 500 0.0221 nan 0.5000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0911 nan 0.5000 0.0978
## 2 1.0012 nan 0.5000 0.0337
## 3 0.9341 nan 0.5000 0.0172
## 4 0.9219 nan 0.5000 -0.0080
## 5 0.8908 nan 0.5000 0.0078
## 6 0.8658 nan 0.5000 0.0037
## 7 0.8566 nan 0.5000 -0.0134
## 8 0.8371 nan 0.5000 -0.0024
## 9 0.8255 nan 0.5000 -0.0141
## 10 0.8012 nan 0.5000 0.0004
## 20 0.7339 nan 0.5000 -0.0065
## 40 0.5987 nan 0.5000 -0.0099
## 60 0.5148 nan 0.5000 -0.0082
## 80 0.4039 nan 0.5000 -0.0100
## 100 0.3424 nan 0.5000 -0.0037
## 120 0.2742 nan 0.5000 -0.0011
## 140 0.2318 nan 0.5000 -0.0046
## 160 0.1953 nan 0.5000 -0.0007
## 180 0.1681 nan 0.5000 -0.0048
## 200 0.1358 nan 0.5000 -0.0026
## 220 0.1143 nan 0.5000 -0.0015
## 240 0.0991 nan 0.5000 -0.0023
## 260 0.0857 nan 0.5000 -0.0020
## 280 0.0737 nan 0.5000 -0.0015
## 300 0.0639 nan 0.5000 -0.0004
## 320 0.0595 nan 0.5000 -0.0017
## 340 0.0520 nan 0.5000 -0.0013
## 360 0.0446 nan 0.5000 -0.0010
## 380 0.0395 nan 0.5000 -0.0008
## 400 0.0359 nan 0.5000 -0.0004
## 420 0.0323 nan 0.5000 -0.0005
## 440 0.0300 nan 0.5000 -0.0008
## 460 0.0261 nan 0.5000 -0.0004
## 480 0.0233 nan 0.5000 -0.0002
## 500 0.0212 nan 0.5000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0874 nan 0.5000 0.0753
## 2 1.0193 nan 0.5000 0.0160
## 3 0.9677 nan 0.5000 0.0135
## 4 0.9361 nan 0.5000 0.0003
## 5 0.8907 nan 0.5000 0.0114
## 6 0.8631 nan 0.5000 -0.0073
## 7 0.8491 nan 0.5000 -0.0106
## 8 0.8367 nan 0.5000 -0.0082
## 9 0.8125 nan 0.5000 0.0006
## 10 0.7929 nan 0.5000 -0.0077
## 20 0.6837 nan 0.5000 -0.0033
## 40 0.5599 nan 0.5000 -0.0076
## 60 0.4473 nan 0.5000 -0.0044
## 80 0.3511 nan 0.5000 0.0005
## 100 0.2933 nan 0.5000 -0.0039
## 120 0.2444 nan 0.5000 -0.0008
## 140 0.2072 nan 0.5000 -0.0032
## 160 0.1709 nan 0.5000 -0.0017
## 180 0.1400 nan 0.5000 -0.0023
## 200 0.1239 nan 0.5000 -0.0028
## 220 0.1038 nan 0.5000 -0.0007
## 240 0.0899 nan 0.5000 -0.0025
## 260 0.0750 nan 0.5000 -0.0006
## 280 0.0663 nan 0.5000 -0.0022
## 300 0.0582 nan 0.5000 -0.0011
## 320 0.0495 nan 0.5000 -0.0013
## 340 0.0440 nan 0.5000 -0.0003
## 360 0.0385 nan 0.5000 -0.0006
## 380 0.0332 nan 0.5000 -0.0007
## 400 0.0292 nan 0.5000 -0.0007
## 420 0.0252 nan 0.5000 -0.0003
## 440 0.0217 nan 0.5000 -0.0003
## 460 0.0201 nan 0.5000 -0.0003
## 480 0.0175 nan 0.5000 -0.0002
## 500 0.0153 nan 0.5000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1277 nan 1.0000 0.0469
## 2 1.0630 nan 1.0000 0.0154
## 3 1.0079 nan 1.0000 0.0105
## 4 0.9848 nan 1.0000 -0.0001
## 5 0.9722 nan 1.0000 -0.0131
## 6 1.0275 nan 1.0000 -0.0703
## 7 1.0128 nan 1.0000 0.0019
## 8 1.0298 nan 1.0000 -0.0278
## 9 1.0141 nan 1.0000 -0.0047
## 10 1.0015 nan 1.0000 -0.0085
## 20 0.9403 nan 1.0000 -0.0121
## 40 1.6101 nan 1.0000 -0.0217
## 60 1.7129 nan 1.0000 -0.0530
## 80 14103816.1715 nan 1.0000 0.0000
## 100 14103816.0541 nan 1.0000 0.0355
## 120 14103817.8795 nan 1.0000 -0.0083
## 140 14103817.8677 nan 1.0000 0.0024
## 160 14103817.8340 nan 1.0000 0.0007
## 180 14103817.8037 nan 1.0000 -0.0031
## 200 14103817.8233 nan 1.0000 -0.0026
## 220 14103819.7096 nan 1.0000 0.0022
## 240 14103819.6722 nan 1.0000 0.0024
## 260 14103819.6417 nan 1.0000 -0.0008
## 280 14103819.6305 nan 1.0000 -0.0005
## 300 14103819.5962 nan 1.0000 0.0019
## 320 14103819.5459 nan 1.0000 -0.0002
## 340 14103819.5182 nan 1.0000 0.0013
## 360 14103819.4915 nan 1.0000 0.0008
## 380 14103819.4756 nan 1.0000 0.0002
## 400 14103819.4497 nan 1.0000 0.0039
## 420 14103819.4481 nan 1.0000 -0.0009
## 440 14103819.4417 nan 1.0000 -0.0017
## 460 14103819.4453 nan 1.0000 -0.0121
## 480 14103819.4155 nan 1.0000 0.0006
## 500 14103819.4059 nan 1.0000 -0.0152
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1408 nan 1.0000 0.0427
## 2 1.0879 nan 1.0000 0.0063
## 3 1.0224 nan 1.0000 0.0211
## 4 1.0392 nan 1.0000 -0.0390
## 5 1.0117 nan 1.0000 0.0085
## 6 0.9997 nan 1.0000 -0.0059
## 7 0.9865 nan 1.0000 -0.0047
## 8 0.9620 nan 1.0000 0.0026
## 9 0.9026 nan 1.0000 0.0247
## 10 0.8840 nan 1.0000 -0.0002
## 20 0.8315 nan 1.0000 -0.0129
## 40 0.8256 nan 1.0000 0.0069
## 60 0.7621 nan 1.0000 -0.0153
## 80 0.7652 nan 1.0000 -0.0068
## 100 0.7514 nan 1.0000 0.0101
## 120 0.7268 nan 1.0000 -0.0047
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
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## 320 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1324 nan 1.0000 0.0643
## 2 1.0728 nan 1.0000 0.0179
## 3 1.0251 nan 1.0000 -0.0094
## 4 1.0458 nan 1.0000 -0.0477
## 5 1.0246 nan 1.0000 -0.0071
## 6 0.9984 nan 1.0000 0.0062
## 7 0.9781 nan 1.0000 -0.0074
## 8 0.9578 nan 1.0000 -0.0105
## 9 0.9466 nan 1.0000 -0.0024
## 10 0.9483 nan 1.0000 -0.0243
## 20 1.5387 nan 1.0000 -0.1960
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0801 nan 1.0000 0.0999
## 2 0.9971 nan 1.0000 0.0121
## 3 0.9763 nan 1.0000 -0.0118
## 4 1.0037 nan 1.0000 -0.0644
## 5 0.9584 nan 1.0000 0.0263
## 6 0.9501 nan 1.0000 -0.0328
## 7 0.9569 nan 1.0000 -0.0397
## 8 0.9620 nan 1.0000 -0.0375
## 9 0.9547 nan 1.0000 -0.0245
## 10 1.0646 nan 1.0000 -0.1439
## 20 29.7114 nan 1.0000 -0.1214
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1073 nan 1.0000 0.0800
## 2 1.0199 nan 1.0000 0.0187
## 3 1.0103 nan 1.0000 -0.0241
## 4 1.0190 nan 1.0000 -0.0445
## 5 1.1857 nan 1.0000 -0.1472
## 6 inf nan 1.0000 -inf
## 7 inf nan 1.0000 nan
## 8 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0896 nan 1.0000 0.0876
## 2 1.0367 nan 1.0000 -0.0245
## 3 1.0463 nan 1.0000 -0.0789
## 4 0.9798 nan 1.0000 0.0273
## 5 0.9506 nan 1.0000 -0.0079
## 6 0.9481 nan 1.0000 -0.0297
## 7 0.9343 nan 1.0000 -0.0003
## 8 0.9743 nan 1.0000 -0.0513
## 9 0.9404 nan 1.0000 -0.0035
## 10 0.9456 nan 1.0000 -0.0386
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0324 nan 1.0000 0.1099
## 2 1.0147 nan 1.0000 -0.0361
## 3 0.9708 nan 1.0000 -0.0225
## 4 0.9440 nan 1.0000 -0.0286
## 5 1.0126 nan 1.0000 -0.1250
## 6 1.0176 nan 1.0000 -0.0675
## 7 1.0878 nan 1.0000 -0.0920
## 8 1.0322 nan 1.0000 0.0017
## 9 0.9791 nan 1.0000 -0.0146
## 10 1.0753 nan 1.0000 -0.1616
## 20 inf nan 1.0000 -inf
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0538 nan 1.0000 0.0823
## 2 0.9939 nan 1.0000 0.0051
## 3 1.0341 nan 1.0000 -0.0799
## 4 0.9595 nan 1.0000 0.0211
## 5 0.9261 nan 1.0000 -0.0333
## 6 0.9251 nan 1.0000 -0.0403
## 7 0.8984 nan 1.0000 -0.0275
## 8 0.9092 nan 1.0000 -0.0513
## 9 0.8699 nan 1.0000 0.0005
## 10 0.8431 nan 1.0000 -0.0147
## 20 1.0379 nan 1.0000 0.0015
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0474 nan 1.0000 0.1115
## 2 1.0020 nan 1.0000 -0.0137
## 3 0.9494 nan 1.0000 -0.0013
## 4 0.9260 nan 1.0000 -0.0355
## 5 0.9117 nan 1.0000 -0.0268
## 6 0.8790 nan 1.0000 0.0011
## 7 0.9074 nan 1.0000 -0.0669
## 8 0.8681 nan 1.0000 0.0079
## 9 0.8885 nan 1.0000 -0.0479
## 10 0.8704 nan 1.0000 -0.0182
## 20 0.8318 nan 1.0000 0.0294
## 40 221496.8629 nan 1.0000 -1.2187
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
## 2 1.2945 nan 0.0000 0.0000
## 3 1.2945 nan 0.0000 0.0000
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## 6 1.2945 nan 0.0000 0.0000
## 7 1.2945 nan 0.0000 0.0000
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## 20 1.2945 nan 0.0000 0.0000
## 40 1.2945 nan 0.0000 0.0000
## 60 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
## 2 1.2945 nan 0.0000 0.0000
## 3 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
## 2 1.2945 nan 0.0000 0.0000
## 3 1.2945 nan 0.0000 0.0000
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## 6 1.2945 nan 0.0000 0.0000
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## 10 1.2945 nan 0.0000 0.0000
## 20 1.2945 nan 0.0000 0.0000
## 40 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
## 2 1.2945 nan 0.0000 0.0000
## 3 1.2945 nan 0.0000 0.0000
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## 6 1.2945 nan 0.0000 0.0000
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## 20 1.2945 nan 0.0000 0.0000
## 40 1.2945 nan 0.0000 0.0000
## 60 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
## 2 1.2945 nan 0.0000 0.0000
## 3 1.2945 nan 0.0000 0.0000
## 4 1.2945 nan 0.0000 0.0000
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## 6 1.2945 nan 0.0000 0.0000
## 7 1.2945 nan 0.0000 0.0000
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## 10 1.2945 nan 0.0000 0.0000
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## 40 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
## 2 1.2945 nan 0.0000 0.0000
## 3 1.2945 nan 0.0000 0.0000
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## 6 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
## 2 1.2945 nan 0.0000 0.0000
## 3 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
## 2 1.2945 nan 0.0000 0.0000
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## 6 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
## 2 1.2945 nan 0.0000 0.0000
## 3 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2942 nan 0.0010 0.0002
## 2 1.2939 nan 0.0010 0.0002
## 3 1.2935 nan 0.0010 0.0002
## 4 1.2932 nan 0.0010 0.0002
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## 6 1.2925 nan 0.0010 0.0002
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## 9 1.2915 nan 0.0010 0.0002
## 10 1.2911 nan 0.0010 0.0002
## 20 1.2877 nan 0.0010 0.0002
## 40 1.2809 nan 0.0010 0.0002
## 60 1.2744 nan 0.0010 0.0001
## 80 1.2681 nan 0.0010 0.0001
## 100 1.2622 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2942 nan 0.0010 0.0002
## 2 1.2939 nan 0.0010 0.0002
## 3 1.2935 nan 0.0010 0.0002
## 4 1.2932 nan 0.0010 0.0002
## 5 1.2928 nan 0.0010 0.0001
## 6 1.2925 nan 0.0010 0.0001
## 7 1.2921 nan 0.0010 0.0002
## 8 1.2918 nan 0.0010 0.0002
## 9 1.2914 nan 0.0010 0.0002
## 10 1.2911 nan 0.0010 0.0002
## 20 1.2874 nan 0.0010 0.0002
## 40 1.2807 nan 0.0010 0.0002
## 60 1.2741 nan 0.0010 0.0001
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## 380 1.1938 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2942 nan 0.0010 0.0002
## 2 1.2939 nan 0.0010 0.0002
## 3 1.2935 nan 0.0010 0.0002
## 4 1.2931 nan 0.0010 0.0001
## 5 1.2927 nan 0.0010 0.0002
## 6 1.2923 nan 0.0010 0.0002
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## 8 1.2916 nan 0.0010 0.0002
## 9 1.2912 nan 0.0010 0.0002
## 10 1.2909 nan 0.0010 0.0001
## 20 1.2874 nan 0.0010 0.0002
## 40 1.2809 nan 0.0010 0.0002
## 60 1.2747 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
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## 3 1.2931 nan 0.0010 0.0002
## 4 1.2927 nan 0.0010 0.0002
## 5 1.2923 nan 0.0010 0.0002
## 6 1.2918 nan 0.0010 0.0002
## 7 1.2913 nan 0.0010 0.0002
## 8 1.2908 nan 0.0010 0.0002
## 9 1.2903 nan 0.0010 0.0002
## 10 1.2899 nan 0.0010 0.0002
## 20 1.2854 nan 0.0010 0.0002
## 40 1.2766 nan 0.0010 0.0002
## 60 1.2681 nan 0.0010 0.0002
## 80 1.2596 nan 0.0010 0.0002
## 100 1.2515 nan 0.0010 0.0001
## 120 1.2438 nan 0.0010 0.0002
## 140 1.2363 nan 0.0010 0.0002
## 160 1.2288 nan 0.0010 0.0001
## 180 1.2217 nan 0.0010 0.0001
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## 220 1.2079 nan 0.0010 0.0001
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## 380 1.1600 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
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## 3 1.2931 nan 0.0010 0.0002
## 4 1.2926 nan 0.0010 0.0002
## 5 1.2922 nan 0.0010 0.0002
## 6 1.2917 nan 0.0010 0.0002
## 7 1.2913 nan 0.0010 0.0002
## 8 1.2908 nan 0.0010 0.0002
## 9 1.2903 nan 0.0010 0.0002
## 10 1.2899 nan 0.0010 0.0002
## 20 1.2854 nan 0.0010 0.0001
## 40 1.2766 nan 0.0010 0.0002
## 60 1.2680 nan 0.0010 0.0002
## 80 1.2598 nan 0.0010 0.0002
## 100 1.2518 nan 0.0010 0.0001
## 120 1.2440 nan 0.0010 0.0002
## 140 1.2366 nan 0.0010 0.0002
## 160 1.2293 nan 0.0010 0.0002
## 180 1.2221 nan 0.0010 0.0002
## 200 1.2153 nan 0.0010 0.0001
## 220 1.2085 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
## 2 1.2936 nan 0.0010 0.0002
## 3 1.2932 nan 0.0010 0.0002
## 4 1.2928 nan 0.0010 0.0002
## 5 1.2923 nan 0.0010 0.0002
## 6 1.2918 nan 0.0010 0.0002
## 7 1.2913 nan 0.0010 0.0002
## 8 1.2908 nan 0.0010 0.0002
## 9 1.2904 nan 0.0010 0.0002
## 10 1.2900 nan 0.0010 0.0002
## 20 1.2854 nan 0.0010 0.0002
## 40 1.2764 nan 0.0010 0.0002
## 60 1.2679 nan 0.0010 0.0002
## 80 1.2595 nan 0.0010 0.0002
## 100 1.2516 nan 0.0010 0.0002
## 120 1.2440 nan 0.0010 0.0002
## 140 1.2364 nan 0.0010 0.0001
## 160 1.2292 nan 0.0010 0.0001
## 180 1.2221 nan 0.0010 0.0002
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## 220 1.2082 nan 0.0010 0.0001
## 240 1.2017 nan 0.0010 0.0001
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## 380 1.1602 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
## 2 1.2935 nan 0.0010 0.0002
## 3 1.2930 nan 0.0010 0.0003
## 4 1.2924 nan 0.0010 0.0003
## 5 1.2919 nan 0.0010 0.0002
## 6 1.2914 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2903 nan 0.0010 0.0002
## 9 1.2898 nan 0.0010 0.0002
## 10 1.2893 nan 0.0010 0.0003
## 20 1.2838 nan 0.0010 0.0003
## 40 1.2734 nan 0.0010 0.0002
## 60 1.2636 nan 0.0010 0.0002
## 80 1.2538 nan 0.0010 0.0002
## 100 1.2444 nan 0.0010 0.0002
## 120 1.2352 nan 0.0010 0.0002
## 140 1.2262 nan 0.0010 0.0002
## 160 1.2177 nan 0.0010 0.0002
## 180 1.2098 nan 0.0010 0.0002
## 200 1.2019 nan 0.0010 0.0002
## 220 1.1943 nan 0.0010 0.0002
## 240 1.1869 nan 0.0010 0.0002
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## 280 1.1725 nan 0.0010 0.0001
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## 320 1.1586 nan 0.0010 0.0001
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## 380 1.1396 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
## 2 1.2935 nan 0.0010 0.0003
## 3 1.2929 nan 0.0010 0.0002
## 4 1.2924 nan 0.0010 0.0002
## 5 1.2919 nan 0.0010 0.0002
## 6 1.2915 nan 0.0010 0.0002
## 7 1.2909 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2898 nan 0.0010 0.0002
## 10 1.2892 nan 0.0010 0.0003
## 20 1.2840 nan 0.0010 0.0003
## 40 1.2738 nan 0.0010 0.0002
## 60 1.2640 nan 0.0010 0.0002
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## 160 1.2186 nan 0.0010 0.0002
## 180 1.2105 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
## 2 1.2935 nan 0.0010 0.0002
## 3 1.2929 nan 0.0010 0.0002
## 4 1.2923 nan 0.0010 0.0002
## 5 1.2918 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2902 nan 0.0010 0.0003
## 9 1.2896 nan 0.0010 0.0002
## 10 1.2891 nan 0.0010 0.0002
## 20 1.2838 nan 0.0010 0.0002
## 40 1.2735 nan 0.0010 0.0002
## 60 1.2634 nan 0.0010 0.0002
## 80 1.2541 nan 0.0010 0.0002
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## 180 1.2101 nan 0.0010 0.0002
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## 220 1.1944 nan 0.0010 0.0001
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## 280 1.1724 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2618 nan 0.1000 0.0133
## 2 1.2336 nan 0.1000 0.0130
## 3 1.2084 nan 0.1000 0.0103
## 4 1.1918 nan 0.1000 0.0066
## 5 1.1696 nan 0.1000 0.0093
## 6 1.1537 nan 0.1000 0.0084
## 7 1.1370 nan 0.1000 0.0069
## 8 1.1241 nan 0.1000 0.0048
## 9 1.1081 nan 0.1000 0.0075
## 10 1.1004 nan 0.1000 0.0003
## 20 1.0047 nan 0.1000 0.0025
## 40 0.9161 nan 0.1000 0.0011
## 60 0.8842 nan 0.1000 -0.0002
## 80 0.8608 nan 0.1000 -0.0009
## 100 0.8403 nan 0.1000 -0.0001
## 120 0.8265 nan 0.1000 -0.0000
## 140 0.8152 nan 0.1000 -0.0013
## 160 0.8060 nan 0.1000 -0.0015
## 180 0.7970 nan 0.1000 -0.0009
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## 240 0.7721 nan 0.1000 -0.0006
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## 280 0.7585 nan 0.1000 -0.0011
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## 320 0.7492 nan 0.1000 -0.0006
## 340 0.7414 nan 0.1000 -0.0015
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## 380 0.7306 nan 0.1000 -0.0010
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## 460 0.7160 nan 0.1000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2648 nan 0.1000 0.0142
## 2 1.2336 nan 0.1000 0.0133
## 3 1.2089 nan 0.1000 0.0103
## 4 1.1910 nan 0.1000 0.0093
## 5 1.1734 nan 0.1000 0.0078
## 6 1.1571 nan 0.1000 0.0071
## 7 1.1428 nan 0.1000 0.0065
## 8 1.1258 nan 0.1000 0.0067
## 9 1.1081 nan 0.1000 0.0049
## 10 1.0948 nan 0.1000 0.0058
## 20 1.0109 nan 0.1000 0.0020
## 40 0.9281 nan 0.1000 0.0009
## 60 0.8901 nan 0.1000 -0.0005
## 80 0.8662 nan 0.1000 -0.0008
## 100 0.8486 nan 0.1000 0.0000
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## 140 0.8260 nan 0.1000 -0.0005
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## 180 0.8097 nan 0.1000 -0.0015
## 200 0.8002 nan 0.1000 -0.0010
## 220 0.7917 nan 0.1000 -0.0010
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## 280 0.7709 nan 0.1000 -0.0017
## 300 0.7638 nan 0.1000 -0.0011
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## 380 0.7419 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2603 nan 0.1000 0.0158
## 2 1.2326 nan 0.1000 0.0133
## 3 1.2063 nan 0.1000 0.0101
## 4 1.1833 nan 0.1000 0.0072
## 5 1.1645 nan 0.1000 0.0074
## 6 1.1478 nan 0.1000 0.0069
## 7 1.1349 nan 0.1000 0.0056
## 8 1.1217 nan 0.1000 0.0038
## 9 1.1079 nan 0.1000 0.0034
## 10 1.0953 nan 0.1000 0.0057
## 20 1.0118 nan 0.1000 0.0027
## 40 0.9222 nan 0.1000 0.0010
## 60 0.8856 nan 0.1000 -0.0005
## 80 0.8614 nan 0.1000 -0.0010
## 100 0.8428 nan 0.1000 0.0000
## 120 0.8293 nan 0.1000 -0.0003
## 140 0.8174 nan 0.1000 -0.0013
## 160 0.8058 nan 0.1000 -0.0009
## 180 0.7974 nan 0.1000 -0.0004
## 200 0.7921 nan 0.1000 -0.0018
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## 240 0.7793 nan 0.1000 -0.0003
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## 280 0.7647 nan 0.1000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2495 nan 0.1000 0.0196
## 2 1.2167 nan 0.1000 0.0134
## 3 1.1838 nan 0.1000 0.0146
## 4 1.1540 nan 0.1000 0.0109
## 5 1.1241 nan 0.1000 0.0113
## 6 1.1013 nan 0.1000 0.0084
## 7 1.0799 nan 0.1000 0.0090
## 8 1.0617 nan 0.1000 0.0085
## 9 1.0466 nan 0.1000 0.0061
## 10 1.0329 nan 0.1000 0.0037
## 20 0.9428 nan 0.1000 0.0022
## 40 0.8613 nan 0.1000 -0.0002
## 60 0.8148 nan 0.1000 -0.0016
## 80 0.7819 nan 0.1000 -0.0019
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## 120 0.7303 nan 0.1000 -0.0012
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## 180 0.6639 nan 0.1000 -0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2474 nan 0.1000 0.0203
## 2 1.2093 nan 0.1000 0.0170
## 3 1.1764 nan 0.1000 0.0131
## 4 1.1516 nan 0.1000 0.0122
## 5 1.1249 nan 0.1000 0.0108
## 6 1.1025 nan 0.1000 0.0108
## 7 1.0858 nan 0.1000 0.0058
## 8 1.0674 nan 0.1000 0.0066
## 9 1.0520 nan 0.1000 0.0061
## 10 1.0380 nan 0.1000 0.0051
## 20 0.9359 nan 0.1000 0.0016
## 40 0.8583 nan 0.1000 -0.0014
## 60 0.8096 nan 0.1000 -0.0006
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## 100 0.7504 nan 0.1000 -0.0007
## 120 0.7313 nan 0.1000 -0.0021
## 140 0.7025 nan 0.1000 -0.0009
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## 180 0.6581 nan 0.1000 -0.0027
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2459 nan 0.1000 0.0195
## 2 1.2060 nan 0.1000 0.0164
## 3 1.1755 nan 0.1000 0.0124
## 4 1.1494 nan 0.1000 0.0089
## 5 1.1226 nan 0.1000 0.0113
## 6 1.0997 nan 0.1000 0.0088
## 7 1.0809 nan 0.1000 0.0076
## 8 1.0649 nan 0.1000 0.0067
## 9 1.0455 nan 0.1000 0.0059
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## 20 0.9391 nan 0.1000 0.0007
## 40 0.8611 nan 0.1000 -0.0007
## 60 0.8126 nan 0.1000 -0.0007
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## 140 0.7026 nan 0.1000 -0.0023
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2399 nan 0.1000 0.0247
## 2 1.1938 nan 0.1000 0.0201
## 3 1.1611 nan 0.1000 0.0150
## 4 1.1250 nan 0.1000 0.0176
## 5 1.0959 nan 0.1000 0.0105
## 6 1.0740 nan 0.1000 0.0083
## 7 1.0549 nan 0.1000 0.0052
## 8 1.0381 nan 0.1000 0.0059
## 9 1.0195 nan 0.1000 0.0059
## 10 1.0029 nan 0.1000 0.0065
## 20 0.8945 nan 0.1000 -0.0010
## 40 0.8074 nan 0.1000 0.0000
## 60 0.7545 nan 0.1000 -0.0019
## 80 0.7112 nan 0.1000 -0.0015
## 100 0.6757 nan 0.1000 -0.0030
## 120 0.6447 nan 0.1000 -0.0030
## 140 0.6114 nan 0.1000 -0.0023
## 160 0.5836 nan 0.1000 -0.0005
## 180 0.5538 nan 0.1000 -0.0003
## 200 0.5316 nan 0.1000 -0.0021
## 220 0.5082 nan 0.1000 -0.0022
## 240 0.4851 nan 0.1000 -0.0001
## 260 0.4642 nan 0.1000 -0.0008
## 280 0.4461 nan 0.1000 -0.0003
## 300 0.4266 nan 0.1000 -0.0015
## 320 0.4138 nan 0.1000 -0.0020
## 340 0.3964 nan 0.1000 -0.0010
## 360 0.3806 nan 0.1000 -0.0008
## 380 0.3666 nan 0.1000 -0.0004
## 400 0.3512 nan 0.1000 -0.0007
## 420 0.3381 nan 0.1000 -0.0007
## 440 0.3257 nan 0.1000 -0.0012
## 460 0.3126 nan 0.1000 -0.0008
## 480 0.3020 nan 0.1000 -0.0009
## 500 0.2926 nan 0.1000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2415 nan 0.1000 0.0235
## 2 1.1948 nan 0.1000 0.0207
## 3 1.1574 nan 0.1000 0.0136
## 4 1.1231 nan 0.1000 0.0138
## 5 1.0948 nan 0.1000 0.0111
## 6 1.0713 nan 0.1000 0.0069
## 7 1.0519 nan 0.1000 0.0064
## 8 1.0327 nan 0.1000 0.0073
## 9 1.0137 nan 0.1000 0.0080
## 10 0.9958 nan 0.1000 0.0051
## 20 0.8932 nan 0.1000 0.0013
## 40 0.8038 nan 0.1000 -0.0020
## 60 0.7550 nan 0.1000 -0.0031
## 80 0.7168 nan 0.1000 -0.0018
## 100 0.6780 nan 0.1000 -0.0008
## 120 0.6449 nan 0.1000 -0.0012
## 140 0.6120 nan 0.1000 -0.0020
## 160 0.5867 nan 0.1000 -0.0015
## 180 0.5626 nan 0.1000 -0.0010
## 200 0.5360 nan 0.1000 -0.0008
## 220 0.5161 nan 0.1000 -0.0010
## 240 0.4957 nan 0.1000 -0.0021
## 260 0.4719 nan 0.1000 -0.0017
## 280 0.4564 nan 0.1000 -0.0010
## 300 0.4377 nan 0.1000 -0.0008
## 320 0.4193 nan 0.1000 -0.0010
## 340 0.4047 nan 0.1000 -0.0012
## 360 0.3907 nan 0.1000 -0.0009
## 380 0.3769 nan 0.1000 -0.0008
## 400 0.3645 nan 0.1000 -0.0007
## 420 0.3499 nan 0.1000 -0.0003
## 440 0.3389 nan 0.1000 -0.0012
## 460 0.3256 nan 0.1000 -0.0012
## 480 0.3153 nan 0.1000 -0.0009
## 500 0.3026 nan 0.1000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2422 nan 0.1000 0.0234
## 2 1.1920 nan 0.1000 0.0209
## 3 1.1598 nan 0.1000 0.0134
## 4 1.1257 nan 0.1000 0.0129
## 5 1.1006 nan 0.1000 0.0100
## 6 1.0762 nan 0.1000 0.0103
## 7 1.0576 nan 0.1000 0.0067
## 8 1.0321 nan 0.1000 0.0077
## 9 1.0123 nan 0.1000 0.0080
## 10 0.9948 nan 0.1000 0.0054
## 20 0.8963 nan 0.1000 0.0005
## 40 0.8016 nan 0.1000 -0.0006
## 60 0.7492 nan 0.1000 -0.0018
## 80 0.7094 nan 0.1000 -0.0018
## 100 0.6679 nan 0.1000 -0.0004
## 120 0.6347 nan 0.1000 -0.0020
## 140 0.6067 nan 0.1000 -0.0019
## 160 0.5797 nan 0.1000 -0.0013
## 180 0.5538 nan 0.1000 -0.0008
## 200 0.5298 nan 0.1000 -0.0009
## 220 0.5027 nan 0.1000 -0.0006
## 240 0.4853 nan 0.1000 -0.0006
## 260 0.4644 nan 0.1000 -0.0017
## 280 0.4478 nan 0.1000 -0.0003
## 300 0.4304 nan 0.1000 -0.0008
## 320 0.4137 nan 0.1000 -0.0004
## 340 0.3950 nan 0.1000 -0.0011
## 360 0.3765 nan 0.1000 -0.0006
## 380 0.3610 nan 0.1000 -0.0005
## 400 0.3479 nan 0.1000 -0.0008
## 420 0.3347 nan 0.1000 -0.0004
## 440 0.3258 nan 0.1000 -0.0007
## 460 0.3138 nan 0.1000 -0.0005
## 480 0.3014 nan 0.1000 -0.0010
## 500 0.2912 nan 0.1000 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2270 nan 0.2000 0.0305
## 2 1.1838 nan 0.2000 0.0161
## 3 1.1447 nan 0.2000 0.0133
## 4 1.1095 nan 0.2000 0.0130
## 5 1.0875 nan 0.2000 0.0111
## 6 1.0680 nan 0.2000 0.0074
## 7 1.0514 nan 0.2000 0.0059
## 8 1.0335 nan 0.2000 0.0062
## 9 1.0252 nan 0.2000 0.0009
## 10 1.0109 nan 0.2000 0.0059
## 20 0.9208 nan 0.2000 0.0001
## 40 0.8652 nan 0.2000 -0.0020
## 60 0.8333 nan 0.2000 -0.0011
## 80 0.8132 nan 0.2000 -0.0017
## 100 0.7913 nan 0.2000 -0.0014
## 120 0.7788 nan 0.2000 -0.0025
## 140 0.7639 nan 0.2000 -0.0015
## 160 0.7471 nan 0.2000 -0.0012
## 180 0.7375 nan 0.2000 -0.0016
## 200 0.7247 nan 0.2000 -0.0044
## 220 0.7157 nan 0.2000 -0.0010
## 240 0.7080 nan 0.2000 -0.0021
## 260 0.7013 nan 0.2000 -0.0038
## 280 0.6971 nan 0.2000 -0.0014
## 300 0.6888 nan 0.2000 -0.0026
## 320 0.6829 nan 0.2000 -0.0014
## 340 0.6758 nan 0.2000 -0.0024
## 360 0.6663 nan 0.2000 -0.0010
## 380 0.6595 nan 0.2000 -0.0013
## 400 0.6525 nan 0.2000 -0.0020
## 420 0.6452 nan 0.2000 -0.0012
## 440 0.6398 nan 0.2000 -0.0025
## 460 0.6333 nan 0.2000 -0.0016
## 480 0.6284 nan 0.2000 -0.0016
## 500 0.6199 nan 0.2000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2311 nan 0.2000 0.0261
## 2 1.1727 nan 0.2000 0.0201
## 3 1.1384 nan 0.2000 0.0109
## 4 1.1085 nan 0.2000 0.0141
## 5 1.0841 nan 0.2000 0.0110
## 6 1.0598 nan 0.2000 0.0072
## 7 1.0395 nan 0.2000 0.0081
## 8 1.0252 nan 0.2000 0.0054
## 9 1.0099 nan 0.2000 0.0052
## 10 0.9950 nan 0.2000 0.0024
## 20 0.9204 nan 0.2000 -0.0014
## 40 0.8635 nan 0.2000 -0.0014
## 60 0.8247 nan 0.2000 -0.0035
## 80 0.7998 nan 0.2000 -0.0015
## 100 0.7886 nan 0.2000 -0.0020
## 120 0.7756 nan 0.2000 -0.0023
## 140 0.7595 nan 0.2000 -0.0021
## 160 0.7491 nan 0.2000 -0.0008
## 180 0.7414 nan 0.2000 -0.0037
## 200 0.7284 nan 0.2000 -0.0016
## 220 0.7185 nan 0.2000 -0.0014
## 240 0.7093 nan 0.2000 -0.0007
## 260 0.6977 nan 0.2000 -0.0029
## 280 0.6913 nan 0.2000 -0.0013
## 300 0.6839 nan 0.2000 -0.0011
## 320 0.6766 nan 0.2000 -0.0016
## 340 0.6672 nan 0.2000 -0.0014
## 360 0.6626 nan 0.2000 -0.0012
## 380 0.6543 nan 0.2000 -0.0021
## 400 0.6465 nan 0.2000 -0.0014
## 420 0.6451 nan 0.2000 -0.0013
## 440 0.6396 nan 0.2000 -0.0028
## 460 0.6320 nan 0.2000 -0.0015
## 480 0.6270 nan 0.2000 -0.0012
## 500 0.6227 nan 0.2000 -0.0022
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2221 nan 0.2000 0.0243
## 2 1.1855 nan 0.2000 0.0161
## 3 1.1530 nan 0.2000 0.0150
## 4 1.1177 nan 0.2000 0.0163
## 5 1.0936 nan 0.2000 0.0112
## 6 1.0710 nan 0.2000 0.0078
## 7 1.0491 nan 0.2000 0.0086
## 8 1.0317 nan 0.2000 0.0073
## 9 1.0169 nan 0.2000 0.0048
## 10 1.0022 nan 0.2000 0.0051
## 20 0.9330 nan 0.2000 -0.0011
## 40 0.8672 nan 0.2000 -0.0010
## 60 0.8455 nan 0.2000 -0.0018
## 80 0.8223 nan 0.2000 -0.0011
## 100 0.8010 nan 0.2000 -0.0027
## 120 0.7878 nan 0.2000 -0.0017
## 140 0.7753 nan 0.2000 -0.0024
## 160 0.7635 nan 0.2000 -0.0004
## 180 0.7491 nan 0.2000 -0.0026
## 200 0.7372 nan 0.2000 -0.0012
## 220 0.7262 nan 0.2000 -0.0014
## 240 0.7203 nan 0.2000 -0.0021
## 260 0.7084 nan 0.2000 -0.0028
## 280 0.7006 nan 0.2000 -0.0021
## 300 0.6912 nan 0.2000 -0.0008
## 320 0.6838 nan 0.2000 -0.0005
## 340 0.6764 nan 0.2000 -0.0013
## 360 0.6657 nan 0.2000 -0.0010
## 380 0.6609 nan 0.2000 -0.0009
## 400 0.6554 nan 0.2000 -0.0023
## 420 0.6458 nan 0.2000 -0.0003
## 440 0.6420 nan 0.2000 -0.0028
## 460 0.6327 nan 0.2000 -0.0014
## 480 0.6292 nan 0.2000 -0.0010
## 500 0.6275 nan 0.2000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2207 nan 0.2000 0.0353
## 2 1.1654 nan 0.2000 0.0283
## 3 1.1276 nan 0.2000 0.0145
## 4 1.0857 nan 0.2000 0.0104
## 5 1.0489 nan 0.2000 0.0134
## 6 1.0198 nan 0.2000 0.0127
## 7 1.0003 nan 0.2000 0.0058
## 8 0.9795 nan 0.2000 0.0067
## 9 0.9628 nan 0.2000 0.0054
## 10 0.9493 nan 0.2000 0.0035
## 20 0.8646 nan 0.2000 -0.0056
## 40 0.7808 nan 0.2000 -0.0016
## 60 0.7246 nan 0.2000 -0.0081
## 80 0.6830 nan 0.2000 -0.0014
## 100 0.6438 nan 0.2000 -0.0037
## 120 0.6052 nan 0.2000 -0.0046
## 140 0.5788 nan 0.2000 -0.0047
## 160 0.5484 nan 0.2000 -0.0015
## 180 0.5164 nan 0.2000 -0.0047
## 200 0.4921 nan 0.2000 -0.0021
## 220 0.4720 nan 0.2000 -0.0010
## 240 0.4517 nan 0.2000 -0.0016
## 260 0.4285 nan 0.2000 -0.0018
## 280 0.4111 nan 0.2000 -0.0015
## 300 0.3892 nan 0.2000 -0.0004
## 320 0.3722 nan 0.2000 -0.0002
## 340 0.3519 nan 0.2000 -0.0027
## 360 0.3366 nan 0.2000 -0.0009
## 380 0.3241 nan 0.2000 -0.0014
## 400 0.3143 nan 0.2000 -0.0018
## 420 0.3033 nan 0.2000 -0.0031
## 440 0.2927 nan 0.2000 -0.0005
## 460 0.2825 nan 0.2000 -0.0018
## 480 0.2718 nan 0.2000 -0.0010
## 500 0.2638 nan 0.2000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2164 nan 0.2000 0.0420
## 2 1.1483 nan 0.2000 0.0318
## 3 1.1008 nan 0.2000 0.0186
## 4 1.0597 nan 0.2000 0.0144
## 5 1.0247 nan 0.2000 0.0119
## 6 0.9948 nan 0.2000 0.0055
## 7 0.9769 nan 0.2000 0.0042
## 8 0.9584 nan 0.2000 0.0059
## 9 0.9412 nan 0.2000 0.0032
## 10 0.9323 nan 0.2000 -0.0006
## 20 0.8573 nan 0.2000 0.0001
## 40 0.7813 nan 0.2000 -0.0019
## 60 0.7332 nan 0.2000 -0.0020
## 80 0.6895 nan 0.2000 -0.0028
## 100 0.6538 nan 0.2000 -0.0023
## 120 0.6130 nan 0.2000 -0.0001
## 140 0.5833 nan 0.2000 -0.0000
## 160 0.5497 nan 0.2000 -0.0014
## 180 0.5285 nan 0.2000 -0.0016
## 200 0.5053 nan 0.2000 -0.0016
## 220 0.4865 nan 0.2000 -0.0013
## 240 0.4617 nan 0.2000 -0.0018
## 260 0.4410 nan 0.2000 -0.0004
## 280 0.4246 nan 0.2000 -0.0024
## 300 0.4061 nan 0.2000 -0.0029
## 320 0.3874 nan 0.2000 0.0000
## 340 0.3722 nan 0.2000 -0.0016
## 360 0.3536 nan 0.2000 -0.0003
## 380 0.3429 nan 0.2000 -0.0014
## 400 0.3325 nan 0.2000 -0.0023
## 420 0.3219 nan 0.2000 -0.0013
## 440 0.3090 nan 0.2000 -0.0014
## 460 0.2996 nan 0.2000 -0.0013
## 480 0.2860 nan 0.2000 -0.0032
## 500 0.2773 nan 0.2000 -0.0015
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2096 nan 0.2000 0.0370
## 2 1.1483 nan 0.2000 0.0292
## 3 1.1054 nan 0.2000 0.0158
## 4 1.0696 nan 0.2000 0.0120
## 5 1.0348 nan 0.2000 0.0118
## 6 1.0046 nan 0.2000 0.0111
## 7 0.9856 nan 0.2000 0.0069
## 8 0.9704 nan 0.2000 0.0043
## 9 0.9572 nan 0.2000 0.0029
## 10 0.9479 nan 0.2000 0.0022
## 20 0.8582 nan 0.2000 -0.0014
## 40 0.7792 nan 0.2000 -0.0019
## 60 0.7350 nan 0.2000 -0.0036
## 80 0.6840 nan 0.2000 -0.0021
## 100 0.6472 nan 0.2000 -0.0015
## 120 0.6115 nan 0.2000 0.0000
## 140 0.5769 nan 0.2000 -0.0005
## 160 0.5472 nan 0.2000 -0.0009
## 180 0.5232 nan 0.2000 -0.0009
## 200 0.4967 nan 0.2000 -0.0005
## 220 0.4770 nan 0.2000 -0.0012
## 240 0.4577 nan 0.2000 -0.0018
## 260 0.4380 nan 0.2000 -0.0015
## 280 0.4204 nan 0.2000 -0.0025
## 300 0.4028 nan 0.2000 -0.0012
## 320 0.3909 nan 0.2000 -0.0023
## 340 0.3762 nan 0.2000 -0.0005
## 360 0.3597 nan 0.2000 -0.0008
## 380 0.3471 nan 0.2000 -0.0009
## 400 0.3338 nan 0.2000 -0.0009
## 420 0.3237 nan 0.2000 -0.0005
## 440 0.3084 nan 0.2000 -0.0010
## 460 0.2960 nan 0.2000 -0.0012
## 480 0.2825 nan 0.2000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2086 nan 0.2000 0.0356
## 2 1.1268 nan 0.2000 0.0359
## 3 1.0815 nan 0.2000 0.0188
## 4 1.0334 nan 0.2000 0.0160
## 5 1.0012 nan 0.2000 0.0099
## 6 0.9785 nan 0.2000 0.0043
## 7 0.9688 nan 0.2000 -0.0033
## 8 0.9488 nan 0.2000 0.0018
## 9 0.9325 nan 0.2000 0.0018
## 10 0.9165 nan 0.2000 0.0029
## 20 0.8225 nan 0.2000 0.0016
## 40 0.7248 nan 0.2000 -0.0021
## 60 0.6609 nan 0.2000 -0.0033
## 80 0.6001 nan 0.2000 0.0001
## 100 0.5492 nan 0.2000 -0.0009
## 120 0.5014 nan 0.2000 -0.0009
## 140 0.4595 nan 0.2000 -0.0032
## 160 0.4240 nan 0.2000 -0.0019
## 180 0.3929 nan 0.2000 -0.0017
## 200 0.3633 nan 0.2000 -0.0013
## 220 0.3398 nan 0.2000 -0.0038
## 240 0.3150 nan 0.2000 -0.0020
## 260 0.2935 nan 0.2000 -0.0013
## 280 0.2707 nan 0.2000 -0.0013
## 300 0.2517 nan 0.2000 -0.0017
## 320 0.2364 nan 0.2000 -0.0015
## 340 0.2204 nan 0.2000 -0.0009
## 360 0.2069 nan 0.2000 -0.0011
## 380 0.1956 nan 0.2000 -0.0007
## 400 0.1851 nan 0.2000 -0.0008
## 420 0.1722 nan 0.2000 -0.0010
## 440 0.1609 nan 0.2000 -0.0006
## 460 0.1522 nan 0.2000 -0.0004
## 480 0.1460 nan 0.2000 -0.0006
## 500 0.1379 nan 0.2000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1910 nan 0.2000 0.0473
## 2 1.1085 nan 0.2000 0.0326
## 3 1.0547 nan 0.2000 0.0234
## 4 1.0162 nan 0.2000 0.0127
## 5 0.9854 nan 0.2000 0.0087
## 6 0.9647 nan 0.2000 0.0027
## 7 0.9471 nan 0.2000 0.0026
## 8 0.9276 nan 0.2000 0.0034
## 9 0.9110 nan 0.2000 0.0038
## 10 0.8958 nan 0.2000 0.0004
## 20 0.8030 nan 0.2000 0.0018
## 40 0.6944 nan 0.2000 -0.0019
## 60 0.6376 nan 0.2000 -0.0023
## 80 0.5841 nan 0.2000 -0.0035
## 100 0.5370 nan 0.2000 -0.0025
## 120 0.4859 nan 0.2000 -0.0030
## 140 0.4398 nan 0.2000 -0.0013
## 160 0.4035 nan 0.2000 -0.0026
## 180 0.3726 nan 0.2000 -0.0011
## 200 0.3482 nan 0.2000 -0.0008
## 220 0.3266 nan 0.2000 -0.0018
## 240 0.3016 nan 0.2000 -0.0012
## 260 0.2808 nan 0.2000 -0.0009
## 280 0.2682 nan 0.2000 -0.0020
## 300 0.2529 nan 0.2000 -0.0009
## 320 0.2401 nan 0.2000 -0.0012
## 340 0.2214 nan 0.2000 -0.0010
## 360 0.2045 nan 0.2000 -0.0005
## 380 0.1895 nan 0.2000 -0.0008
## 400 0.1785 nan 0.2000 -0.0018
## 420 0.1656 nan 0.2000 -0.0018
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## 460 0.1464 nan 0.2000 -0.0005
## 480 0.1380 nan 0.2000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1986 nan 0.2000 0.0476
## 2 1.1355 nan 0.2000 0.0240
## 3 1.0672 nan 0.2000 0.0233
## 4 1.0262 nan 0.2000 0.0123
## 5 0.9921 nan 0.2000 0.0105
## 6 0.9633 nan 0.2000 0.0102
## 7 0.9397 nan 0.2000 0.0041
## 8 0.9229 nan 0.2000 0.0037
## 9 0.9109 nan 0.2000 -0.0001
## 10 0.9001 nan 0.2000 -0.0001
## 20 0.7986 nan 0.2000 -0.0012
## 40 0.7077 nan 0.2000 -0.0009
## 60 0.6300 nan 0.2000 -0.0040
## 80 0.5843 nan 0.2000 -0.0029
## 100 0.5398 nan 0.2000 -0.0019
## 120 0.4969 nan 0.2000 -0.0025
## 140 0.4592 nan 0.2000 -0.0010
## 160 0.4266 nan 0.2000 -0.0025
## 180 0.3956 nan 0.2000 -0.0020
## 200 0.3735 nan 0.2000 -0.0031
## 220 0.3480 nan 0.2000 -0.0022
## 240 0.3192 nan 0.2000 -0.0014
## 260 0.2941 nan 0.2000 -0.0016
## 280 0.2720 nan 0.2000 -0.0010
## 300 0.2509 nan 0.2000 -0.0014
## 320 0.2366 nan 0.2000 -0.0008
## 340 0.2222 nan 0.2000 -0.0009
## 360 0.2087 nan 0.2000 -0.0015
## 380 0.1955 nan 0.2000 -0.0006
## 400 0.1843 nan 0.2000 -0.0015
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## 460 0.1501 nan 0.2000 -0.0018
## 480 0.1408 nan 0.2000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1994 nan 0.3000 0.0392
## 2 1.1479 nan 0.3000 0.0223
## 3 1.1048 nan 0.3000 0.0173
## 4 1.0715 nan 0.3000 0.0130
## 5 1.0467 nan 0.3000 0.0075
## 6 1.0232 nan 0.3000 0.0074
## 7 1.0112 nan 0.3000 0.0015
## 8 0.9974 nan 0.3000 0.0050
## 9 0.9843 nan 0.3000 0.0015
## 10 0.9658 nan 0.3000 0.0038
## 20 0.8902 nan 0.3000 -0.0012
## 40 0.8522 nan 0.3000 -0.0023
## 60 0.8188 nan 0.3000 -0.0045
## 80 0.8006 nan 0.3000 -0.0018
## 100 0.7790 nan 0.3000 -0.0028
## 120 0.7658 nan 0.3000 -0.0011
## 140 0.7550 nan 0.3000 -0.0019
## 160 0.7448 nan 0.3000 -0.0006
## 180 0.7299 nan 0.3000 -0.0022
## 200 0.7207 nan 0.3000 -0.0043
## 220 0.7032 nan 0.3000 -0.0020
## 240 0.6918 nan 0.3000 -0.0042
## 260 0.6811 nan 0.3000 -0.0013
## 280 0.6728 nan 0.3000 -0.0019
## 300 0.6631 nan 0.3000 -0.0022
## 320 0.6509 nan 0.3000 -0.0020
## 340 0.6472 nan 0.3000 -0.0027
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## 440 0.6085 nan 0.3000 -0.0018
## 460 0.6023 nan 0.3000 -0.0012
## 480 0.5920 nan 0.3000 -0.0046
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2012 nan 0.3000 0.0383
## 2 1.1505 nan 0.3000 0.0121
## 3 1.1123 nan 0.3000 0.0143
## 4 1.0701 nan 0.3000 0.0124
## 5 1.0506 nan 0.3000 0.0039
## 6 1.0235 nan 0.3000 0.0103
## 7 0.9999 nan 0.3000 0.0063
## 8 0.9805 nan 0.3000 0.0038
## 9 0.9698 nan 0.3000 0.0033
## 10 0.9558 nan 0.3000 0.0001
## 20 0.9044 nan 0.3000 -0.0008
## 40 0.8451 nan 0.3000 -0.0059
## 60 0.8084 nan 0.3000 -0.0044
## 80 0.7912 nan 0.3000 -0.0030
## 100 0.7757 nan 0.3000 -0.0054
## 120 0.7569 nan 0.3000 -0.0034
## 140 0.7396 nan 0.3000 -0.0020
## 160 0.7205 nan 0.3000 -0.0009
## 180 0.7067 nan 0.3000 -0.0033
## 200 0.6948 nan 0.3000 -0.0046
## 220 0.6893 nan 0.3000 -0.0033
## 240 0.6705 nan 0.3000 -0.0007
## 260 0.6582 nan 0.3000 -0.0028
## 280 0.6494 nan 0.3000 -0.0026
## 300 0.6413 nan 0.3000 -0.0014
## 320 0.6359 nan 0.3000 -0.0053
## 340 0.6274 nan 0.3000 -0.0042
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## 380 0.6208 nan 0.3000 -0.0029
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## 440 0.5987 nan 0.3000 -0.0016
## 460 0.5912 nan 0.3000 -0.0005
## 480 0.5875 nan 0.3000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2018 nan 0.3000 0.0352
## 2 1.1455 nan 0.3000 0.0222
## 3 1.1070 nan 0.3000 0.0130
## 4 1.0701 nan 0.3000 0.0151
## 5 1.0418 nan 0.3000 0.0093
## 6 1.0278 nan 0.3000 0.0039
## 7 1.0073 nan 0.3000 0.0096
## 8 0.9958 nan 0.3000 -0.0000
## 9 0.9829 nan 0.3000 0.0023
## 10 0.9691 nan 0.3000 0.0005
## 20 0.8963 nan 0.3000 -0.0039
## 40 0.8419 nan 0.3000 0.0005
## 60 0.8112 nan 0.3000 -0.0024
## 80 0.7944 nan 0.3000 -0.0038
## 100 0.7802 nan 0.3000 -0.0055
## 120 0.7664 nan 0.3000 -0.0039
## 140 0.7574 nan 0.3000 -0.0028
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## 180 0.7368 nan 0.3000 -0.0048
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## 220 0.7061 nan 0.3000 -0.0041
## 240 0.6938 nan 0.3000 -0.0019
## 260 0.6832 nan 0.3000 -0.0010
## 280 0.6726 nan 0.3000 -0.0036
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## 320 0.6509 nan 0.3000 -0.0026
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## 380 0.6343 nan 0.3000 -0.0021
## 400 0.6254 nan 0.3000 -0.0018
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## 460 0.6040 nan 0.3000 -0.0014
## 480 0.5921 nan 0.3000 -0.0024
## 500 0.5811 nan 0.3000 -0.0020
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1782 nan 0.3000 0.0503
## 2 1.0911 nan 0.3000 0.0276
## 3 1.0552 nan 0.3000 0.0091
## 4 1.0128 nan 0.3000 0.0168
## 5 0.9795 nan 0.3000 0.0097
## 6 0.9647 nan 0.3000 -0.0035
## 7 0.9387 nan 0.3000 0.0064
## 8 0.9217 nan 0.3000 0.0061
## 9 0.9060 nan 0.3000 0.0040
## 10 0.8941 nan 0.3000 -0.0005
## 20 0.8227 nan 0.3000 -0.0031
## 40 0.7410 nan 0.3000 -0.0036
## 60 0.6751 nan 0.3000 -0.0024
## 80 0.6243 nan 0.3000 0.0003
## 100 0.5786 nan 0.3000 -0.0063
## 120 0.5300 nan 0.3000 -0.0026
## 140 0.4948 nan 0.3000 -0.0040
## 160 0.4618 nan 0.3000 -0.0002
## 180 0.4340 nan 0.3000 -0.0022
## 200 0.4124 nan 0.3000 -0.0016
## 220 0.3912 nan 0.3000 -0.0027
## 240 0.3665 nan 0.3000 -0.0000
## 260 0.3433 nan 0.3000 -0.0031
## 280 0.3272 nan 0.3000 -0.0013
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## 320 0.2939 nan 0.3000 -0.0023
## 340 0.2754 nan 0.3000 -0.0022
## 360 0.2625 nan 0.3000 -0.0003
## 380 0.2521 nan 0.3000 -0.0012
## 400 0.2362 nan 0.3000 -0.0017
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## 440 0.2150 nan 0.3000 -0.0012
## 460 0.2037 nan 0.3000 -0.0026
## 480 0.1931 nan 0.3000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1688 nan 0.3000 0.0563
## 2 1.1092 nan 0.3000 0.0194
## 3 1.0578 nan 0.3000 0.0174
## 4 1.0072 nan 0.3000 0.0195
## 5 0.9725 nan 0.3000 0.0109
## 6 0.9547 nan 0.3000 0.0064
## 7 0.9412 nan 0.3000 0.0006
## 8 0.9271 nan 0.3000 -0.0003
## 9 0.9130 nan 0.3000 0.0019
## 10 0.8967 nan 0.3000 0.0007
## 20 0.8327 nan 0.3000 -0.0033
## 40 0.7458 nan 0.3000 -0.0055
## 60 0.6953 nan 0.3000 -0.0057
## 80 0.6456 nan 0.3000 -0.0030
## 100 0.6060 nan 0.3000 -0.0003
## 120 0.5747 nan 0.3000 -0.0061
## 140 0.5261 nan 0.3000 -0.0031
## 160 0.4886 nan 0.3000 -0.0042
## 180 0.4621 nan 0.3000 -0.0042
## 200 0.4279 nan 0.3000 -0.0022
## 220 0.4031 nan 0.3000 -0.0041
## 240 0.3767 nan 0.3000 -0.0047
## 260 0.3594 nan 0.3000 -0.0029
## 280 0.3332 nan 0.3000 -0.0015
## 300 0.3157 nan 0.3000 -0.0006
## 320 0.2954 nan 0.3000 -0.0001
## 340 0.2795 nan 0.3000 -0.0018
## 360 0.2637 nan 0.3000 -0.0010
## 380 0.2511 nan 0.3000 -0.0025
## 400 0.2390 nan 0.3000 -0.0005
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## 440 0.2224 nan 0.3000 -0.0037
## 460 0.2088 nan 0.3000 -0.0005
## 480 0.1990 nan 0.3000 -0.0028
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1844 nan 0.3000 0.0577
## 2 1.0993 nan 0.3000 0.0414
## 3 1.0395 nan 0.3000 0.0202
## 4 1.0023 nan 0.3000 0.0158
## 5 0.9677 nan 0.3000 0.0043
## 6 0.9545 nan 0.3000 -0.0029
## 7 0.9331 nan 0.3000 0.0048
## 8 0.9058 nan 0.3000 0.0065
## 9 0.9005 nan 0.3000 -0.0066
## 10 0.8880 nan 0.3000 -0.0033
## 20 0.8142 nan 0.3000 -0.0093
## 40 0.7366 nan 0.3000 -0.0008
## 60 0.6809 nan 0.3000 -0.0024
## 80 0.6240 nan 0.3000 -0.0060
## 100 0.5703 nan 0.3000 -0.0037
## 120 0.5306 nan 0.3000 -0.0046
## 140 0.5045 nan 0.3000 -0.0066
## 160 0.4647 nan 0.3000 -0.0016
## 180 0.4296 nan 0.3000 -0.0029
## 200 0.4022 nan 0.3000 -0.0028
## 220 0.3797 nan 0.3000 -0.0025
## 240 0.3592 nan 0.3000 -0.0017
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## 280 0.3222 nan 0.3000 -0.0050
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## 320 0.2854 nan 0.3000 -0.0018
## 340 0.2678 nan 0.3000 -0.0010
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## 380 0.2326 nan 0.3000 -0.0018
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## 440 0.2036 nan 0.3000 0.0000
## 460 0.1918 nan 0.3000 -0.0006
## 480 0.1812 nan 0.3000 -0.0013
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1476 nan 0.3000 0.0681
## 2 1.0616 nan 0.3000 0.0247
## 3 1.0126 nan 0.3000 0.0141
## 4 0.9776 nan 0.3000 0.0066
## 5 0.9401 nan 0.3000 0.0136
## 6 0.9138 nan 0.3000 0.0012
## 7 0.8966 nan 0.3000 -0.0008
## 8 0.8866 nan 0.3000 -0.0082
## 9 0.8703 nan 0.3000 -0.0003
## 10 0.8575 nan 0.3000 -0.0029
## 20 0.7437 nan 0.3000 -0.0027
## 40 0.6372 nan 0.3000 -0.0048
## 60 0.5648 nan 0.3000 -0.0061
## 80 0.5144 nan 0.3000 -0.0030
## 100 0.4585 nan 0.3000 -0.0037
## 120 0.4003 nan 0.3000 -0.0033
## 140 0.3517 nan 0.3000 -0.0019
## 160 0.3055 nan 0.3000 -0.0033
## 180 0.2731 nan 0.3000 -0.0015
## 200 0.2436 nan 0.3000 -0.0015
## 220 0.2230 nan 0.3000 -0.0024
## 240 0.2038 nan 0.3000 -0.0005
## 260 0.1848 nan 0.3000 -0.0036
## 280 0.1670 nan 0.3000 -0.0030
## 300 0.1511 nan 0.3000 -0.0011
## 320 0.1353 nan 0.3000 -0.0012
## 340 0.1271 nan 0.3000 -0.0009
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## 380 0.1082 nan 0.3000 -0.0015
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## 420 0.0916 nan 0.3000 -0.0006
## 440 0.0851 nan 0.3000 -0.0004
## 460 0.0794 nan 0.3000 -0.0011
## 480 0.0731 nan 0.3000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1660 nan 0.3000 0.0457
## 2 1.0853 nan 0.3000 0.0233
## 3 1.0134 nan 0.3000 0.0161
## 4 0.9784 nan 0.3000 0.0080
## 5 0.9512 nan 0.3000 -0.0012
## 6 0.9264 nan 0.3000 0.0014
## 7 0.8971 nan 0.3000 0.0013
## 8 0.8839 nan 0.3000 -0.0048
## 9 0.8706 nan 0.3000 0.0001
## 10 0.8593 nan 0.3000 -0.0001
## 20 0.7767 nan 0.3000 -0.0034
## 40 0.6587 nan 0.3000 -0.0057
## 60 0.5627 nan 0.3000 -0.0061
## 80 0.5069 nan 0.3000 -0.0033
## 100 0.4359 nan 0.3000 -0.0027
## 120 0.3885 nan 0.3000 -0.0050
## 140 0.3435 nan 0.3000 -0.0043
## 160 0.3013 nan 0.3000 -0.0023
## 180 0.2680 nan 0.3000 -0.0028
## 200 0.2429 nan 0.3000 -0.0023
## 220 0.2203 nan 0.3000 -0.0015
## 240 0.1983 nan 0.3000 -0.0027
## 260 0.1819 nan 0.3000 -0.0009
## 280 0.1654 nan 0.3000 -0.0032
## 300 0.1484 nan 0.3000 -0.0015
## 320 0.1354 nan 0.3000 -0.0007
## 340 0.1215 nan 0.3000 -0.0008
## 360 0.1120 nan 0.3000 -0.0006
## 380 0.1026 nan 0.3000 -0.0005
## 400 0.0922 nan 0.3000 -0.0005
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## 440 0.0793 nan 0.3000 -0.0004
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## 480 0.0683 nan 0.3000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1488 nan 0.3000 0.0598
## 2 1.0641 nan 0.3000 0.0376
## 3 1.0132 nan 0.3000 0.0202
## 4 0.9652 nan 0.3000 0.0198
## 5 0.9383 nan 0.3000 0.0065
## 6 0.9178 nan 0.3000 0.0034
## 7 0.9014 nan 0.3000 -0.0014
## 8 0.8793 nan 0.3000 0.0004
## 9 0.8713 nan 0.3000 -0.0070
## 10 0.8543 nan 0.3000 0.0010
## 20 0.7619 nan 0.3000 -0.0045
## 40 0.6628 nan 0.3000 -0.0081
## 60 0.5812 nan 0.3000 -0.0011
## 80 0.5204 nan 0.3000 -0.0041
## 100 0.4500 nan 0.3000 -0.0009
## 120 0.4007 nan 0.3000 -0.0017
## 140 0.3575 nan 0.3000 -0.0027
## 160 0.3186 nan 0.3000 -0.0030
## 180 0.2886 nan 0.3000 -0.0039
## 200 0.2575 nan 0.3000 -0.0030
## 220 0.2267 nan 0.3000 -0.0007
## 240 0.2085 nan 0.3000 -0.0015
## 260 0.1879 nan 0.3000 -0.0003
## 280 0.1671 nan 0.3000 -0.0014
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## 320 0.1437 nan 0.3000 -0.0010
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## 380 0.1084 nan 0.3000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1633 nan 0.5000 0.0582
## 2 1.1076 nan 0.5000 0.0169
## 3 1.0623 nan 0.5000 0.0135
## 4 1.0077 nan 0.5000 0.0190
## 5 0.9813 nan 0.5000 0.0120
## 6 0.9656 nan 0.5000 -0.0026
## 7 0.9421 nan 0.5000 0.0051
## 8 0.9357 nan 0.5000 0.0015
## 9 0.9308 nan 0.5000 -0.0017
## 10 0.9199 nan 0.5000 -0.0014
## 20 0.8690 nan 0.5000 -0.0016
## 40 0.8266 nan 0.5000 -0.0026
## 60 0.8021 nan 0.5000 -0.0062
## 80 0.7814 nan 0.5000 -0.0040
## 100 0.7547 nan 0.5000 -0.0069
## 120 0.7355 nan 0.5000 -0.0086
## 140 0.7159 nan 0.5000 -0.0061
## 160 0.6935 nan 0.5000 -0.0041
## 180 0.6786 nan 0.5000 -0.0030
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## 280 0.6151 nan 0.5000 -0.0072
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## 460 0.5099 nan 0.5000 -0.0037
## 480 0.5039 nan 0.5000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1640 nan 0.5000 0.0669
## 2 1.1164 nan 0.5000 0.0124
## 3 1.0618 nan 0.5000 0.0220
## 4 1.0149 nan 0.5000 0.0179
## 5 0.9853 nan 0.5000 0.0101
## 6 0.9690 nan 0.5000 -0.0016
## 7 0.9586 nan 0.5000 -0.0007
## 8 0.9351 nan 0.5000 0.0086
## 9 0.9269 nan 0.5000 -0.0048
## 10 0.9194 nan 0.5000 -0.0044
## 20 0.8635 nan 0.5000 0.0006
## 40 0.8228 nan 0.5000 -0.0039
## 60 0.7885 nan 0.5000 -0.0045
## 80 0.7623 nan 0.5000 -0.0067
## 100 0.7490 nan 0.5000 -0.0082
## 120 0.7191 nan 0.5000 -0.0018
## 140 0.7043 nan 0.5000 -0.0043
## 160 0.6879 nan 0.5000 -0.0040
## 180 0.6734 nan 0.5000 -0.0100
## 200 0.6500 nan 0.5000 -0.0125
## 220 0.6341 nan 0.5000 -0.0027
## 240 0.6236 nan 0.5000 -0.0061
## 260 0.6078 nan 0.5000 -0.0021
## 280 0.5913 nan 0.5000 -0.0061
## 300 0.5790 nan 0.5000 -0.0033
## 320 0.5692 nan 0.5000 -0.0014
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## 380 0.5443 nan 0.5000 -0.0018
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## 460 0.5165 nan 0.5000 -0.0024
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1572 nan 0.5000 0.0629
## 2 1.0964 nan 0.5000 0.0200
## 3 1.0337 nan 0.5000 0.0200
## 4 0.9988 nan 0.5000 0.0173
## 5 0.9744 nan 0.5000 0.0111
## 6 0.9627 nan 0.5000 0.0008
## 7 0.9438 nan 0.5000 0.0055
## 8 0.9253 nan 0.5000 0.0002
## 9 0.9113 nan 0.5000 -0.0021
## 10 0.9073 nan 0.5000 -0.0007
## 20 0.8628 nan 0.5000 -0.0102
## 40 0.8077 nan 0.5000 -0.0023
## 60 0.7804 nan 0.5000 -0.0021
## 80 0.7518 nan 0.5000 -0.0072
## 100 0.7373 nan 0.5000 -0.0039
## 120 0.7327 nan 0.5000 -0.0058
## 140 0.7167 nan 0.5000 -0.0010
## 160 0.6895 nan 0.5000 0.0031
## 180 0.6834 nan 0.5000 -0.0144
## 200 0.6647 nan 0.5000 -0.0059
## 220 0.6499 nan 0.5000 -0.0051
## 240 0.6334 nan 0.5000 -0.0041
## 260 0.6135 nan 0.5000 -0.0036
## 280 0.6070 nan 0.5000 -0.0071
## 300 0.5936 nan 0.5000 -0.0027
## 320 0.5942 nan 0.5000 -0.0044
## 340 0.5897 nan 0.5000 -0.0060
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## 380 0.5731 nan 0.5000 -0.0069
## 400 0.5541 nan 0.5000 -0.0014
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## 480 0.5252 nan 0.5000 -0.0049
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1370 nan 0.5000 0.0754
## 2 1.0297 nan 0.5000 0.0477
## 3 0.9744 nan 0.5000 0.0171
## 4 0.9488 nan 0.5000 0.0048
## 5 0.9338 nan 0.5000 -0.0070
## 6 0.9136 nan 0.5000 -0.0033
## 7 0.9049 nan 0.5000 -0.0044
## 8 0.8931 nan 0.5000 -0.0114
## 9 0.8864 nan 0.5000 -0.0159
## 10 0.8688 nan 0.5000 -0.0046
## 20 0.8010 nan 0.5000 -0.0122
## 40 0.7039 nan 0.5000 -0.0110
## 60 0.5955 nan 0.5000 -0.0034
## 80 0.5529 nan 0.5000 -0.0073
## 100 0.4882 nan 0.5000 -0.0042
## 120 0.4324 nan 0.5000 -0.0064
## 140 0.3891 nan 0.5000 -0.0012
## 160 0.3545 nan 0.5000 -0.0041
## 180 0.3074 nan 0.5000 -0.0025
## 200 0.2747 nan 0.5000 -0.0009
## 220 0.2509 nan 0.5000 -0.0026
## 240 0.2210 nan 0.5000 -0.0040
## 260 0.2019 nan 0.5000 -0.0023
## 280 0.1813 nan 0.5000 -0.0015
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## 320 0.1525 nan 0.5000 -0.0000
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## 380 0.1204 nan 0.5000 -0.0031
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## 460 0.0915 nan 0.5000 -0.0003
## 480 0.0875 nan 0.5000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1042 nan 0.5000 0.0561
## 2 1.0394 nan 0.5000 0.0199
## 3 0.9942 nan 0.5000 0.0131
## 4 0.9624 nan 0.5000 0.0074
## 5 0.9453 nan 0.5000 -0.0047
## 6 0.9053 nan 0.5000 0.0082
## 7 0.9003 nan 0.5000 -0.0128
## 8 0.8864 nan 0.5000 0.0012
## 9 0.8820 nan 0.5000 -0.0103
## 10 0.8690 nan 0.5000 -0.0021
## 20 0.7985 nan 0.5000 -0.0058
## 40 0.6927 nan 0.5000 -0.0045
## 60 0.6120 nan 0.5000 -0.0083
## 80 0.5625 nan 0.5000 -0.0051
## 100 0.4915 nan 0.5000 -0.0025
## 120 0.4469 nan 0.5000 -0.0055
## 140 0.3953 nan 0.5000 -0.0034
## 160 0.3545 nan 0.5000 -0.0058
## 180 0.3307 nan 0.5000 -0.0028
## 200 0.3027 nan 0.5000 -0.0066
## 220 0.2804 nan 0.5000 -0.0070
## 240 0.2471 nan 0.5000 -0.0036
## 260 0.2279 nan 0.5000 -0.0052
## 280 0.2034 nan 0.5000 -0.0035
## 300 0.1802 nan 0.5000 -0.0028
## 320 0.1642 nan 0.5000 -0.0002
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## 480 0.0835 nan 0.5000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1326 nan 0.5000 0.0525
## 2 1.0410 nan 0.5000 0.0435
## 3 0.9923 nan 0.5000 0.0108
## 4 0.9619 nan 0.5000 0.0036
## 5 0.9382 nan 0.5000 -0.0053
## 6 0.9161 nan 0.5000 0.0023
## 7 0.8854 nan 0.5000 0.0107
## 8 0.8701 nan 0.5000 -0.0018
## 9 0.8651 nan 0.5000 -0.0111
## 10 0.8611 nan 0.5000 -0.0091
## 20 0.7969 nan 0.5000 -0.0035
## 40 0.6945 nan 0.5000 -0.0033
## 60 0.6335 nan 0.5000 -0.0161
## 80 0.5533 nan 0.5000 -0.0068
## 100 0.5088 nan 0.5000 -0.0058
## 120 0.4539 nan 0.5000 -0.0064
## 140 0.4380 nan 0.5000 -0.0097
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## 180 0.3546 nan 0.5000 -0.0082
## 200 0.3225 nan 0.5000 -0.0059
## 220 0.2876 nan 0.5000 -0.0015
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## 280 0.2173 nan 0.5000 -0.0046
## 300 0.1967 nan 0.5000 -0.0011
## 320 0.1816 nan 0.5000 -0.0029
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## 480 0.0952 nan 0.5000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1018 nan 0.5000 0.0837
## 2 1.0002 nan 0.5000 0.0386
## 3 0.9592 nan 0.5000 0.0017
## 4 0.9250 nan 0.5000 0.0094
## 5 0.9038 nan 0.5000 -0.0076
## 6 0.8880 nan 0.5000 -0.0115
## 7 0.8605 nan 0.5000 -0.0066
## 8 0.8464 nan 0.5000 -0.0109
## 9 0.8327 nan 0.5000 -0.0138
## 10 0.8296 nan 0.5000 -0.0146
## 20 0.7554 nan 0.5000 -0.0259
## 40 13569.6981 nan 0.5000 -0.0077
## 60 13598.8695 nan 0.5000 -0.0002
## 80 13598.7847 nan 0.5000 -0.0003
## 100 inf nan 0.5000 -0.0183
## 120 inf nan 0.5000 -0.0015
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 7278132543442879774820.0000 nan 0.5000 inf
## 200 7278132543442879774820.0000 nan 0.5000 0.0001
## 220 7278132543442879774820.0000 nan 0.5000 -0.0001
## 240 7278132543442879774820.0000 nan 0.5000 -0.0110
## 260 7278132543442879774820.0000 nan 0.5000 -0.0001
## 280 7278132543442879774820.0000 nan 0.5000 -0.0020
## 300 7278132543442879774820.0000 nan 0.5000 -0.0006
## 320 7278132543442879774820.0000 nan 0.5000 0.0000
## 340 7278132543442879774820.0000 nan 0.5000 -0.0007
## 360 7278132543442879774820.0000 nan 0.5000 0.0000
## 380 7278132543442879774820.0000 nan 0.5000 0.0004
## 400 7278132543442879774820.0000 nan 0.5000 -0.0025
## 420 7278132543442879774820.0000 nan 0.5000 -0.0009
## 440 7278132543442879774820.0000 nan 0.5000 -0.0002
## 460 7278132543442879774820.0000 nan 0.5000 -0.0006
## 480 7278132543442879774820.0000 nan 0.5000 -0.0001
## 500 7278132543442879774820.0000 nan 0.5000 -0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1187 nan 0.5000 0.0625
## 2 0.9977 nan 0.5000 0.0372
## 3 0.9664 nan 0.5000 -0.0017
## 4 0.9414 nan 0.5000 -0.0048
## 5 0.9081 nan 0.5000 0.0134
## 6 0.8969 nan 0.5000 -0.0163
## 7 0.8762 nan 0.5000 -0.0082
## 8 0.8475 nan 0.5000 0.0056
## 9 0.8225 nan 0.5000 -0.0030
## 10 0.8136 nan 0.5000 -0.0075
## 20 0.7423 nan 0.5000 -0.0107
## 40 0.5888 nan 0.5000 -0.0015
## 60 0.4832 nan 0.5000 -0.0053
## 80 0.4202 nan 0.5000 -0.0072
## 100 0.3535 nan 0.5000 -0.0059
## 120 0.2869 nan 0.5000 -0.0065
## 140 0.2248 nan 0.5000 0.0001
## 160 0.1949 nan 0.5000 -0.0023
## 180 0.1726 nan 0.5000 -0.0004
## 200 0.1464 nan 0.5000 -0.0030
## 220 0.1276 nan 0.5000 -0.0035
## 240 0.1141 nan 0.5000 -0.0002
## 260 0.0984 nan 0.5000 -0.0013
## 280 0.0852 nan 0.5000 -0.0011
## 300 0.0748 nan 0.5000 -0.0004
## 320 0.0671 nan 0.5000 -0.0014
## 340 0.0589 nan 0.5000 -0.0004
## 360 0.0522 nan 0.5000 -0.0004
## 380 0.0459 nan 0.5000 -0.0011
## 400 0.0399 nan 0.5000 -0.0004
## 420 0.0350 nan 0.5000 -0.0003
## 440 0.0319 nan 0.5000 -0.0004
## 460 0.0274 nan 0.5000 -0.0007
## 480 0.0247 nan 0.5000 -0.0001
## 500 0.0223 nan 0.5000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0992 nan 0.5000 0.0751
## 2 1.0073 nan 0.5000 0.0333
## 3 0.9478 nan 0.5000 0.0231
## 4 0.9177 nan 0.5000 0.0040
## 5 0.8962 nan 0.5000 0.0006
## 6 0.8896 nan 0.5000 -0.0181
## 7 0.8670 nan 0.5000 -0.0067
## 8 0.8402 nan 0.5000 -0.0024
## 9 0.8316 nan 0.5000 -0.0161
## 10 0.8122 nan 0.5000 -0.0124
## 20 0.7193 nan 0.5000 -0.0056
## 40 0.6159 nan 0.5000 -0.0080
## 60 0.5024 nan 0.5000 -0.0077
## 80 0.4348 nan 0.5000 -0.0032
## 100 0.3516 nan 0.5000 -0.0075
## 120 0.2744 nan 0.5000 -0.0057
## 140 0.2238 nan 0.5000 -0.0042
## 160 0.1828 nan 0.5000 -0.0031
## 180 0.1571 nan 0.5000 -0.0011
## 200 0.1347 nan 0.5000 -0.0021
## 220 0.1165 nan 0.5000 -0.0004
## 240 0.1018 nan 0.5000 -0.0020
## 260 0.0875 nan 0.5000 -0.0010
## 280 0.0760 nan 0.5000 -0.0011
## 300 0.0666 nan 0.5000 -0.0011
## 320 0.0566 nan 0.5000 -0.0005
## 340 0.0504 nan 0.5000 -0.0009
## 360 0.0443 nan 0.5000 -0.0002
## 380 0.0393 nan 0.5000 -0.0004
## 400 0.0363 nan 0.5000 -0.0007
## 420 0.0326 nan 0.5000 -0.0008
## 440 0.0294 nan 0.5000 -0.0005
## 460 0.0256 nan 0.5000 -0.0008
## 480 0.0224 nan 0.5000 -0.0002
## 500 0.0201 nan 0.5000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1406 nan 1.0000 0.0651
## 2 1.0974 nan 1.0000 -0.0001
## 3 1.0463 nan 1.0000 0.0250
## 4 1.0443 nan 1.0000 -0.0258
## 5 1.0130 nan 1.0000 0.0096
## 6 1.0163 nan 1.0000 -0.0186
## 7 1.0255 nan 1.0000 -0.0200
## 8 1.0201 nan 1.0000 -0.0113
## 9 1.0248 nan 1.0000 -0.0203
## 10 1.0345 nan 1.0000 -0.0250
## 20 0.9814 nan 1.0000 -0.0109
## 40 1.1722 nan 1.0000 -0.0372
## 60 1.8367 nan 1.0000 -0.0019
## 80 5.2571 nan 1.0000 -0.0010
## 100 5.2802 nan 1.0000 -0.0007
## 120 5.2626 nan 1.0000 0.0020
## 140 5.2111 nan 1.0000 -0.0012
## 160 5.2195 nan 1.0000 0.0009
## 180 391.2125 nan 1.0000 -0.0169
## 200 4015659054.2571 nan 1.0000 -0.0098
## 220 4015659054.1409 nan 1.0000 0.0019
## 240 4015659055.6829 nan 1.0000 -0.9159
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1429 nan 1.0000 0.0533
## 2 1.0508 nan 1.0000 0.0422
## 3 0.9938 nan 1.0000 0.0085
## 4 0.9551 nan 1.0000 -0.0032
## 5 0.9580 nan 1.0000 -0.0157
## 6 0.9537 nan 1.0000 -0.0083
## 7 0.9311 nan 1.0000 -0.0004
## 8 0.9196 nan 1.0000 -0.0043
## 9 0.9182 nan 1.0000 -0.0150
## 10 0.9105 nan 1.0000 -0.0028
## 20 0.8827 nan 1.0000 -0.0084
## 40 43.3363 nan 1.0000 -0.0002
## 60 43.2636 nan 1.0000 -0.0165
## 80 57460465.6053 nan 1.0000 -0.0042
## 100 57460465.5679 nan 1.0000 -0.0047
## 120 57460465.5400 nan 1.0000 0.0014
## 140 57460467.3999 nan 1.0000 -1.8905
## 160 57460467.3755 nan 1.0000 -0.0023
## 180 57460467.3403 nan 1.0000 0.0004
## 200 inf nan 1.0000 -0.0000
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1547 nan 1.0000 0.0300
## 2 1.0975 nan 1.0000 0.0043
## 3 1.0471 nan 1.0000 0.0206
## 4 1.0037 nan 1.0000 0.0095
## 5 0.9991 nan 1.0000 -0.0065
## 6 1.0029 nan 1.0000 -0.0198
## 7 0.9890 nan 1.0000 -0.0061
## 8 0.9654 nan 1.0000 0.0071
## 9 0.9554 nan 1.0000 -0.0094
## 10 0.9558 nan 1.0000 -0.0213
## 20 0.9414 nan 1.0000 -0.0323
## 40 1.0048 nan 1.0000 -0.0075
## 60 0.9676 nan 1.0000 -0.0293
## 80 0.8901 nan 1.0000 -0.0141
## 100 78097103364806.4688 nan 1.0000 -0.0009
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
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## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 -0.0145
## 300 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0751 nan 1.0000 0.1093
## 2 1.0325 nan 1.0000 -0.0158
## 3 0.9792 nan 1.0000 0.0103
## 4 0.9767 nan 1.0000 -0.0206
## 5 0.9532 nan 1.0000 -0.0195
## 6 0.9362 nan 1.0000 -0.0154
## 7 0.9640 nan 1.0000 -0.0625
## 8 0.9345 nan 1.0000 0.0078
## 9 0.9314 nan 1.0000 -0.0204
## 10 0.9330 nan 1.0000 -0.0191
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0699 nan 1.0000 0.0836
## 2 0.9871 nan 1.0000 0.0241
## 3 0.9588 nan 1.0000 0.0007
## 4 0.9080 nan 1.0000 0.0130
## 5 0.8900 nan 1.0000 -0.0149
## 6 0.9102 nan 1.0000 -0.0433
## 7 0.9000 nan 1.0000 -0.0124
## 8 0.8929 nan 1.0000 -0.0159
## 9 0.8999 nan 1.0000 -0.0284
## 10 0.8732 nan 1.0000 -0.0041
## 20 0.8710 nan 1.0000 -0.0145
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0684 nan 1.0000 0.1026
## 2 0.9620 nan 1.0000 0.0333
## 3 0.9347 nan 1.0000 -0.0090
## 4 1.0411 nan 1.0000 -0.1455
## 5 0.9767 nan 1.0000 0.0313
## 6 0.9828 nan 1.0000 -0.0260
## 7 0.9977 nan 1.0000 0.0028
## 8 1.0153 nan 1.0000 -0.0541
## 9 1.0156 nan 1.0000 -0.0276
## 10 0.9966 nan 1.0000 -0.0061
## 20 0.9612 nan 1.0000 -0.0217
## 40 1.0398 nan 1.0000 -0.1048
## 60 0.9066 nan 1.0000 -0.0262
## 80 0.9370 nan 1.0000 -0.0075
## 100 13.8214 nan 1.0000 -13.1257
## 120 82312.9346 nan 1.0000 1.7357
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0844 nan 1.0000 0.0617
## 2 1.0530 nan 1.0000 -0.0364
## 3 1.1251 nan 1.0000 -0.1134
## 4 1.1372 nan 1.0000 -0.0766
## 5 1.1780 nan 1.0000 -0.0982
## 6 1.1202 nan 1.0000 -0.0040
## 7 1.0752 nan 1.0000 -0.0219
## 8 1.0765 nan 1.0000 -0.0797
## 9 1.0245 nan 1.0000 -0.0015
## 10 1.0541 nan 1.0000 -0.0973
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0465 nan 1.0000 0.0772
## 2 0.9910 nan 1.0000 0.0016
## 3 0.9434 nan 1.0000 0.0011
## 4 0.9342 nan 1.0000 -0.0192
## 5 0.9481 nan 1.0000 -0.0582
## 6 0.9330 nan 1.0000 -0.0226
## 7 0.9501 nan 1.0000 -0.0581
## 8 0.9412 nan 1.0000 -0.0288
## 9 0.8975 nan 1.0000 0.0034
## 10 0.8834 nan 1.0000 -0.0386
## 20 0.9797 nan 1.0000 -0.0196
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0549 nan 1.0000 0.1055
## 2 1.0463 nan 1.0000 -0.0453
## 3 1.0247 nan 1.0000 -0.0537
## 4 0.9513 nan 1.0000 0.0223
## 5 0.9510 nan 1.0000 -0.0408
## 6 0.9451 nan 1.0000 -0.0310
## 7 0.9342 nan 1.0000 -0.0185
## 8 0.9510 nan 1.0000 -0.0666
## 9 0.9350 nan 1.0000 -0.0244
## 10 0.9640 nan 1.0000 -0.0570
## 20 3.0920 nan 1.0000 -0.0487
## 40 28.6800 nan 1.0000 0.0037
## 60 187557354839.2242 nan 1.0000 -0.1835
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
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## 220 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2903 nan 0.0010 0.0002
## 9 1.2899 nan 0.0010 0.0002
## 10 1.2896 nan 0.0010 0.0002
## 20 1.2860 nan 0.0010 0.0001
## 40 1.2791 nan 0.0010 0.0002
## 60 1.2722 nan 0.0010 0.0001
## 80 1.2660 nan 0.0010 0.0002
## 100 1.2596 nan 0.0010 0.0001
## 120 1.2535 nan 0.0010 0.0001
## 140 1.2477 nan 0.0010 0.0001
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## 180 1.2362 nan 0.0010 0.0001
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## 380 1.1888 nan 0.0010 0.0001
## 400 1.1848 nan 0.0010 0.0001
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## 480 1.1694 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2927 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2905 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2790 nan 0.0010 0.0002
## 60 1.2723 nan 0.0010 0.0002
## 80 1.2658 nan 0.0010 0.0002
## 100 1.2592 nan 0.0010 0.0001
## 120 1.2531 nan 0.0010 0.0002
## 140 1.2475 nan 0.0010 0.0001
## 160 1.2417 nan 0.0010 0.0001
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## 220 1.2254 nan 0.0010 0.0001
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## 320 1.2014 nan 0.0010 0.0001
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## 380 1.1886 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2927 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2900 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2862 nan 0.0010 0.0002
## 40 1.2789 nan 0.0010 0.0002
## 60 1.2720 nan 0.0010 0.0002
## 80 1.2654 nan 0.0010 0.0002
## 100 1.2591 nan 0.0010 0.0001
## 120 1.2529 nan 0.0010 0.0002
## 140 1.2469 nan 0.0010 0.0001
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## 280 1.2109 nan 0.0010 0.0001
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## 320 1.2019 nan 0.0010 0.0001
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## 380 1.1890 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2899 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
## 9 1.2891 nan 0.0010 0.0002
## 10 1.2886 nan 0.0010 0.0002
## 20 1.2836 nan 0.0010 0.0002
## 40 1.2742 nan 0.0010 0.0002
## 60 1.2654 nan 0.0010 0.0002
## 80 1.2568 nan 0.0010 0.0002
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## 140 1.2326 nan 0.0010 0.0001
## 160 1.2250 nan 0.0010 0.0002
## 180 1.2174 nan 0.0010 0.0002
## 200 1.2102 nan 0.0010 0.0001
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## 240 1.1963 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2895 nan 0.0010 0.0002
## 9 1.2890 nan 0.0010 0.0003
## 10 1.2885 nan 0.0010 0.0002
## 20 1.2838 nan 0.0010 0.0002
## 40 1.2748 nan 0.0010 0.0002
## 60 1.2660 nan 0.0010 0.0002
## 80 1.2574 nan 0.0010 0.0001
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## 140 1.2333 nan 0.0010 0.0002
## 160 1.2257 nan 0.0010 0.0002
## 180 1.2181 nan 0.0010 0.0002
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## 220 1.2040 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2899 nan 0.0010 0.0002
## 8 1.2894 nan 0.0010 0.0002
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## 10 1.2885 nan 0.0010 0.0002
## 20 1.2837 nan 0.0010 0.0002
## 40 1.2744 nan 0.0010 0.0002
## 60 1.2655 nan 0.0010 0.0002
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## 140 1.2329 nan 0.0010 0.0002
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## 180 1.2179 nan 0.0010 0.0002
## 200 1.2108 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0003
## 4 1.2912 nan 0.0010 0.0003
## 5 1.2906 nan 0.0010 0.0003
## 6 1.2900 nan 0.0010 0.0003
## 7 1.2894 nan 0.0010 0.0002
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## 10 1.2878 nan 0.0010 0.0003
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## 40 1.2720 nan 0.0010 0.0002
## 60 1.2614 nan 0.0010 0.0003
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## 180 1.2061 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2907 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2895 nan 0.0010 0.0003
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## 10 1.2879 nan 0.0010 0.0003
## 20 1.2824 nan 0.0010 0.0002
## 40 1.2719 nan 0.0010 0.0003
## 60 1.2616 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2916 nan 0.0010 0.0003
## 4 1.2911 nan 0.0010 0.0002
## 5 1.2905 nan 0.0010 0.0003
## 6 1.2899 nan 0.0010 0.0003
## 7 1.2893 nan 0.0010 0.0002
## 8 1.2887 nan 0.0010 0.0002
## 9 1.2882 nan 0.0010 0.0002
## 10 1.2876 nan 0.0010 0.0002
## 20 1.2820 nan 0.0010 0.0003
## 40 1.2715 nan 0.0010 0.0002
## 60 1.2611 nan 0.0010 0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2608 nan 0.1000 0.0163
## 2 1.2300 nan 0.1000 0.0138
## 3 1.2035 nan 0.1000 0.0120
## 4 1.1821 nan 0.1000 0.0093
## 5 1.1659 nan 0.1000 0.0076
## 6 1.1478 nan 0.1000 0.0077
## 7 1.1344 nan 0.1000 0.0055
## 8 1.1181 nan 0.1000 0.0071
## 9 1.1042 nan 0.1000 0.0066
## 10 1.0902 nan 0.1000 0.0054
## 20 0.9984 nan 0.1000 0.0023
## 40 0.9181 nan 0.1000 0.0004
## 60 0.8754 nan 0.1000 0.0005
## 80 0.8489 nan 0.1000 -0.0009
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## 140 0.8017 nan 0.1000 -0.0009
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## 180 0.7827 nan 0.1000 -0.0015
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## 460 0.6853 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2628 nan 0.1000 0.0143
## 2 1.2303 nan 0.1000 0.0149
## 3 1.2054 nan 0.1000 0.0118
## 4 1.1815 nan 0.1000 0.0098
## 5 1.1639 nan 0.1000 0.0065
## 6 1.1484 nan 0.1000 0.0063
## 7 1.1338 nan 0.1000 0.0061
## 8 1.1168 nan 0.1000 0.0049
## 9 1.1063 nan 0.1000 0.0033
## 10 1.0949 nan 0.1000 0.0046
## 20 1.0037 nan 0.1000 0.0026
## 40 0.9192 nan 0.1000 0.0003
## 60 0.8749 nan 0.1000 -0.0006
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## 180 0.7802 nan 0.1000 -0.0006
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## 460 0.6944 nan 0.1000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2574 nan 0.1000 0.0166
## 2 1.2253 nan 0.1000 0.0133
## 3 1.2007 nan 0.1000 0.0108
## 4 1.1815 nan 0.1000 0.0073
## 5 1.1603 nan 0.1000 0.0073
## 6 1.1422 nan 0.1000 0.0078
## 7 1.1283 nan 0.1000 0.0074
## 8 1.1127 nan 0.1000 0.0054
## 9 1.0965 nan 0.1000 0.0059
## 10 1.0856 nan 0.1000 0.0045
## 20 1.0010 nan 0.1000 0.0021
## 40 0.9209 nan 0.1000 -0.0003
## 60 0.8805 nan 0.1000 0.0002
## 80 0.8467 nan 0.1000 -0.0013
## 100 0.8289 nan 0.1000 -0.0009
## 120 0.8135 nan 0.1000 -0.0004
## 140 0.8022 nan 0.1000 -0.0009
## 160 0.7918 nan 0.1000 -0.0006
## 180 0.7815 nan 0.1000 -0.0004
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## 220 0.7614 nan 0.1000 -0.0007
## 240 0.7535 nan 0.1000 -0.0004
## 260 0.7459 nan 0.1000 -0.0007
## 280 0.7397 nan 0.1000 -0.0007
## 300 0.7329 nan 0.1000 -0.0011
## 320 0.7250 nan 0.1000 -0.0011
## 340 0.7193 nan 0.1000 -0.0005
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## 380 0.7077 nan 0.1000 -0.0006
## 400 0.7025 nan 0.1000 -0.0013
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## 440 0.6928 nan 0.1000 -0.0015
## 460 0.6874 nan 0.1000 -0.0010
## 480 0.6830 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2463 nan 0.1000 0.0195
## 2 1.2079 nan 0.1000 0.0171
## 3 1.1774 nan 0.1000 0.0146
## 4 1.1429 nan 0.1000 0.0115
## 5 1.1214 nan 0.1000 0.0090
## 6 1.1028 nan 0.1000 0.0044
## 7 1.0820 nan 0.1000 0.0057
## 8 1.0634 nan 0.1000 0.0087
## 9 1.0474 nan 0.1000 0.0056
## 10 1.0307 nan 0.1000 0.0063
## 20 0.9393 nan 0.1000 0.0018
## 40 0.8499 nan 0.1000 -0.0000
## 60 0.7997 nan 0.1000 -0.0021
## 80 0.7607 nan 0.1000 -0.0026
## 100 0.7264 nan 0.1000 -0.0008
## 120 0.7039 nan 0.1000 -0.0010
## 140 0.6840 nan 0.1000 -0.0009
## 160 0.6637 nan 0.1000 -0.0017
## 180 0.6442 nan 0.1000 -0.0012
## 200 0.6281 nan 0.1000 -0.0010
## 220 0.6133 nan 0.1000 -0.0019
## 240 0.5944 nan 0.1000 -0.0011
## 260 0.5816 nan 0.1000 -0.0007
## 280 0.5663 nan 0.1000 -0.0008
## 300 0.5507 nan 0.1000 -0.0011
## 320 0.5409 nan 0.1000 -0.0012
## 340 0.5287 nan 0.1000 -0.0009
## 360 0.5153 nan 0.1000 -0.0007
## 380 0.5040 nan 0.1000 -0.0006
## 400 0.4941 nan 0.1000 -0.0005
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## 440 0.4746 nan 0.1000 -0.0004
## 460 0.4626 nan 0.1000 -0.0003
## 480 0.4545 nan 0.1000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2467 nan 0.1000 0.0186
## 2 1.2141 nan 0.1000 0.0184
## 3 1.1779 nan 0.1000 0.0135
## 4 1.1468 nan 0.1000 0.0132
## 5 1.1191 nan 0.1000 0.0121
## 6 1.0962 nan 0.1000 0.0092
## 7 1.0747 nan 0.1000 0.0091
## 8 1.0559 nan 0.1000 0.0075
## 9 1.0396 nan 0.1000 0.0078
## 10 1.0275 nan 0.1000 0.0050
## 20 0.9354 nan 0.1000 -0.0007
## 40 0.8454 nan 0.1000 0.0008
## 60 0.7910 nan 0.1000 -0.0006
## 80 0.7603 nan 0.1000 -0.0017
## 100 0.7281 nan 0.1000 -0.0014
## 120 0.7003 nan 0.1000 -0.0006
## 140 0.6771 nan 0.1000 -0.0012
## 160 0.6577 nan 0.1000 -0.0013
## 180 0.6345 nan 0.1000 -0.0011
## 200 0.6159 nan 0.1000 -0.0001
## 220 0.6002 nan 0.1000 -0.0012
## 240 0.5854 nan 0.1000 -0.0009
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## 280 0.5587 nan 0.1000 -0.0007
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## 320 0.5344 nan 0.1000 -0.0011
## 340 0.5229 nan 0.1000 -0.0016
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## 380 0.4961 nan 0.1000 -0.0013
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## 460 0.4481 nan 0.1000 -0.0008
## 480 0.4373 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2493 nan 0.1000 0.0191
## 2 1.2098 nan 0.1000 0.0191
## 3 1.1752 nan 0.1000 0.0154
## 4 1.1469 nan 0.1000 0.0135
## 5 1.1264 nan 0.1000 0.0055
## 6 1.1038 nan 0.1000 0.0087
## 7 1.0842 nan 0.1000 0.0084
## 8 1.0646 nan 0.1000 0.0073
## 9 1.0463 nan 0.1000 0.0048
## 10 1.0328 nan 0.1000 0.0047
## 20 0.9343 nan 0.1000 0.0010
## 40 0.8384 nan 0.1000 -0.0009
## 60 0.7867 nan 0.1000 0.0011
## 80 0.7527 nan 0.1000 -0.0016
## 100 0.7247 nan 0.1000 -0.0014
## 120 0.6968 nan 0.1000 -0.0009
## 140 0.6762 nan 0.1000 -0.0021
## 160 0.6568 nan 0.1000 -0.0014
## 180 0.6429 nan 0.1000 -0.0033
## 200 0.6242 nan 0.1000 -0.0020
## 220 0.6038 nan 0.1000 -0.0004
## 240 0.5905 nan 0.1000 -0.0009
## 260 0.5735 nan 0.1000 -0.0003
## 280 0.5580 nan 0.1000 -0.0014
## 300 0.5422 nan 0.1000 0.0001
## 320 0.5289 nan 0.1000 -0.0021
## 340 0.5149 nan 0.1000 -0.0002
## 360 0.5015 nan 0.1000 -0.0009
## 380 0.4896 nan 0.1000 -0.0006
## 400 0.4797 nan 0.1000 -0.0017
## 420 0.4693 nan 0.1000 -0.0013
## 440 0.4562 nan 0.1000 -0.0004
## 460 0.4484 nan 0.1000 -0.0018
## 480 0.4403 nan 0.1000 -0.0018
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2400 nan 0.1000 0.0251
## 2 1.1929 nan 0.1000 0.0212
## 3 1.1507 nan 0.1000 0.0156
## 4 1.1190 nan 0.1000 0.0143
## 5 1.0907 nan 0.1000 0.0112
## 6 1.0663 nan 0.1000 0.0099
## 7 1.0404 nan 0.1000 0.0087
## 8 1.0252 nan 0.1000 0.0057
## 9 1.0083 nan 0.1000 0.0059
## 10 0.9918 nan 0.1000 0.0041
## 20 0.8866 nan 0.1000 0.0014
## 40 0.7962 nan 0.1000 -0.0018
## 60 0.7291 nan 0.1000 0.0005
## 80 0.6874 nan 0.1000 -0.0018
## 100 0.6422 nan 0.1000 -0.0016
## 120 0.5989 nan 0.1000 -0.0002
## 140 0.5735 nan 0.1000 -0.0017
## 160 0.5476 nan 0.1000 -0.0015
## 180 0.5196 nan 0.1000 -0.0013
## 200 0.4972 nan 0.1000 -0.0004
## 220 0.4759 nan 0.1000 -0.0012
## 240 0.4552 nan 0.1000 -0.0007
## 260 0.4352 nan 0.1000 -0.0013
## 280 0.4160 nan 0.1000 -0.0009
## 300 0.3963 nan 0.1000 -0.0012
## 320 0.3786 nan 0.1000 -0.0004
## 340 0.3670 nan 0.1000 0.0001
## 360 0.3545 nan 0.1000 -0.0008
## 380 0.3419 nan 0.1000 -0.0014
## 400 0.3270 nan 0.1000 -0.0014
## 420 0.3134 nan 0.1000 -0.0001
## 440 0.3019 nan 0.1000 -0.0008
## 460 0.2906 nan 0.1000 -0.0015
## 480 0.2790 nan 0.1000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2421 nan 0.1000 0.0226
## 2 1.1930 nan 0.1000 0.0201
## 3 1.1540 nan 0.1000 0.0166
## 4 1.1230 nan 0.1000 0.0102
## 5 1.0981 nan 0.1000 0.0092
## 6 1.0717 nan 0.1000 0.0112
## 7 1.0494 nan 0.1000 0.0108
## 8 1.0277 nan 0.1000 0.0085
## 9 1.0103 nan 0.1000 0.0063
## 10 0.9952 nan 0.1000 0.0049
## 20 0.8879 nan 0.1000 0.0022
## 40 0.7912 nan 0.1000 -0.0011
## 60 0.7334 nan 0.1000 -0.0004
## 80 0.6863 nan 0.1000 -0.0007
## 100 0.6476 nan 0.1000 -0.0003
## 120 0.6174 nan 0.1000 -0.0010
## 140 0.5836 nan 0.1000 -0.0014
## 160 0.5534 nan 0.1000 -0.0017
## 180 0.5254 nan 0.1000 -0.0007
## 200 0.4969 nan 0.1000 -0.0002
## 220 0.4747 nan 0.1000 -0.0016
## 240 0.4557 nan 0.1000 -0.0014
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## 280 0.4170 nan 0.1000 -0.0009
## 300 0.3958 nan 0.1000 -0.0005
## 320 0.3814 nan 0.1000 -0.0021
## 340 0.3644 nan 0.1000 -0.0016
## 360 0.3524 nan 0.1000 -0.0010
## 380 0.3375 nan 0.1000 -0.0017
## 400 0.3228 nan 0.1000 -0.0002
## 420 0.3113 nan 0.1000 -0.0006
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## 460 0.2851 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2454 nan 0.1000 0.0221
## 2 1.1984 nan 0.1000 0.0225
## 3 1.1577 nan 0.1000 0.0193
## 4 1.1216 nan 0.1000 0.0163
## 5 1.0923 nan 0.1000 0.0121
## 6 1.0702 nan 0.1000 0.0085
## 7 1.0456 nan 0.1000 0.0086
## 8 1.0243 nan 0.1000 0.0063
## 9 1.0103 nan 0.1000 0.0031
## 10 0.9971 nan 0.1000 0.0030
## 20 0.8904 nan 0.1000 0.0014
## 40 0.7996 nan 0.1000 -0.0019
## 60 0.7390 nan 0.1000 -0.0004
## 80 0.6893 nan 0.1000 -0.0018
## 100 0.6550 nan 0.1000 -0.0001
## 120 0.6254 nan 0.1000 0.0000
## 140 0.5924 nan 0.1000 -0.0010
## 160 0.5652 nan 0.1000 -0.0019
## 180 0.5434 nan 0.1000 -0.0013
## 200 0.5193 nan 0.1000 -0.0007
## 220 0.4923 nan 0.1000 -0.0016
## 240 0.4716 nan 0.1000 -0.0010
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## 280 0.4366 nan 0.1000 -0.0007
## 300 0.4219 nan 0.1000 -0.0015
## 320 0.4046 nan 0.1000 -0.0005
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## 380 0.3604 nan 0.1000 -0.0012
## 400 0.3448 nan 0.1000 -0.0004
## 420 0.3333 nan 0.1000 -0.0010
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## 460 0.3064 nan 0.1000 -0.0007
## 480 0.2971 nan 0.1000 -0.0015
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2285 nan 0.2000 0.0291
## 2 1.1802 nan 0.2000 0.0219
## 3 1.1483 nan 0.2000 0.0145
## 4 1.1134 nan 0.2000 0.0149
## 5 1.0831 nan 0.2000 0.0118
## 6 1.0646 nan 0.2000 0.0077
## 7 1.0469 nan 0.2000 0.0074
## 8 1.0326 nan 0.2000 0.0040
## 9 1.0164 nan 0.2000 0.0074
## 10 0.9997 nan 0.2000 0.0063
## 20 0.9185 nan 0.2000 -0.0002
## 40 0.8497 nan 0.2000 -0.0007
## 60 0.8131 nan 0.2000 -0.0004
## 80 0.7920 nan 0.2000 -0.0009
## 100 0.7754 nan 0.2000 -0.0024
## 120 0.7548 nan 0.2000 0.0001
## 140 0.7417 nan 0.2000 -0.0034
## 160 0.7275 nan 0.2000 -0.0015
## 180 0.7181 nan 0.2000 -0.0025
## 200 0.7087 nan 0.2000 -0.0042
## 220 0.6968 nan 0.2000 -0.0024
## 240 0.6830 nan 0.2000 -0.0020
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## 280 0.6697 nan 0.2000 -0.0018
## 300 0.6612 nan 0.2000 -0.0017
## 320 0.6538 nan 0.2000 -0.0024
## 340 0.6492 nan 0.2000 -0.0020
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## 380 0.6323 nan 0.2000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2236 nan 0.2000 0.0336
## 2 1.1840 nan 0.2000 0.0179
## 3 1.1454 nan 0.2000 0.0174
## 4 1.1151 nan 0.2000 0.0107
## 5 1.0840 nan 0.2000 0.0102
## 6 1.0608 nan 0.2000 0.0028
## 7 1.0355 nan 0.2000 0.0110
## 8 1.0231 nan 0.2000 0.0039
## 9 1.0099 nan 0.2000 0.0044
## 10 0.9935 nan 0.2000 0.0082
## 20 0.9211 nan 0.2000 -0.0006
## 40 0.8535 nan 0.2000 -0.0014
## 60 0.8169 nan 0.2000 -0.0007
## 80 0.7896 nan 0.2000 -0.0009
## 100 0.7687 nan 0.2000 -0.0011
## 120 0.7533 nan 0.2000 -0.0015
## 140 0.7392 nan 0.2000 -0.0013
## 160 0.7253 nan 0.2000 -0.0007
## 180 0.7155 nan 0.2000 -0.0013
## 200 0.7026 nan 0.2000 -0.0041
## 220 0.6943 nan 0.2000 -0.0007
## 240 0.6836 nan 0.2000 -0.0007
## 260 0.6728 nan 0.2000 -0.0014
## 280 0.6639 nan 0.2000 -0.0012
## 300 0.6578 nan 0.2000 -0.0014
## 320 0.6476 nan 0.2000 -0.0021
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## 360 0.6279 nan 0.2000 -0.0007
## 380 0.6224 nan 0.2000 -0.0005
## 400 0.6176 nan 0.2000 -0.0016
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## 460 0.6017 nan 0.2000 -0.0036
## 480 0.5976 nan 0.2000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2233 nan 0.2000 0.0324
## 2 1.1773 nan 0.2000 0.0185
## 3 1.1422 nan 0.2000 0.0141
## 4 1.1127 nan 0.2000 0.0128
## 5 1.0835 nan 0.2000 0.0124
## 6 1.0596 nan 0.2000 0.0086
## 7 1.0412 nan 0.2000 0.0067
## 8 1.0204 nan 0.2000 0.0055
## 9 1.0063 nan 0.2000 0.0036
## 10 0.9918 nan 0.2000 0.0053
## 20 0.9140 nan 0.2000 0.0006
## 40 0.8462 nan 0.2000 -0.0051
## 60 0.8153 nan 0.2000 0.0004
## 80 0.7890 nan 0.2000 -0.0017
## 100 0.7716 nan 0.2000 -0.0022
## 120 0.7559 nan 0.2000 -0.0023
## 140 0.7441 nan 0.2000 -0.0051
## 160 0.7331 nan 0.2000 -0.0015
## 180 0.7217 nan 0.2000 -0.0025
## 200 0.7066 nan 0.2000 -0.0006
## 220 0.6947 nan 0.2000 -0.0010
## 240 0.6843 nan 0.2000 -0.0028
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## 280 0.6673 nan 0.2000 -0.0019
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## 320 0.6527 nan 0.2000 -0.0021
## 340 0.6438 nan 0.2000 -0.0015
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## 480 0.5978 nan 0.2000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2139 nan 0.2000 0.0406
## 2 1.1530 nan 0.2000 0.0323
## 3 1.1056 nan 0.2000 0.0213
## 4 1.0613 nan 0.2000 0.0175
## 5 1.0259 nan 0.2000 0.0156
## 6 1.0005 nan 0.2000 0.0100
## 7 0.9772 nan 0.2000 0.0074
## 8 0.9601 nan 0.2000 0.0001
## 9 0.9410 nan 0.2000 0.0078
## 10 0.9296 nan 0.2000 0.0028
## 20 0.8449 nan 0.2000 -0.0022
## 40 0.7566 nan 0.2000 -0.0053
## 60 0.7066 nan 0.2000 -0.0004
## 80 0.6730 nan 0.2000 -0.0012
## 100 0.6352 nan 0.2000 -0.0013
## 120 0.5953 nan 0.2000 -0.0044
## 140 0.5674 nan 0.2000 -0.0038
## 160 0.5321 nan 0.2000 -0.0038
## 180 0.5106 nan 0.2000 -0.0015
## 200 0.4913 nan 0.2000 -0.0040
## 220 0.4709 nan 0.2000 -0.0020
## 240 0.4546 nan 0.2000 -0.0032
## 260 0.4304 nan 0.2000 -0.0018
## 280 0.4077 nan 0.2000 -0.0026
## 300 0.3856 nan 0.2000 -0.0014
## 320 0.3736 nan 0.2000 -0.0082
## 340 0.3571 nan 0.2000 -0.0010
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## 380 0.3329 nan 0.2000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2035 nan 0.2000 0.0354
## 2 1.1599 nan 0.2000 0.0133
## 3 1.1006 nan 0.2000 0.0221
## 4 1.0551 nan 0.2000 0.0234
## 5 1.0191 nan 0.2000 0.0141
## 6 0.9954 nan 0.2000 0.0062
## 7 0.9719 nan 0.2000 0.0066
## 8 0.9559 nan 0.2000 0.0027
## 9 0.9449 nan 0.2000 -0.0013
## 10 0.9291 nan 0.2000 0.0025
## 20 0.8563 nan 0.2000 -0.0009
## 40 0.7731 nan 0.2000 -0.0026
## 60 0.7218 nan 0.2000 -0.0024
## 80 0.6846 nan 0.2000 -0.0024
## 100 0.6552 nan 0.2000 -0.0030
## 120 0.6202 nan 0.2000 -0.0014
## 140 0.5937 nan 0.2000 -0.0029
## 160 0.5586 nan 0.2000 -0.0006
## 180 0.5328 nan 0.2000 -0.0046
## 200 0.5101 nan 0.2000 -0.0026
## 220 0.4829 nan 0.2000 -0.0026
## 240 0.4660 nan 0.2000 -0.0036
## 260 0.4451 nan 0.2000 -0.0021
## 280 0.4218 nan 0.2000 -0.0008
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## 320 0.3915 nan 0.2000 -0.0010
## 340 0.3724 nan 0.2000 -0.0035
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## 380 0.3445 nan 0.2000 -0.0022
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## 460 0.2897 nan 0.2000 -0.0024
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2152 nan 0.2000 0.0387
## 2 1.1448 nan 0.2000 0.0295
## 3 1.0845 nan 0.2000 0.0232
## 4 1.0530 nan 0.2000 0.0084
## 5 1.0247 nan 0.2000 0.0114
## 6 0.9996 nan 0.2000 0.0074
## 7 0.9737 nan 0.2000 0.0086
## 8 0.9603 nan 0.2000 0.0041
## 9 0.9423 nan 0.2000 0.0052
## 10 0.9318 nan 0.2000 0.0005
## 20 0.8555 nan 0.2000 -0.0035
## 40 0.7749 nan 0.2000 -0.0018
## 60 0.7203 nan 0.2000 -0.0006
## 80 0.6751 nan 0.2000 -0.0012
## 100 0.6488 nan 0.2000 -0.0025
## 120 0.6036 nan 0.2000 -0.0004
## 140 0.5764 nan 0.2000 -0.0006
## 160 0.5448 nan 0.2000 -0.0017
## 180 0.5239 nan 0.2000 -0.0016
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## 240 0.4555 nan 0.2000 -0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1912 nan 0.2000 0.0470
## 2 1.1249 nan 0.2000 0.0281
## 3 1.0784 nan 0.2000 0.0182
## 4 1.0365 nan 0.2000 0.0150
## 5 1.0053 nan 0.2000 0.0090
## 6 0.9773 nan 0.2000 0.0055
## 7 0.9523 nan 0.2000 0.0111
## 8 0.9308 nan 0.2000 0.0042
## 9 0.9082 nan 0.2000 0.0017
## 10 0.8914 nan 0.2000 0.0004
## 20 0.8001 nan 0.2000 -0.0030
## 40 0.6976 nan 0.2000 -0.0023
## 60 0.6315 nan 0.2000 -0.0026
## 80 0.5743 nan 0.2000 -0.0070
## 100 0.5217 nan 0.2000 -0.0013
## 120 0.4841 nan 0.2000 0.0004
## 140 0.4456 nan 0.2000 -0.0018
## 160 0.4113 nan 0.2000 -0.0019
## 180 0.3706 nan 0.2000 -0.0013
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## 240 0.2928 nan 0.2000 -0.0032
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1947 nan 0.2000 0.0467
## 2 1.1163 nan 0.2000 0.0339
## 3 1.0673 nan 0.2000 0.0163
## 4 1.0235 nan 0.2000 0.0190
## 5 0.9970 nan 0.2000 0.0056
## 6 0.9642 nan 0.2000 0.0090
## 7 0.9456 nan 0.2000 0.0033
## 8 0.9260 nan 0.2000 0.0061
## 9 0.9094 nan 0.2000 0.0017
## 10 0.8917 nan 0.2000 0.0042
## 20 0.7991 nan 0.2000 -0.0014
## 40 0.7177 nan 0.2000 -0.0041
## 60 0.6374 nan 0.2000 -0.0021
## 80 0.5658 nan 0.2000 -0.0032
## 100 0.5171 nan 0.2000 -0.0032
## 120 0.4899 nan 0.2000 -0.0185
## 140 0.4383 nan 0.2000 -0.0018
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## 180 0.3727 nan 0.2000 -0.0011
## 200 0.3363 nan 0.2000 -0.0009
## 220 0.3122 nan 0.2000 -0.0026
## 240 0.2942 nan 0.2000 -0.0024
## 260 0.2712 nan 0.2000 -0.0010
## 280 0.2533 nan 0.2000 -0.0009
## 300 0.2336 nan 0.2000 -0.0014
## 320 0.2182 nan 0.2000 -0.0005
## 340 0.2028 nan 0.2000 -0.0013
## 360 0.1876 nan 0.2000 -0.0008
## 380 0.1735 nan 0.2000 -0.0007
## 400 0.1654 nan 0.2000 -0.0007
## 420 0.1544 nan 0.2000 -0.0010
## 440 0.1462 nan 0.2000 -0.0006
## 460 0.1374 nan 0.2000 -0.0011
## 480 0.1298 nan 0.2000 -0.0003
## 500 0.1223 nan 0.2000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2019 nan 0.2000 0.0398
## 2 1.1325 nan 0.2000 0.0250
## 3 1.0793 nan 0.2000 0.0235
## 4 1.0426 nan 0.2000 0.0116
## 5 1.0066 nan 0.2000 0.0126
## 6 0.9795 nan 0.2000 0.0102
## 7 0.9534 nan 0.2000 0.0091
## 8 0.9344 nan 0.2000 0.0044
## 9 0.9120 nan 0.2000 0.0025
## 10 0.8986 nan 0.2000 -0.0034
## 20 0.8043 nan 0.2000 -0.0064
## 40 0.7004 nan 0.2000 -0.0008
## 60 0.6314 nan 0.2000 -0.0039
## 80 0.5639 nan 0.2000 -0.0015
## 100 0.5097 nan 0.2000 -0.0026
## 120 0.4724 nan 0.2000 -0.0038
## 140 0.4288 nan 0.2000 -0.0033
## 160 0.3952 nan 0.2000 -0.0023
## 180 0.3637 nan 0.2000 -0.0013
## 200 0.3389 nan 0.2000 -0.0022
## 220 0.3130 nan 0.2000 -0.0007
## 240 0.2914 nan 0.2000 -0.0023
## 260 0.2680 nan 0.2000 -0.0005
## 280 0.2470 nan 0.2000 -0.0009
## 300 0.2313 nan 0.2000 -0.0023
## 320 0.2157 nan 0.2000 -0.0019
## 340 0.2005 nan 0.2000 -0.0015
## 360 0.1880 nan 0.2000 -0.0008
## 380 0.1757 nan 0.2000 -0.0007
## 400 0.1648 nan 0.2000 -0.0012
## 420 0.1543 nan 0.2000 -0.0006
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## 460 0.1389 nan 0.2000 -0.0009
## 480 0.1308 nan 0.2000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2011 nan 0.3000 0.0408
## 2 1.1300 nan 0.3000 0.0267
## 3 1.0925 nan 0.3000 0.0158
## 4 1.0548 nan 0.3000 0.0185
## 5 1.0236 nan 0.3000 0.0113
## 6 1.0055 nan 0.3000 0.0058
## 7 0.9860 nan 0.3000 0.0056
## 8 0.9718 nan 0.3000 0.0025
## 9 0.9492 nan 0.3000 0.0070
## 10 0.9402 nan 0.3000 0.0031
## 20 0.8734 nan 0.3000 -0.0008
## 40 0.8245 nan 0.3000 0.0007
## 60 0.7895 nan 0.3000 -0.0032
## 80 0.7557 nan 0.3000 -0.0047
## 100 0.7316 nan 0.3000 -0.0041
## 120 0.7093 nan 0.3000 -0.0008
## 140 0.6885 nan 0.3000 -0.0016
## 160 0.6724 nan 0.3000 -0.0000
## 180 0.6596 nan 0.3000 -0.0016
## 200 0.6531 nan 0.3000 -0.0042
## 220 0.6408 nan 0.3000 -0.0018
## 240 0.6241 nan 0.3000 -0.0012
## 260 0.6134 nan 0.3000 -0.0029
## 280 0.6058 nan 0.3000 -0.0023
## 300 0.6003 nan 0.3000 -0.0024
## 320 0.5935 nan 0.3000 -0.0035
## 340 0.5833 nan 0.3000 -0.0047
## 360 0.5763 nan 0.3000 -0.0049
## 380 0.5701 nan 0.3000 -0.0029
## 400 0.5587 nan 0.3000 -0.0037
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## 460 0.5392 nan 0.3000 -0.0016
## 480 0.5329 nan 0.3000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2021 nan 0.3000 0.0471
## 2 1.1384 nan 0.3000 0.0278
## 3 1.0970 nan 0.3000 0.0196
## 4 1.0734 nan 0.3000 0.0071
## 5 1.0356 nan 0.3000 0.0163
## 6 1.0105 nan 0.3000 0.0013
## 7 0.9944 nan 0.3000 0.0024
## 8 0.9713 nan 0.3000 0.0110
## 9 0.9566 nan 0.3000 0.0036
## 10 0.9461 nan 0.3000 -0.0010
## 20 0.8739 nan 0.3000 -0.0025
## 40 0.8269 nan 0.3000 -0.0080
## 60 0.7994 nan 0.3000 -0.0033
## 80 0.7705 nan 0.3000 -0.0016
## 100 0.7485 nan 0.3000 -0.0044
## 120 0.7336 nan 0.3000 -0.0027
## 140 0.7130 nan 0.3000 -0.0044
## 160 0.6997 nan 0.3000 -0.0043
## 180 0.6834 nan 0.3000 -0.0036
## 200 0.6729 nan 0.3000 -0.0017
## 220 0.6619 nan 0.3000 -0.0030
## 240 0.6476 nan 0.3000 -0.0039
## 260 0.6348 nan 0.3000 -0.0016
## 280 0.6248 nan 0.3000 -0.0035
## 300 0.6116 nan 0.3000 -0.0009
## 320 0.6055 nan 0.3000 -0.0040
## 340 0.5983 nan 0.3000 -0.0019
## 360 0.5910 nan 0.3000 -0.0016
## 380 0.5858 nan 0.3000 -0.0026
## 400 0.5723 nan 0.3000 -0.0020
## 420 0.5673 nan 0.3000 -0.0011
## 440 0.5635 nan 0.3000 -0.0029
## 460 0.5527 nan 0.3000 -0.0034
## 480 0.5454 nan 0.3000 -0.0058
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2025 nan 0.3000 0.0430
## 2 1.1464 nan 0.3000 0.0273
## 3 1.1076 nan 0.3000 0.0202
## 4 1.0679 nan 0.3000 0.0168
## 5 1.0368 nan 0.3000 0.0178
## 6 1.0115 nan 0.3000 0.0097
## 7 0.9925 nan 0.3000 0.0034
## 8 0.9853 nan 0.3000 -0.0023
## 9 0.9738 nan 0.3000 -0.0000
## 10 0.9591 nan 0.3000 0.0034
## 20 0.8935 nan 0.3000 -0.0005
## 40 0.8362 nan 0.3000 -0.0001
## 60 0.8042 nan 0.3000 -0.0064
## 80 0.7730 nan 0.3000 -0.0018
## 100 0.7471 nan 0.3000 -0.0009
## 120 0.7319 nan 0.3000 -0.0037
## 140 0.7164 nan 0.3000 -0.0021
## 160 0.7081 nan 0.3000 -0.0043
## 180 0.6981 nan 0.3000 -0.0059
## 200 0.6853 nan 0.3000 -0.0039
## 220 0.6709 nan 0.3000 -0.0011
## 240 0.6558 nan 0.3000 -0.0016
## 260 0.6470 nan 0.3000 -0.0028
## 280 0.6342 nan 0.3000 -0.0016
## 300 0.6246 nan 0.3000 -0.0011
## 320 0.6126 nan 0.3000 -0.0015
## 340 0.6035 nan 0.3000 -0.0023
## 360 0.5995 nan 0.3000 -0.0004
## 380 0.5908 nan 0.3000 -0.0029
## 400 0.5851 nan 0.3000 -0.0009
## 420 0.5771 nan 0.3000 -0.0032
## 440 0.5687 nan 0.3000 -0.0036
## 460 0.5556 nan 0.3000 -0.0010
## 480 0.5518 nan 0.3000 -0.0046
## 500 0.5485 nan 0.3000 -0.0039
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1783 nan 0.3000 0.0578
## 2 1.0957 nan 0.3000 0.0384
## 3 1.0592 nan 0.3000 0.0131
## 4 1.0126 nan 0.3000 0.0196
## 5 0.9794 nan 0.3000 0.0086
## 6 0.9483 nan 0.3000 0.0119
## 7 0.9279 nan 0.3000 0.0021
## 8 0.9130 nan 0.3000 0.0012
## 9 0.9025 nan 0.3000 0.0000
## 10 0.8888 nan 0.3000 0.0034
## 20 0.8144 nan 0.3000 -0.0029
## 40 0.7325 nan 0.3000 -0.0060
## 60 0.6673 nan 0.3000 -0.0015
## 80 0.6107 nan 0.3000 -0.0004
## 100 0.5743 nan 0.3000 -0.0026
## 120 0.5275 nan 0.3000 -0.0025
## 140 0.4907 nan 0.3000 -0.0010
## 160 0.4604 nan 0.3000 -0.0013
## 180 0.4312 nan 0.3000 -0.0042
## 200 0.3976 nan 0.3000 -0.0002
## 220 0.3727 nan 0.3000 -0.0016
## 240 0.3484 nan 0.3000 -0.0025
## 260 0.3281 nan 0.3000 -0.0021
## 280 0.3064 nan 0.3000 -0.0020
## 300 0.2921 nan 0.3000 -0.0035
## 320 0.2747 nan 0.3000 -0.0024
## 340 0.2554 nan 0.3000 -0.0006
## 360 0.2459 nan 0.3000 -0.0019
## 380 0.2347 nan 0.3000 -0.0030
## 400 0.2197 nan 0.3000 -0.0011
## 420 0.2088 nan 0.3000 -0.0015
## 440 0.2005 nan 0.3000 -0.0028
## 460 0.1908 nan 0.3000 -0.0029
## 480 0.1820 nan 0.3000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1721 nan 0.3000 0.0561
## 2 1.1001 nan 0.3000 0.0339
## 3 1.0488 nan 0.3000 0.0199
## 4 1.0114 nan 0.3000 0.0154
## 5 0.9811 nan 0.3000 0.0117
## 6 0.9509 nan 0.3000 0.0081
## 7 0.9337 nan 0.3000 0.0031
## 8 0.9188 nan 0.3000 0.0010
## 9 0.9075 nan 0.3000 -0.0014
## 10 0.8922 nan 0.3000 0.0014
## 20 0.8198 nan 0.3000 -0.0090
## 40 0.7218 nan 0.3000 -0.0037
## 60 0.6614 nan 0.3000 -0.0016
## 80 0.6051 nan 0.3000 -0.0061
## 100 0.5681 nan 0.3000 -0.0034
## 120 0.5269 nan 0.3000 -0.0053
## 140 0.4912 nan 0.3000 -0.0043
## 160 0.4617 nan 0.3000 -0.0023
## 180 inf nan 0.3000 nan
## 200 inf nan 0.3000 nan
## 220 inf nan 0.3000 nan
## 240 inf nan 0.3000 nan
## 260 inf nan 0.3000 nan
## 280 inf nan 0.3000 nan
## 300 inf nan 0.3000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1780 nan 0.3000 0.0546
## 2 1.1037 nan 0.3000 0.0231
## 3 1.0389 nan 0.3000 0.0217
## 4 1.0025 nan 0.3000 0.0099
## 5 0.9714 nan 0.3000 0.0129
## 6 0.9519 nan 0.3000 0.0005
## 7 0.9328 nan 0.3000 0.0014
## 8 0.9226 nan 0.3000 -0.0044
## 9 0.9086 nan 0.3000 0.0050
## 10 0.8963 nan 0.3000 0.0041
## 20 0.8107 nan 0.3000 -0.0048
## 40 0.7383 nan 0.3000 -0.0033
## 60 0.6853 nan 0.3000 -0.0034
## 80 0.6312 nan 0.3000 -0.0048
## 100 0.5762 nan 0.3000 -0.0030
## 120 0.5425 nan 0.3000 -0.0004
## 140 0.4985 nan 0.3000 -0.0048
## 160 0.4646 nan 0.3000 -0.0012
## 180 0.4401 nan 0.3000 -0.0021
## 200 0.4113 nan 0.3000 -0.0042
## 220 0.3815 nan 0.3000 -0.0004
## 240 0.3532 nan 0.3000 -0.0016
## 260 0.3343 nan 0.3000 -0.0032
## 280 0.3170 nan 0.3000 -0.0040
## 300 0.3005 nan 0.3000 -0.0016
## 320 0.2806 nan 0.3000 -0.0012
## 340 0.2669 nan 0.3000 -0.0017
## 360 0.2554 nan 0.3000 -0.0014
## 380 0.2430 nan 0.3000 -0.0009
## 400 0.2364 nan 0.3000 -0.0019
## 420 0.2233 nan 0.3000 -0.0001
## 440 0.2132 nan 0.3000 -0.0014
## 460 0.2050 nan 0.3000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1635 nan 0.3000 0.0500
## 2 1.0794 nan 0.3000 0.0362
## 3 1.0094 nan 0.3000 0.0218
## 4 0.9675 nan 0.3000 0.0122
## 5 0.9388 nan 0.3000 0.0085
## 6 0.9068 nan 0.3000 0.0089
## 7 0.8885 nan 0.3000 0.0007
## 8 0.8734 nan 0.3000 0.0001
## 9 0.8682 nan 0.3000 -0.0079
## 10 0.8499 nan 0.3000 0.0018
## 20 0.7489 nan 0.3000 0.0002
## 40 0.6421 nan 0.3000 -0.0006
## 60 0.5632 nan 0.3000 -0.0012
## 80 0.4965 nan 0.3000 -0.0047
## 100 0.4300 nan 0.3000 -0.0022
## 120 0.3789 nan 0.3000 0.0009
## 140 0.3333 nan 0.3000 -0.0032
## 160 inf nan 0.3000 nan
## 180 inf nan 0.3000 nan
## 200 inf nan 0.3000 nan
## 220 inf nan 0.3000 nan
## 240 inf nan 0.3000 nan
## 260 inf nan 0.3000 nan
## 280 inf nan 0.3000 nan
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## 320 inf nan 0.3000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1598 nan 0.3000 0.0590
## 2 1.0647 nan 0.3000 0.0433
## 3 1.0172 nan 0.3000 0.0152
## 4 0.9676 nan 0.3000 0.0191
## 5 0.9494 nan 0.3000 0.0009
## 6 0.9171 nan 0.3000 0.0083
## 7 0.8946 nan 0.3000 0.0023
## 8 0.8757 nan 0.3000 0.0016
## 9 0.8581 nan 0.3000 0.0035
## 10 0.8431 nan 0.3000 0.0002
## 20 0.7489 nan 0.3000 0.0006
## 40 0.6525 nan 0.3000 -0.0046
## 60 0.5845 nan 0.3000 -0.0047
## 80 0.5050 nan 0.3000 -0.0042
## 100 0.4504 nan 0.3000 -0.0031
## 120 0.4065 nan 0.3000 -0.0041
## 140 0.3499 nan 0.3000 -0.0048
## 160 0.3180 nan 0.3000 -0.0011
## 180 0.2839 nan 0.3000 -0.0015
## 200 0.2529 nan 0.3000 -0.0006
## 220 0.2228 nan 0.3000 -0.0013
## 240 0.2005 nan 0.3000 -0.0016
## 260 0.1834 nan 0.3000 -0.0013
## 280 0.1641 nan 0.3000 -0.0013
## 300 0.1502 nan 0.3000 -0.0011
## 320 0.1375 nan 0.3000 -0.0009
## 340 0.1273 nan 0.3000 -0.0021
## 360 0.1176 nan 0.3000 -0.0005
## 380 0.1076 nan 0.3000 -0.0003
## 400 0.1005 nan 0.3000 -0.0003
## 420 0.0917 nan 0.3000 -0.0010
## 440 0.0834 nan 0.3000 -0.0009
## 460 0.0765 nan 0.3000 -0.0002
## 480 0.0709 nan 0.3000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1643 nan 0.3000 0.0501
## 2 1.0778 nan 0.3000 0.0430
## 3 1.0167 nan 0.3000 0.0214
## 4 0.9707 nan 0.3000 0.0139
## 5 0.9321 nan 0.3000 0.0135
## 6 0.9130 nan 0.3000 -0.0013
## 7 0.8912 nan 0.3000 0.0005
## 8 0.8762 nan 0.3000 -0.0036
## 9 0.8657 nan 0.3000 -0.0027
## 10 0.8558 nan 0.3000 -0.0016
## 20 0.7512 nan 0.3000 0.0015
## 40 0.6287 nan 0.3000 -0.0060
## 60 0.5382 nan 0.3000 -0.0058
## 80 0.4768 nan 0.3000 -0.0038
## 100 0.4251 nan 0.3000 -0.0037
## 120 0.3719 nan 0.3000 -0.0026
## 140 0.3252 nan 0.3000 -0.0023
## 160 0.2907 nan 0.3000 -0.0044
## 180 0.2664 nan 0.3000 -0.0011
## 200 0.2329 nan 0.3000 -0.0010
## 220 0.2086 nan 0.3000 -0.0024
## 240 0.1870 nan 0.3000 -0.0009
## 260 0.1684 nan 0.3000 -0.0033
## 280 0.1557 nan 0.3000 -0.0016
## 300 0.1409 nan 0.3000 -0.0004
## 320 0.1280 nan 0.3000 -0.0006
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## 360 0.1082 nan 0.3000 -0.0010
## 380 0.0988 nan 0.3000 -0.0009
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## 460 0.0699 nan 0.3000 -0.0011
## 480 0.0637 nan 0.3000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1699 nan 0.5000 0.0560
## 2 1.0832 nan 0.5000 0.0308
## 3 1.0296 nan 0.5000 0.0263
## 4 0.9894 nan 0.5000 0.0201
## 5 0.9692 nan 0.5000 0.0066
## 6 0.9549 nan 0.5000 -0.0018
## 7 0.9378 nan 0.5000 0.0042
## 8 0.9181 nan 0.5000 0.0052
## 9 0.9089 nan 0.5000 -0.0022
## 10 0.8969 nan 0.5000 -0.0032
## 20 0.8603 nan 0.5000 -0.0024
## 40 0.7902 nan 0.5000 -0.0018
## 60 0.7526 nan 0.5000 -0.0043
## 80 0.7106 nan 0.5000 -0.0024
## 100 0.6887 nan 0.5000 -0.0031
## 120 0.6694 nan 0.5000 -0.0036
## 140 0.6579 nan 0.5000 -0.0066
## 160 0.6347 nan 0.5000 -0.0059
## 180 0.6270 nan 0.5000 -0.0037
## 200 0.6063 nan 0.5000 -0.0019
## 220 0.5882 nan 0.5000 -0.0042
## 240 0.5843 nan 0.5000 -0.0078
## 260 0.5638 nan 0.5000 -0.0025
## 280 0.5554 nan 0.5000 -0.0040
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## 320 0.5426 nan 0.5000 -0.0108
## 340 0.5266 nan 0.5000 -0.0044
## 360 0.5196 nan 0.5000 -0.0019
## 380 0.5066 nan 0.5000 -0.0033
## 400 0.4958 nan 0.5000 -0.0048
## 420 0.4920 nan 0.5000 -0.0019
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## 460 0.4770 nan 0.5000 0.0016
## 480 0.4749 nan 0.5000 -0.0072
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1702 nan 0.5000 0.0629
## 2 1.0888 nan 0.5000 0.0308
## 3 1.0306 nan 0.5000 0.0151
## 4 0.9859 nan 0.5000 0.0151
## 5 0.9723 nan 0.5000 0.0008
## 6 0.9670 nan 0.5000 -0.0071
## 7 0.9447 nan 0.5000 0.0086
## 8 0.9323 nan 0.5000 0.0017
## 9 0.9159 nan 0.5000 0.0042
## 10 0.9111 nan 0.5000 -0.0035
## 20 0.8388 nan 0.5000 0.0021
## 40 0.7784 nan 0.5000 -0.0049
## 60 0.7377 nan 0.5000 -0.0011
## 80 0.7213 nan 0.5000 -0.0067
## 100 0.7015 nan 0.5000 -0.0075
## 120 0.6812 nan 0.5000 -0.0067
## 140 0.6426 nan 0.5000 -0.0074
## 160 0.6345 nan 0.5000 -0.0002
## 180 0.6227 nan 0.5000 -0.0015
## 200 0.6150 nan 0.5000 -0.0083
## 220 0.5945 nan 0.5000 -0.0067
## 240 0.5828 nan 0.5000 -0.0012
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## 280 0.5547 nan 0.5000 -0.0031
## 300 0.5512 nan 0.5000 -0.0032
## 320 0.5410 nan 0.5000 -0.0023
## 340 0.5276 nan 0.5000 -0.0003
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## 440 0.4924 nan 0.5000 -0.0060
## 460 0.4747 nan 0.5000 -0.0028
## 480 0.4757 nan 0.5000 -0.0076
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1581 nan 0.5000 0.0590
## 2 1.0832 nan 0.5000 0.0264
## 3 1.0312 nan 0.5000 0.0225
## 4 0.9854 nan 0.5000 0.0214
## 5 0.9687 nan 0.5000 0.0023
## 6 0.9570 nan 0.5000 -0.0016
## 7 0.9408 nan 0.5000 0.0025
## 8 0.9202 nan 0.5000 0.0036
## 9 0.9082 nan 0.5000 -0.0043
## 10 0.9022 nan 0.5000 -0.0023
## 20 0.8342 nan 0.5000 0.0017
## 40 0.7689 nan 0.5000 -0.0065
## 60 0.7409 nan 0.5000 -0.0112
## 80 0.7146 nan 0.5000 -0.0087
## 100 0.6914 nan 0.5000 -0.0027
## 120 0.6804 nan 0.5000 -0.0046
## 140 0.6550 nan 0.5000 -0.0036
## 160 0.6436 nan 0.5000 -0.0042
## 180 0.6309 nan 0.5000 -0.0079
## 200 0.6118 nan 0.5000 -0.0054
## 220 0.5965 nan 0.5000 -0.0058
## 240 0.5768 nan 0.5000 -0.0048
## 260 0.5631 nan 0.5000 -0.0035
## 280 0.5564 nan 0.5000 -0.0019
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## 320 0.5316 nan 0.5000 -0.0061
## 340 0.5248 nan 0.5000 -0.0025
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## 380 0.5047 nan 0.5000 -0.0036
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## 460 0.4643 nan 0.5000 -0.0030
## 480 0.4605 nan 0.5000 -0.0018
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1334 nan 0.5000 0.0602
## 2 1.0439 nan 0.5000 0.0274
## 3 0.9764 nan 0.5000 0.0315
## 4 0.9490 nan 0.5000 0.0025
## 5 0.9398 nan 0.5000 -0.0180
## 6 0.9340 nan 0.5000 -0.0188
## 7 0.9071 nan 0.5000 0.0064
## 8 0.8844 nan 0.5000 0.0040
## 9 0.8598 nan 0.5000 0.0062
## 10 0.8521 nan 0.5000 -0.0080
## 20 0.7641 nan 0.5000 -0.0047
## 40 0.6877 nan 0.5000 -0.0102
## 60 0.6168 nan 0.5000 -0.0071
## 80 0.5881 nan 0.5000 -0.0052
## 100 1325029.2534 nan 0.5000 -0.0049
## 120 1325029.1995 nan 0.5000 -0.0073
## 140 1325029.1723 nan 0.5000 -0.0009
## 160 1325029.1445 nan 0.5000 -0.0026
## 180 1325029.1078 nan 0.5000 -0.0105
## 200 1325029.0880 nan 0.5000 -0.0030
## 220 1325029.0674 nan 0.5000 -0.0035
## 240 1325029.0523 nan 0.5000 0.0002
## 260 1325030.2055 nan 0.5000 -1.1729
## 280 inf nan 0.5000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1241 nan 0.5000 0.0804
## 2 1.0414 nan 0.5000 0.0259
## 3 0.9920 nan 0.5000 0.0158
## 4 0.9582 nan 0.5000 0.0076
## 5 0.9369 nan 0.5000 0.0018
## 6 0.9134 nan 0.5000 0.0079
## 7 0.8988 nan 0.5000 -0.0059
## 8 0.9046 nan 0.5000 -0.0241
## 9 0.8876 nan 0.5000 0.0026
## 10 0.8774 nan 0.5000 -0.0100
## 20 0.8049 nan 0.5000 -0.0061
## 40 0.6836 nan 0.5000 -0.0023
## 60 0.6281 nan 0.5000 -0.0169
## 80 0.5708 nan 0.5000 -0.0096
## 100 0.4895 nan 0.5000 -0.0047
## 120 0.4336 nan 0.5000 -0.0057
## 140 0.3816 nan 0.5000 -0.0068
## 160 0.3392 nan 0.5000 -0.0027
## 180 0.3068 nan 0.5000 -0.0021
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## 220 0.2388 nan 0.5000 -0.0035
## 240 0.2232 nan 0.5000 -0.0019
## 260 0.1978 nan 0.5000 -0.0008
## 280 0.1818 nan 0.5000 -0.0006
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## 320 0.1536 nan 0.5000 -0.0004
## 340 0.1428 nan 0.5000 -0.0013
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## 380 0.1187 nan 0.5000 -0.0000
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## 480 0.0794 nan 0.5000 -0.0017
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1429 nan 0.5000 0.0595
## 2 1.0520 nan 0.5000 0.0182
## 3 1.0108 nan 0.5000 0.0076
## 4 0.9618 nan 0.5000 0.0132
## 5 0.9328 nan 0.5000 0.0085
## 6 0.9193 nan 0.5000 0.0013
## 7 0.9090 nan 0.5000 -0.0065
## 8 0.8979 nan 0.5000 -0.0039
## 9 0.8865 nan 0.5000 -0.0035
## 10 0.8653 nan 0.5000 0.0003
## 20 0.7968 nan 0.5000 -0.0068
## 40 0.7178 nan 0.5000 -0.0087
## 60 0.6387 nan 0.5000 -0.0008
## 80 0.5908 nan 0.5000 -0.0072
## 100 0.5174 nan 0.5000 -0.0092
## 120 0.4798 nan 0.5000 -0.0142
## 140 0.4409 nan 0.5000 -0.0037
## 160 0.3937 nan 0.5000 -0.0059
## 180 0.3661 nan 0.5000 -0.0076
## 200 0.3117 nan 0.5000 -0.0058
## 220 0.2842 nan 0.5000 -0.0043
## 240 0.2623 nan 0.5000 -0.0029
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## 280 0.2234 nan 0.5000 -0.0030
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## 380 0.1432 nan 0.5000 -0.0023
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## 420 0.1195 nan 0.5000 -0.0003
## 440 0.1063 nan 0.5000 -0.0010
## 460 0.0992 nan 0.5000 -0.0012
## 480 0.0931 nan 0.5000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0978 nan 0.5000 0.0860
## 2 0.9844 nan 0.5000 0.0446
## 3 0.9411 nan 0.5000 0.0096
## 4 0.9128 nan 0.5000 -0.0111
## 5 0.8917 nan 0.5000 -0.0084
## 6 0.8683 nan 0.5000 -0.0121
## 7 0.8535 nan 0.5000 -0.0067
## 8 0.8419 nan 0.5000 -0.0160
## 9 0.8267 nan 0.5000 -0.0061
## 10 0.8104 nan 0.5000 -0.0022
## 20 0.7802 nan 0.5000 -0.0107
## 40 0.6281 nan 0.5000 -0.0146
## 60 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0988 nan 0.5000 0.0770
## 2 1.0149 nan 0.5000 0.0219
## 3 0.9609 nan 0.5000 0.0107
## 4 0.9408 nan 0.5000 -0.0109
## 5 0.9071 nan 0.5000 0.0050
## 6 0.8821 nan 0.5000 -0.0023
## 7 0.8495 nan 0.5000 0.0050
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## 9 0.8221 nan 0.5000 -0.0040
## 10 0.8209 nan 0.5000 -0.0212
## 20 0.7232 nan 0.5000 -0.0078
## 40 0.5891 nan 0.5000 -0.0123
## 60 0.7246 nan 0.5000 -0.0054
## 80 0.4692 nan 0.5000 -0.0019
## 100 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0761 nan 0.5000 0.1026
## 2 0.9745 nan 0.5000 0.0342
## 3 0.9337 nan 0.5000 0.0048
## 4 0.8888 nan 0.5000 0.0132
## 5 0.8565 nan 0.5000 -0.0008
## 6 0.8364 nan 0.5000 -0.0026
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## 8 0.8137 nan 0.5000 -0.0059
## 9 0.8101 nan 0.5000 -0.0217
## 10 0.8112 nan 0.5000 -0.0204
## 20 0.7058 nan 0.5000 -0.0092
## 40 0.5670 nan 0.5000 -0.0285
## 60 0.4574 nan 0.5000 -0.0015
## 80 0.3609 nan 0.5000 -0.0077
## 100 0.2893 nan 0.5000 -0.0050
## 120 0.2373 nan 0.5000 -0.0024
## 140 0.1952 nan 0.5000 -0.0029
## 160 0.1727 nan 0.5000 -0.0033
## 180 0.1483 nan 0.5000 -0.0027
## 200 0.1284 nan 0.5000 -0.0022
## 220 0.1077 nan 0.5000 -0.0005
## 240 0.0897 nan 0.5000 0.0002
## 260 0.0764 nan 0.5000 -0.0004
## 280 0.0674 nan 0.5000 -0.0012
## 300 0.0589 nan 0.5000 -0.0014
## 320 0.0533 nan 0.5000 -0.0016
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## 380 0.0357 nan 0.5000 -0.0002
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## 480 0.0189 nan 0.5000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1229 nan 1.0000 0.0837
## 2 1.0877 nan 1.0000 -0.0221
## 3 1.0182 nan 1.0000 0.0314
## 4 0.9980 nan 1.0000 -0.0179
## 5 0.9633 nan 1.0000 0.0028
## 6 0.9631 nan 1.0000 -0.0181
## 7 0.9425 nan 1.0000 0.0001
## 8 0.9067 nan 1.0000 0.0114
## 9 0.9071 nan 1.0000 -0.0152
## 10 0.9211 nan 1.0000 -0.0273
## 20 0.8199 nan 1.0000 -0.0086
## 40 0.7853 nan 1.0000 -0.0335
## 60 382121397.6836 nan 1.0000 -0.0088
## 80 382126016.3943 nan 1.0000 0.0005
## 100 382126016.3611 nan 1.0000 0.0000
## 120 382126016.3342 nan 1.0000 -0.0123
## 140 382126016.3292 nan 1.0000 -0.0033
## 160 382126016.3376 nan 1.0000 -0.0118
## 180 382126016.2803 nan 1.0000 -0.0022
## 200 382126016.2768 nan 1.0000 -0.0005
## 220 382126016.2546 nan 1.0000 -0.0051
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1277 nan 1.0000 0.0649
## 2 1.0442 nan 1.0000 0.0327
## 3 0.9900 nan 1.0000 0.0194
## 4 0.9692 nan 1.0000 -0.0059
## 5 0.9509 nan 1.0000 0.0005
## 6 0.9531 nan 1.0000 -0.0189
## 7 0.9454 nan 1.0000 -0.0135
## 8 0.9331 nan 1.0000 -0.0051
## 9 0.9081 nan 1.0000 0.0020
## 10 0.9157 nan 1.0000 -0.0232
## 20 44948346997.6312 nan 1.0000 0.0142
## 40 44948347000.3201 nan 1.0000 -0.0004
## 60 44948347000.3714 nan 1.0000 0.0023
## 80 44948347002.0234 nan 1.0000 -0.2762
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1304 nan 1.0000 0.0574
## 2 1.0541 nan 1.0000 0.0276
## 3 1.0071 nan 1.0000 0.0208
## 4 0.9742 nan 1.0000 0.0046
## 5 0.9643 nan 1.0000 -0.0235
## 6 0.9607 nan 1.0000 -0.0146
## 7 0.9379 nan 1.0000 0.0028
## 8 0.9346 nan 1.0000 -0.0164
## 9 0.9502 nan 1.0000 -0.0311
## 10 0.9397 nan 1.0000 -0.0114
## 20 0.8765 nan 1.0000 -0.0005
## 40 0.8000 nan 1.0000 -0.0085
## 60 0.7465 nan 1.0000 -0.0173
## 80 1.9083 nan 1.0000 -0.0000
## 100 42217376975772114944.0000 nan 1.0000 -0.0027
## 120 42217376975772114944.0000 nan 1.0000 -0.0018
## 140 42217376975772114944.0000 nan 1.0000 -0.0229
## 160 42217376975772114944.0000 nan 1.0000 -0.0005
## 180 42217376975772114944.0000 nan 1.0000 0.0093
## 200 42217376975772114944.0000 nan 1.0000 0.0046
## 220 42217376975774302208.0000 nan 1.0000 -1820470.4146
## 240 42217376975774302208.0000 nan 1.0000 -0.0109
## 260 42217376975774302208.0000 nan 1.0000 -0.0019
## 280 42217376975774302208.0000 nan 1.0000 0.0007
## 300 42217376975774302208.0000 nan 1.0000 -0.0247
## 320 42217376975774302208.0000 nan 1.0000 -0.2193
## 340 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0910 nan 1.0000 0.0638
## 2 0.9909 nan 1.0000 0.0217
## 3 0.9547 nan 1.0000 -0.0069
## 4 0.9452 nan 1.0000 -0.0264
## 5 0.9204 nan 1.0000 0.0045
## 6 0.8893 nan 1.0000 -0.0088
## 7 0.8959 nan 1.0000 -0.0329
## 8 0.8680 nan 1.0000 -0.0007
## 9 0.9534 nan 1.0000 -0.1195
## 10 0.9099 nan 1.0000 0.0023
## 20 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0818 nan 1.0000 0.0546
## 2 0.9991 nan 1.0000 0.0072
## 3 0.9888 nan 1.0000 -0.0340
## 4 0.9443 nan 1.0000 -0.0120
## 5 1.0251 nan 1.0000 -0.0970
## 6 1.0156 nan 1.0000 -0.0164
## 7 1.0192 nan 1.0000 -0.0430
## 8 0.9368 nan 1.0000 0.0052
## 9 0.9169 nan 1.0000 -0.0091
## 10 0.9224 nan 1.0000 -0.0315
## 20 55.6658 nan 1.0000 -0.0560
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0778 nan 1.0000 0.0988
## 2 1.0059 nan 1.0000 0.0187
## 3 1.0073 nan 1.0000 -0.0426
## 4 0.9823 nan 1.0000 -0.0185
## 5 0.9812 nan 1.0000 -0.0194
## 6 0.9683 nan 1.0000 -0.0188
## 7 0.9436 nan 1.0000 -0.0195
## 8 0.9087 nan 1.0000 -0.0263
## 9 0.9863 nan 1.0000 -0.0900
## 10 0.9683 nan 1.0000 -0.0276
## 20 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0472 nan 1.0000 0.1070
## 2 0.9571 nan 1.0000 0.0166
## 3 0.9199 nan 1.0000 -0.0170
## 4 0.9128 nan 1.0000 -0.0308
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## 6 0.8921 nan 1.0000 -0.0468
## 7 0.8512 nan 1.0000 -0.0070
## 8 0.8420 nan 1.0000 -0.0259
## 9 0.8510 nan 1.0000 -0.0511
## 10 0.8238 nan 1.0000 -0.0114
## 20 0.9330 nan 1.0000 -0.1019
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0871 nan 1.0000 0.0498
## 2 1.0198 nan 1.0000 -0.0049
## 3 0.9687 nan 1.0000 -0.0066
## 4 0.9131 nan 1.0000 0.0045
## 5 0.9310 nan 1.0000 -0.0605
## 6 0.9135 nan 1.0000 -0.0182
## 7 0.8948 nan 1.0000 -0.0173
## 8 0.8956 nan 1.0000 -0.0439
## 9 0.9202 nan 1.0000 -0.0657
## 10 0.9132 nan 1.0000 -0.0605
## 20 1.7378 nan 1.0000 -0.7957
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0768 nan 1.0000 0.0516
## 2 0.9758 nan 1.0000 0.0330
## 3 0.9234 nan 1.0000 0.0012
## 4 0.9226 nan 1.0000 -0.0423
## 5 0.9045 nan 1.0000 -0.0444
## 6 0.8830 nan 1.0000 -0.0195
## 7 0.8731 nan 1.0000 -0.0333
## 8 0.8482 nan 1.0000 -0.0212
## 9 0.8308 nan 1.0000 -0.0149
## 10 0.8336 nan 1.0000 -0.0350
## 20 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## 3 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
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## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
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## 10 1.2897 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2791 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
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## 3 1.2923 nan 0.0010 0.0002
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## 5 1.2915 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
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## 3 1.2923 nan 0.0010 0.0002
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## 5 1.2916 nan 0.0010 0.0001
## 6 1.2913 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## 3 1.2919 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## 3 1.2919 nan 0.0010 0.0002
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## 6 1.2905 nan 0.0010 0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## 3 1.2920 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0002
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## 6 1.2899 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2923 nan 0.0010 0.0002
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## 280 1.1718 nan 0.0010 0.0002
## 300 1.1651 nan 0.0010 0.0002
## 320 1.1583 nan 0.0010 0.0001
## 340 1.1520 nan 0.0010 0.0001
## 360 1.1456 nan 0.0010 0.0001
## 380 1.1394 nan 0.0010 0.0001
## 400 1.1336 nan 0.0010 0.0001
## 420 1.1278 nan 0.0010 0.0001
## 440 1.1222 nan 0.0010 0.0001
## 460 1.1168 nan 0.0010 0.0001
## 480 1.1115 nan 0.0010 0.0001
## 500 1.1062 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2545 nan 0.1000 0.0165
## 2 1.2240 nan 0.1000 0.0121
## 3 1.1980 nan 0.1000 0.0097
## 4 1.1781 nan 0.1000 0.0083
## 5 1.1624 nan 0.1000 0.0071
## 6 1.1491 nan 0.1000 0.0065
## 7 1.1319 nan 0.1000 0.0057
## 8 1.1176 nan 0.1000 0.0055
## 9 1.1053 nan 0.1000 0.0047
## 10 1.0949 nan 0.1000 0.0041
## 20 1.0154 nan 0.1000 0.0029
## 40 0.9352 nan 0.1000 0.0004
## 60 0.8980 nan 0.1000 -0.0002
## 80 0.8663 nan 0.1000 0.0003
## 100 0.8515 nan 0.1000 -0.0009
## 120 0.8406 nan 0.1000 -0.0002
## 140 0.8319 nan 0.1000 -0.0012
## 160 0.8219 nan 0.1000 -0.0011
## 180 0.8119 nan 0.1000 -0.0004
## 200 0.8044 nan 0.1000 -0.0011
## 220 0.7937 nan 0.1000 0.0001
## 240 0.7890 nan 0.1000 -0.0028
## 260 0.7825 nan 0.1000 -0.0006
## 280 0.7753 nan 0.1000 -0.0013
## 300 0.7694 nan 0.1000 -0.0006
## 320 0.7640 nan 0.1000 -0.0013
## 340 0.7585 nan 0.1000 -0.0013
## 360 0.7538 nan 0.1000 -0.0008
## 380 0.7462 nan 0.1000 -0.0014
## 400 0.7414 nan 0.1000 -0.0004
## 420 0.7389 nan 0.1000 -0.0014
## 440 0.7342 nan 0.1000 -0.0006
## 460 0.7306 nan 0.1000 -0.0011
## 480 0.7249 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2613 nan 0.1000 0.0161
## 2 1.2339 nan 0.1000 0.0137
## 3 1.2114 nan 0.1000 0.0089
## 4 1.1919 nan 0.1000 0.0062
## 5 1.1696 nan 0.1000 0.0073
## 6 1.1528 nan 0.1000 0.0079
## 7 1.1363 nan 0.1000 0.0057
## 8 1.1237 nan 0.1000 0.0052
## 9 1.1108 nan 0.1000 0.0040
## 10 1.0975 nan 0.1000 0.0052
## 20 1.0120 nan 0.1000 0.0020
## 40 0.9357 nan 0.1000 -0.0002
## 60 0.8988 nan 0.1000 0.0004
## 80 0.8759 nan 0.1000 -0.0004
## 100 0.8573 nan 0.1000 -0.0007
## 120 0.8437 nan 0.1000 -0.0015
## 140 0.8265 nan 0.1000 -0.0014
## 160 0.8161 nan 0.1000 -0.0003
## 180 0.8052 nan 0.1000 -0.0003
## 200 0.7992 nan 0.1000 -0.0010
## 220 0.7908 nan 0.1000 -0.0006
## 240 0.7835 nan 0.1000 -0.0009
## 260 0.7779 nan 0.1000 -0.0009
## 280 0.7701 nan 0.1000 -0.0002
## 300 0.7646 nan 0.1000 -0.0011
## 320 0.7585 nan 0.1000 -0.0013
## 340 0.7508 nan 0.1000 -0.0008
## 360 0.7451 nan 0.1000 -0.0016
## 380 0.7388 nan 0.1000 -0.0010
## 400 0.7361 nan 0.1000 -0.0009
## 420 0.7304 nan 0.1000 0.0001
## 440 0.7266 nan 0.1000 -0.0012
## 460 0.7234 nan 0.1000 -0.0013
## 480 0.7184 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2586 nan 0.1000 0.0171
## 2 1.2335 nan 0.1000 0.0115
## 3 1.2070 nan 0.1000 0.0115
## 4 1.1948 nan 0.1000 0.0048
## 5 1.1732 nan 0.1000 0.0079
## 6 1.1571 nan 0.1000 0.0061
## 7 1.1415 nan 0.1000 0.0072
## 8 1.1257 nan 0.1000 0.0060
## 9 1.1115 nan 0.1000 0.0046
## 10 1.0986 nan 0.1000 0.0048
## 20 1.0176 nan 0.1000 0.0021
## 40 0.9383 nan 0.1000 -0.0002
## 60 0.9011 nan 0.1000 -0.0008
## 80 0.8768 nan 0.1000 -0.0006
## 100 0.8584 nan 0.1000 -0.0004
## 120 0.8446 nan 0.1000 -0.0008
## 140 0.8307 nan 0.1000 -0.0014
## 160 0.8184 nan 0.1000 -0.0010
## 180 0.8103 nan 0.1000 -0.0006
## 200 0.8011 nan 0.1000 -0.0003
## 220 0.7938 nan 0.1000 -0.0012
## 240 0.7857 nan 0.1000 -0.0010
## 260 0.7788 nan 0.1000 -0.0011
## 280 0.7707 nan 0.1000 -0.0008
## 300 0.7668 nan 0.1000 -0.0004
## 320 0.7619 nan 0.1000 -0.0016
## 340 0.7532 nan 0.1000 -0.0004
## 360 0.7481 nan 0.1000 -0.0011
## 380 0.7423 nan 0.1000 -0.0007
## 400 0.7372 nan 0.1000 -0.0011
## 420 0.7315 nan 0.1000 -0.0019
## 440 0.7268 nan 0.1000 -0.0016
## 460 0.7221 nan 0.1000 -0.0000
## 480 0.7164 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2529 nan 0.1000 0.0221
## 2 1.2182 nan 0.1000 0.0149
## 3 1.1973 nan 0.1000 0.0081
## 4 1.1649 nan 0.1000 0.0160
## 5 1.1449 nan 0.1000 0.0092
## 6 1.1260 nan 0.1000 0.0061
## 7 1.1018 nan 0.1000 0.0091
## 8 1.0782 nan 0.1000 0.0103
## 9 1.0606 nan 0.1000 0.0065
## 10 1.0444 nan 0.1000 0.0050
## 20 0.9515 nan 0.1000 0.0014
## 40 0.8679 nan 0.1000 0.0012
## 60 0.8139 nan 0.1000 -0.0010
## 80 0.7766 nan 0.1000 -0.0010
## 100 0.7526 nan 0.1000 -0.0017
## 120 0.7267 nan 0.1000 -0.0012
## 140 0.7078 nan 0.1000 -0.0006
## 160 0.6860 nan 0.1000 -0.0007
## 180 0.6626 nan 0.1000 -0.0004
## 200 0.6444 nan 0.1000 -0.0007
## 220 0.6247 nan 0.1000 -0.0002
## 240 0.6076 nan 0.1000 -0.0008
## 260 0.5939 nan 0.1000 -0.0006
## 280 0.5747 nan 0.1000 -0.0009
## 300 0.5602 nan 0.1000 -0.0017
## 320 0.5442 nan 0.1000 -0.0009
## 340 0.5320 nan 0.1000 -0.0013
## 360 0.5198 nan 0.1000 -0.0006
## 380 0.5082 nan 0.1000 -0.0009
## 400 0.4984 nan 0.1000 -0.0002
## 420 0.4867 nan 0.1000 -0.0016
## 440 0.4710 nan 0.1000 -0.0005
## 460 0.4613 nan 0.1000 -0.0014
## 480 0.4520 nan 0.1000 -0.0013
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2452 nan 0.1000 0.0209
## 2 1.2095 nan 0.1000 0.0181
## 3 1.1785 nan 0.1000 0.0126
## 4 1.1501 nan 0.1000 0.0115
## 5 1.1232 nan 0.1000 0.0105
## 6 1.1000 nan 0.1000 0.0074
## 7 1.0861 nan 0.1000 0.0034
## 8 1.0707 nan 0.1000 0.0049
## 9 1.0524 nan 0.1000 0.0056
## 10 1.0380 nan 0.1000 0.0059
## 20 0.9475 nan 0.1000 0.0003
## 40 0.8667 nan 0.1000 -0.0015
## 60 0.8236 nan 0.1000 -0.0042
## 80 0.7864 nan 0.1000 -0.0004
## 100 0.7533 nan 0.1000 -0.0021
## 120 0.7300 nan 0.1000 -0.0010
## 140 0.7094 nan 0.1000 -0.0015
## 160 0.6908 nan 0.1000 -0.0009
## 180 0.6708 nan 0.1000 -0.0007
## 200 0.6505 nan 0.1000 -0.0004
## 220 0.6292 nan 0.1000 -0.0013
## 240 0.6120 nan 0.1000 -0.0008
## 260 0.5949 nan 0.1000 -0.0010
## 280 0.5802 nan 0.1000 -0.0009
## 300 0.5661 nan 0.1000 -0.0012
## 320 0.5547 nan 0.1000 -0.0010
## 340 0.5418 nan 0.1000 -0.0008
## 360 0.5305 nan 0.1000 -0.0014
## 380 0.5126 nan 0.1000 -0.0005
## 400 0.5012 nan 0.1000 -0.0019
## 420 0.4894 nan 0.1000 -0.0015
## 440 0.4755 nan 0.1000 -0.0009
## 460 0.4648 nan 0.1000 -0.0018
## 480 0.4531 nan 0.1000 -0.0002
## 500 0.4414 nan 0.1000 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2507 nan 0.1000 0.0213
## 2 1.2124 nan 0.1000 0.0181
## 3 1.1798 nan 0.1000 0.0131
## 4 1.1506 nan 0.1000 0.0137
## 5 1.1266 nan 0.1000 0.0100
## 6 1.1058 nan 0.1000 0.0082
## 7 1.0842 nan 0.1000 0.0069
## 8 1.0694 nan 0.1000 0.0042
## 9 1.0565 nan 0.1000 0.0040
## 10 1.0421 nan 0.1000 0.0049
## 20 0.9537 nan 0.1000 0.0022
## 40 0.8629 nan 0.1000 -0.0004
## 60 0.8135 nan 0.1000 0.0007
## 80 0.7767 nan 0.1000 -0.0026
## 100 0.7500 nan 0.1000 -0.0010
## 120 0.7267 nan 0.1000 -0.0009
## 140 0.7070 nan 0.1000 -0.0012
## 160 0.6857 nan 0.1000 -0.0008
## 180 0.6654 nan 0.1000 -0.0009
## 200 0.6462 nan 0.1000 -0.0013
## 220 0.6305 nan 0.1000 -0.0015
## 240 0.6158 nan 0.1000 -0.0012
## 260 0.5980 nan 0.1000 -0.0007
## 280 0.5831 nan 0.1000 -0.0009
## 300 0.5666 nan 0.1000 -0.0006
## 320 0.5501 nan 0.1000 -0.0013
## 340 0.5363 nan 0.1000 -0.0010
## 360 0.5253 nan 0.1000 -0.0010
## 380 0.5117 nan 0.1000 -0.0014
## 400 0.4997 nan 0.1000 -0.0014
## 420 0.4903 nan 0.1000 -0.0019
## 440 0.4787 nan 0.1000 -0.0023
## 460 0.4675 nan 0.1000 -0.0006
## 480 0.4583 nan 0.1000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2449 nan 0.1000 0.0214
## 2 1.2016 nan 0.1000 0.0168
## 3 1.1590 nan 0.1000 0.0163
## 4 1.1273 nan 0.1000 0.0149
## 5 1.1015 nan 0.1000 0.0105
## 6 1.0746 nan 0.1000 0.0082
## 7 1.0542 nan 0.1000 0.0057
## 8 1.0374 nan 0.1000 0.0059
## 9 1.0226 nan 0.1000 0.0042
## 10 1.0064 nan 0.1000 0.0045
## 20 0.9073 nan 0.1000 -0.0010
## 40 0.8160 nan 0.1000 -0.0006
## 60 0.7583 nan 0.1000 -0.0015
## 80 0.7098 nan 0.1000 -0.0015
## 100 0.6760 nan 0.1000 -0.0015
## 120 0.6413 nan 0.1000 -0.0003
## 140 0.6151 nan 0.1000 -0.0005
## 160 0.5821 nan 0.1000 -0.0015
## 180 0.5558 nan 0.1000 -0.0016
## 200 0.5271 nan 0.1000 0.0001
## 220 0.5032 nan 0.1000 -0.0011
## 240 0.4826 nan 0.1000 -0.0007
## 260 0.4578 nan 0.1000 -0.0010
## 280 0.4394 nan 0.1000 -0.0010
## 300 0.4182 nan 0.1000 -0.0007
## 320 0.3999 nan 0.1000 -0.0006
## 340 0.3850 nan 0.1000 -0.0009
## 360 0.3693 nan 0.1000 -0.0008
## 380 0.3556 nan 0.1000 -0.0009
## 400 0.3443 nan 0.1000 -0.0014
## 420 0.3313 nan 0.1000 -0.0010
## 440 0.3191 nan 0.1000 -0.0005
## 460 0.3076 nan 0.1000 -0.0009
## 480 0.2976 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2393 nan 0.1000 0.0249
## 2 1.1982 nan 0.1000 0.0183
## 3 1.1587 nan 0.1000 0.0173
## 4 1.1270 nan 0.1000 0.0105
## 5 1.1005 nan 0.1000 0.0122
## 6 1.0743 nan 0.1000 0.0106
## 7 1.0590 nan 0.1000 0.0033
## 8 1.0376 nan 0.1000 0.0067
## 9 1.0185 nan 0.1000 0.0055
## 10 1.0022 nan 0.1000 0.0065
## 20 0.9101 nan 0.1000 0.0006
## 40 0.8138 nan 0.1000 -0.0018
## 60 0.7575 nan 0.1000 -0.0013
## 80 0.7157 nan 0.1000 -0.0003
## 100 0.6758 nan 0.1000 -0.0016
## 120 0.6463 nan 0.1000 -0.0009
## 140 0.6197 nan 0.1000 -0.0014
## 160 0.5881 nan 0.1000 -0.0008
## 180 0.5580 nan 0.1000 -0.0009
## 200 0.5306 nan 0.1000 -0.0006
## 220 0.5127 nan 0.1000 -0.0016
## 240 0.4890 nan 0.1000 -0.0016
## 260 0.4675 nan 0.1000 -0.0009
## 280 0.4479 nan 0.1000 -0.0006
## 300 0.4332 nan 0.1000 -0.0026
## 320 0.4145 nan 0.1000 -0.0013
## 340 0.3993 nan 0.1000 -0.0017
## 360 0.3833 nan 0.1000 -0.0011
## 380 0.3678 nan 0.1000 -0.0001
## 400 0.3505 nan 0.1000 -0.0015
## 420 0.3372 nan 0.1000 -0.0008
## 440 0.3250 nan 0.1000 -0.0010
## 460 0.3144 nan 0.1000 -0.0008
## 480 0.3041 nan 0.1000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2440 nan 0.1000 0.0238
## 2 1.1997 nan 0.1000 0.0212
## 3 1.1617 nan 0.1000 0.0125
## 4 1.1268 nan 0.1000 0.0149
## 5 1.1001 nan 0.1000 0.0095
## 6 1.0743 nan 0.1000 0.0115
## 7 1.0533 nan 0.1000 0.0074
## 8 1.0382 nan 0.1000 0.0048
## 9 1.0209 nan 0.1000 0.0062
## 10 1.0092 nan 0.1000 0.0015
## 20 0.9039 nan 0.1000 0.0012
## 40 0.8128 nan 0.1000 0.0005
## 60 0.7527 nan 0.1000 -0.0024
## 80 0.7078 nan 0.1000 -0.0012
## 100 0.6715 nan 0.1000 -0.0020
## 120 0.6375 nan 0.1000 -0.0006
## 140 0.6059 nan 0.1000 -0.0011
## 160 0.5764 nan 0.1000 -0.0008
## 180 0.5551 nan 0.1000 -0.0019
## 200 0.5303 nan 0.1000 -0.0022
## 220 0.5100 nan 0.1000 -0.0009
## 240 0.4856 nan 0.1000 -0.0006
## 260 0.4634 nan 0.1000 -0.0008
## 280 0.4409 nan 0.1000 -0.0017
## 300 0.4237 nan 0.1000 -0.0008
## 320 0.4081 nan 0.1000 -0.0006
## 340 0.3926 nan 0.1000 -0.0014
## 360 0.3774 nan 0.1000 -0.0009
## 380 0.3620 nan 0.1000 -0.0011
## 400 0.3504 nan 0.1000 -0.0008
## 420 0.3376 nan 0.1000 -0.0015
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## 460 0.3126 nan 0.1000 -0.0013
## 480 0.2994 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2210 nan 0.2000 0.0307
## 2 1.1812 nan 0.2000 0.0133
## 3 1.1548 nan 0.2000 0.0087
## 4 1.1249 nan 0.2000 0.0131
## 5 1.0990 nan 0.2000 0.0102
## 6 1.0757 nan 0.2000 0.0063
## 7 1.0557 nan 0.2000 0.0095
## 8 1.0419 nan 0.2000 0.0025
## 9 1.0236 nan 0.2000 0.0052
## 10 1.0134 nan 0.2000 0.0025
## 20 0.9400 nan 0.2000 0.0012
## 40 0.8763 nan 0.2000 -0.0019
## 60 0.8442 nan 0.2000 -0.0013
## 80 0.8228 nan 0.2000 -0.0008
## 100 0.8040 nan 0.2000 -0.0025
## 120 0.7921 nan 0.2000 -0.0043
## 140 0.7817 nan 0.2000 -0.0015
## 160 0.7676 nan 0.2000 -0.0012
## 180 0.7593 nan 0.2000 -0.0015
## 200 0.7507 nan 0.2000 -0.0014
## 220 0.7405 nan 0.2000 -0.0007
## 240 0.7355 nan 0.2000 -0.0021
## 260 0.7246 nan 0.2000 -0.0014
## 280 0.7166 nan 0.2000 -0.0013
## 300 0.7118 nan 0.2000 -0.0006
## 320 0.7059 nan 0.2000 -0.0018
## 340 0.6986 nan 0.2000 -0.0007
## 360 0.6916 nan 0.2000 -0.0006
## 380 0.6840 nan 0.2000 -0.0034
## 400 0.6790 nan 0.2000 -0.0020
## 420 0.6753 nan 0.2000 -0.0015
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## 460 0.6634 nan 0.2000 -0.0022
## 480 0.6534 nan 0.2000 -0.0013
## 500 0.6492 nan 0.2000 -0.0031
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2271 nan 0.2000 0.0264
## 2 1.1800 nan 0.2000 0.0168
## 3 1.1493 nan 0.2000 0.0124
## 4 1.1215 nan 0.2000 0.0112
## 5 1.0966 nan 0.2000 0.0100
## 6 1.0823 nan 0.2000 0.0061
## 7 1.0639 nan 0.2000 0.0060
## 8 1.0421 nan 0.2000 0.0072
## 9 1.0301 nan 0.2000 0.0033
## 10 1.0146 nan 0.2000 0.0017
## 20 0.9449 nan 0.2000 -0.0003
## 40 0.8839 nan 0.2000 -0.0016
## 60 0.8509 nan 0.2000 -0.0030
## 80 0.8308 nan 0.2000 -0.0032
## 100 0.8147 nan 0.2000 -0.0034
## 120 0.7998 nan 0.2000 -0.0009
## 140 0.7835 nan 0.2000 -0.0008
## 160 0.7711 nan 0.2000 -0.0021
## 180 0.7642 nan 0.2000 -0.0011
## 200 0.7508 nan 0.2000 -0.0018
## 220 0.7415 nan 0.2000 -0.0027
## 240 0.7287 nan 0.2000 -0.0008
## 260 0.7191 nan 0.2000 -0.0026
## 280 0.7135 nan 0.2000 -0.0024
## 300 0.7040 nan 0.2000 -0.0003
## 320 0.6963 nan 0.2000 -0.0038
## 340 0.6888 nan 0.2000 -0.0018
## 360 0.6775 nan 0.2000 -0.0019
## 380 0.6713 nan 0.2000 -0.0027
## 400 0.6658 nan 0.2000 -0.0009
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## 460 0.6461 nan 0.2000 -0.0011
## 480 0.6399 nan 0.2000 -0.0023
## 500 0.6350 nan 0.2000 -0.0032
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2225 nan 0.2000 0.0279
## 2 1.1850 nan 0.2000 0.0191
## 3 1.1548 nan 0.2000 0.0129
## 4 1.1246 nan 0.2000 0.0121
## 5 1.0981 nan 0.2000 0.0106
## 6 1.0751 nan 0.2000 0.0115
## 7 1.0607 nan 0.2000 0.0045
## 8 1.0490 nan 0.2000 0.0008
## 9 1.0318 nan 0.2000 0.0041
## 10 1.0176 nan 0.2000 0.0067
## 20 0.9390 nan 0.2000 -0.0004
## 40 0.8749 nan 0.2000 -0.0015
## 60 0.8401 nan 0.2000 -0.0028
## 80 0.8176 nan 0.2000 -0.0023
## 100 0.8015 nan 0.2000 -0.0018
## 120 0.7869 nan 0.2000 -0.0016
## 140 0.7790 nan 0.2000 -0.0006
## 160 0.7674 nan 0.2000 -0.0016
## 180 0.7539 nan 0.2000 -0.0019
## 200 0.7440 nan 0.2000 -0.0023
## 220 0.7307 nan 0.2000 -0.0012
## 240 0.7245 nan 0.2000 -0.0030
## 260 0.7115 nan 0.2000 -0.0013
## 280 0.7087 nan 0.2000 -0.0017
## 300 0.6980 nan 0.2000 -0.0023
## 320 0.6915 nan 0.2000 -0.0034
## 340 0.6838 nan 0.2000 -0.0018
## 360 0.6786 nan 0.2000 -0.0005
## 380 0.6762 nan 0.2000 -0.0020
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## 460 0.6461 nan 0.2000 -0.0002
## 480 0.6425 nan 0.2000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2110 nan 0.2000 0.0370
## 2 1.1509 nan 0.2000 0.0283
## 3 1.1072 nan 0.2000 0.0177
## 4 1.0727 nan 0.2000 0.0101
## 5 1.0491 nan 0.2000 0.0080
## 6 1.0197 nan 0.2000 0.0098
## 7 0.9978 nan 0.2000 0.0080
## 8 0.9784 nan 0.2000 0.0068
## 9 0.9644 nan 0.2000 0.0022
## 10 0.9533 nan 0.2000 0.0015
## 20 0.8675 nan 0.2000 -0.0005
## 40 0.7883 nan 0.2000 0.0008
## 60 0.7286 nan 0.2000 -0.0046
## 80 0.6842 nan 0.2000 -0.0042
## 100 0.6519 nan 0.2000 -0.0011
## 120 2.2376 nan 0.2000 -0.0007
## 140 inf nan 0.2000 nan
## 160 inf nan 0.2000 nan
## 180 inf nan 0.2000 nan
## 200 inf nan 0.2000 nan
## 220 inf nan 0.2000 nan
## 240 inf nan 0.2000 nan
## 260 inf nan 0.2000 nan
## 280 inf nan 0.2000 nan
## 300 inf nan 0.2000 nan
## 320 inf nan 0.2000 -0.0005
## 340 inf nan 0.2000 -0.0004
## 360 inf nan 0.2000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2145 nan 0.2000 0.0338
## 2 1.1542 nan 0.2000 0.0281
## 3 1.1095 nan 0.2000 0.0177
## 4 1.0762 nan 0.2000 0.0119
## 5 1.0518 nan 0.2000 0.0064
## 6 1.0321 nan 0.2000 0.0043
## 7 1.0101 nan 0.2000 0.0075
## 8 0.9886 nan 0.2000 0.0048
## 9 0.9721 nan 0.2000 0.0011
## 10 0.9630 nan 0.2000 0.0007
## 20 0.8714 nan 0.2000 -0.0030
## 40 0.7912 nan 0.2000 -0.0016
## 60 0.7377 nan 0.2000 -0.0020
## 80 0.6977 nan 0.2000 -0.0020
## 100 0.6627 nan 0.2000 -0.0003
## 120 0.6277 nan 0.2000 -0.0018
## 140 0.5965 nan 0.2000 -0.0045
## 160 0.5649 nan 0.2000 -0.0002
## 180 0.5344 nan 0.2000 -0.0021
## 200 0.5145 nan 0.2000 -0.0023
## 220 0.4886 nan 0.2000 -0.0005
## 240 0.4671 nan 0.2000 -0.0011
## 260 0.4509 nan 0.2000 -0.0004
## 280 0.4315 nan 0.2000 -0.0017
## 300 0.4223 nan 0.2000 -0.0031
## 320 0.4066 nan 0.2000 -0.0023
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## 380 0.3616 nan 0.2000 -0.0003
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## 460 0.3079 nan 0.2000 -0.0016
## 480 0.2940 nan 0.2000 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2069 nan 0.2000 0.0367
## 2 1.1587 nan 0.2000 0.0168
## 3 1.1067 nan 0.2000 0.0196
## 4 1.0728 nan 0.2000 0.0118
## 5 1.0484 nan 0.2000 0.0046
## 6 1.0268 nan 0.2000 0.0086
## 7 1.0016 nan 0.2000 0.0074
## 8 0.9851 nan 0.2000 0.0019
## 9 0.9732 nan 0.2000 0.0040
## 10 0.9580 nan 0.2000 0.0025
## 20 0.8712 nan 0.2000 -0.0029
## 40 0.7900 nan 0.2000 -0.0019
## 60 0.7329 nan 0.2000 -0.0016
## 80 0.6884 nan 0.2000 -0.0002
## 100 0.6551 nan 0.2000 -0.0015
## 120 0.6167 nan 0.2000 -0.0016
## 140 0.5833 nan 0.2000 -0.0031
## 160 0.5552 nan 0.2000 -0.0028
## 180 0.5275 nan 0.2000 -0.0031
## 200 0.5005 nan 0.2000 -0.0014
## 220 0.4767 nan 0.2000 -0.0014
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## 280 0.4112 nan 0.2000 -0.0003
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## 320 0.3798 nan 0.2000 -0.0009
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## 380 0.3350 nan 0.2000 -0.0011
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## 440 0.2980 nan 0.2000 -0.0007
## 460 0.2895 nan 0.2000 -0.0017
## 480 0.2821 nan 0.2000 -0.0033
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1930 nan 0.2000 0.0524
## 2 1.1217 nan 0.2000 0.0270
## 3 1.0692 nan 0.2000 0.0212
## 4 1.0309 nan 0.2000 0.0162
## 5 1.0014 nan 0.2000 0.0116
## 6 0.9734 nan 0.2000 0.0083
## 7 0.9503 nan 0.2000 0.0062
## 8 0.9346 nan 0.2000 -0.0005
## 9 0.9232 nan 0.2000 -0.0006
## 10 0.9093 nan 0.2000 0.0000
## 20 0.8269 nan 0.2000 -0.0007
## 40 0.7188 nan 0.2000 -0.0026
## 60 0.6575 nan 0.2000 -0.0042
## 80 0.6027 nan 0.2000 -0.0014
## 100 0.5479 nan 0.2000 -0.0020
## 120 0.5059 nan 0.2000 -0.0072
## 140 inf nan 0.2000 -0.0013
## 160 inf nan 0.2000 nan
## 180 inf nan 0.2000 nan
## 200 inf nan 0.2000 nan
## 220 inf nan 0.2000 nan
## 240 inf nan 0.2000 nan
## 260 inf nan 0.2000 nan
## 280 inf nan 0.2000 nan
## 300 inf nan 0.2000 nan
## 320 inf nan 0.2000 nan
## 340 inf nan 0.2000 -0.0010
## 360 inf nan 0.2000 nan
## 380 inf nan 0.2000 nan
## 400 inf nan 0.2000 nan
## 420 inf nan 0.2000 nan
## 440 inf nan 0.2000 -0.0004
## 460 inf nan 0.2000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2083 nan 0.2000 0.0336
## 2 1.1297 nan 0.2000 0.0352
## 3 1.0651 nan 0.2000 0.0273
## 4 1.0330 nan 0.2000 0.0108
## 5 1.0063 nan 0.2000 0.0049
## 6 0.9817 nan 0.2000 0.0096
## 7 0.9582 nan 0.2000 0.0066
## 8 0.9370 nan 0.2000 0.0044
## 9 0.9217 nan 0.2000 0.0033
## 10 0.9075 nan 0.2000 0.0045
## 20 0.8255 nan 0.2000 -0.0047
## 40 0.7216 nan 0.2000 -0.0044
## 60 0.6426 nan 0.2000 -0.0020
## 80 0.5914 nan 0.2000 -0.0024
## 100 0.5356 nan 0.2000 -0.0014
## 120 0.4881 nan 0.2000 -0.0017
## 140 0.4527 nan 0.2000 -0.0028
## 160 0.4156 nan 0.2000 -0.0028
## 180 0.3877 nan 0.2000 -0.0031
## 200 0.3598 nan 0.2000 -0.0003
## 220 0.3307 nan 0.2000 -0.0012
## 240 0.3078 nan 0.2000 -0.0020
## 260 0.2873 nan 0.2000 -0.0017
## 280 0.2699 nan 0.2000 -0.0012
## 300 0.2490 nan 0.2000 -0.0012
## 320 0.2325 nan 0.2000 -0.0008
## 340 0.2195 nan 0.2000 -0.0012
## 360 0.2056 nan 0.2000 -0.0008
## 380 0.1935 nan 0.2000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2003 nan 0.2000 0.0466
## 2 1.1374 nan 0.2000 0.0228
## 3 1.0788 nan 0.2000 0.0249
## 4 1.0515 nan 0.2000 0.0030
## 5 1.0126 nan 0.2000 0.0108
## 6 0.9831 nan 0.2000 0.0101
## 7 0.9647 nan 0.2000 0.0056
## 8 0.9476 nan 0.2000 0.0031
## 9 0.9248 nan 0.2000 0.0048
## 10 0.9121 nan 0.2000 -0.0012
## 20 0.8174 nan 0.2000 -0.0008
## 40 0.7100 nan 0.2000 -0.0026
## 60 0.6384 nan 0.2000 -0.0027
## 80 0.5734 nan 0.2000 -0.0014
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## 140 0.4554 nan 0.2000 -0.0034
## 160 0.4153 nan 0.2000 -0.0010
## 180 0.3813 nan 0.2000 0.0003
## 200 0.3521 nan 0.2000 -0.0027
## 220 0.3267 nan 0.2000 -0.0026
## 240 0.3023 nan 0.2000 -0.0007
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## 320 0.2230 nan 0.2000 -0.0004
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## 380 0.1839 nan 0.2000 -0.0010
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## 480 0.1375 nan 0.2000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2017 nan 0.3000 0.0415
## 2 1.1517 nan 0.3000 0.0212
## 3 1.1136 nan 0.3000 0.0130
## 4 1.0838 nan 0.3000 0.0120
## 5 1.0518 nan 0.3000 0.0164
## 6 1.0375 nan 0.3000 0.0003
## 7 1.0213 nan 0.3000 0.0046
## 8 1.0004 nan 0.3000 0.0081
## 9 0.9847 nan 0.3000 0.0048
## 10 0.9748 nan 0.3000 0.0042
## 20 0.9104 nan 0.3000 0.0040
## 40 0.8551 nan 0.3000 -0.0039
## 60 0.8315 nan 0.3000 -0.0011
## 80 0.8039 nan 0.3000 -0.0001
## 100 0.7787 nan 0.3000 -0.0023
## 120 0.7578 nan 0.3000 -0.0016
## 140 0.7472 nan 0.3000 -0.0030
## 160 0.7309 nan 0.3000 -0.0016
## 180 0.7152 nan 0.3000 -0.0026
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## 220 0.6951 nan 0.3000 -0.0015
## 240 0.6814 nan 0.3000 -0.0008
## 260 0.6764 nan 0.3000 -0.0002
## 280 0.6665 nan 0.3000 -0.0012
## 300 0.6555 nan 0.3000 -0.0040
## 320 0.6473 nan 0.3000 -0.0014
## 340 0.6386 nan 0.3000 -0.0040
## 360 0.6273 nan 0.3000 -0.0011
## 380 0.6222 nan 0.3000 -0.0015
## 400 0.6139 nan 0.3000 -0.0032
## 420 0.6070 nan 0.3000 -0.0024
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## 460 0.5891 nan 0.3000 -0.0020
## 480 0.5795 nan 0.3000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2152 nan 0.3000 0.0296
## 2 1.1567 nan 0.3000 0.0246
## 3 1.1197 nan 0.3000 0.0143
## 4 1.0796 nan 0.3000 0.0161
## 5 1.0443 nan 0.3000 0.0156
## 6 1.0264 nan 0.3000 0.0081
## 7 1.0106 nan 0.3000 0.0034
## 8 0.9959 nan 0.3000 0.0016
## 9 0.9846 nan 0.3000 0.0007
## 10 0.9773 nan 0.3000 -0.0010
## 20 0.9033 nan 0.3000 -0.0026
## 40 0.8586 nan 0.3000 -0.0050
## 60 0.8228 nan 0.3000 -0.0037
## 80 0.7921 nan 0.3000 -0.0022
## 100 0.7696 nan 0.3000 -0.0024
## 120 0.7541 nan 0.3000 -0.0018
## 140 0.7390 nan 0.3000 -0.0027
## 160 0.7217 nan 0.3000 0.0000
## 180 0.7159 nan 0.3000 -0.0014
## 200 0.7019 nan 0.3000 -0.0039
## 220 0.6891 nan 0.3000 -0.0002
## 240 0.6771 nan 0.3000 -0.0028
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## 280 0.6599 nan 0.3000 -0.0025
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## 320 0.6378 nan 0.3000 -0.0011
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## 380 0.6133 nan 0.3000 -0.0033
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## 440 0.5983 nan 0.3000 -0.0013
## 460 0.5859 nan 0.3000 -0.0031
## 480 0.5774 nan 0.3000 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2062 nan 0.3000 0.0361
## 2 1.1528 nan 0.3000 0.0259
## 3 1.1159 nan 0.3000 0.0155
## 4 1.0869 nan 0.3000 0.0089
## 5 1.0526 nan 0.3000 0.0122
## 6 1.0279 nan 0.3000 0.0066
## 7 1.0062 nan 0.3000 0.0038
## 8 0.9938 nan 0.3000 0.0029
## 9 0.9867 nan 0.3000 0.0006
## 10 0.9762 nan 0.3000 0.0015
## 20 0.9098 nan 0.3000 -0.0016
## 40 0.8490 nan 0.3000 -0.0042
## 60 0.8164 nan 0.3000 0.0014
## 80 0.7907 nan 0.3000 -0.0026
## 100 0.7781 nan 0.3000 -0.0040
## 120 0.7618 nan 0.3000 -0.0023
## 140 0.7465 nan 0.3000 -0.0046
## 160 0.7308 nan 0.3000 -0.0049
## 180 0.7183 nan 0.3000 -0.0021
## 200 0.7064 nan 0.3000 -0.0023
## 220 0.6967 nan 0.3000 -0.0017
## 240 0.6837 nan 0.3000 -0.0027
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## 280 0.6693 nan 0.3000 -0.0047
## 300 0.6579 nan 0.3000 -0.0014
## 320 0.6457 nan 0.3000 -0.0017
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1792 nan 0.3000 0.0544
## 2 1.1260 nan 0.3000 0.0157
## 3 1.0694 nan 0.3000 0.0231
## 4 1.0294 nan 0.3000 0.0140
## 5 1.0037 nan 0.3000 0.0065
## 6 0.9830 nan 0.3000 0.0035
## 7 0.9638 nan 0.3000 0.0019
## 8 0.9443 nan 0.3000 0.0075
## 9 0.9359 nan 0.3000 -0.0048
## 10 0.9252 nan 0.3000 -0.0009
## 20 0.8417 nan 0.3000 -0.0023
## 40 0.7601 nan 0.3000 -0.0027
## 60 0.6967 nan 0.3000 -0.0007
## 80 0.6386 nan 0.3000 -0.0035
## 100 0.5953 nan 0.3000 -0.0030
## 120 0.5535 nan 0.3000 -0.0050
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## 160 0.4761 nan 0.3000 -0.0013
## 180 0.4414 nan 0.3000 0.0023
## 200 0.4110 nan 0.3000 -0.0038
## 220 0.3853 nan 0.3000 -0.0023
## 240 0.3655 nan 0.3000 -0.0036
## 260 0.3475 nan 0.3000 -0.0037
## 280 0.3212 nan 0.3000 -0.0003
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## 320 0.2892 nan 0.3000 -0.0026
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1616 nan 0.3000 0.0558
## 2 1.0906 nan 0.3000 0.0234
## 3 1.0400 nan 0.3000 0.0243
## 4 1.0083 nan 0.3000 0.0127
## 5 0.9836 nan 0.3000 0.0066
## 6 0.9709 nan 0.3000 -0.0013
## 7 0.9486 nan 0.3000 0.0027
## 8 0.9304 nan 0.3000 0.0023
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## 10 0.9070 nan 0.3000 -0.0003
## 20 0.8451 nan 0.3000 -0.0058
## 40 0.7637 nan 0.3000 -0.0024
## 60 0.7056 nan 0.3000 -0.0034
## 80 0.6450 nan 0.3000 0.0000
## 100 0.5931 nan 0.3000 -0.0018
## 120 0.5538 nan 0.3000 -0.0016
## 140 0.5117 nan 0.3000 -0.0036
## 160 0.4674 nan 0.3000 -0.0053
## 180 0.4302 nan 0.3000 -0.0051
## 200 0.4040 nan 0.3000 -0.0027
## 220 0.3751 nan 0.3000 -0.0052
## 240 0.3542 nan 0.3000 -0.0018
## 260 0.3307 nan 0.3000 -0.0009
## 280 0.3075 nan 0.3000 -0.0024
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## 360 0.2463 nan 0.3000 -0.0016
## 380 0.2326 nan 0.3000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1801 nan 0.3000 0.0586
## 2 1.0998 nan 0.3000 0.0265
## 3 1.0555 nan 0.3000 0.0164
## 4 1.0097 nan 0.3000 0.0133
## 5 0.9775 nan 0.3000 0.0095
## 6 0.9562 nan 0.3000 0.0010
## 7 0.9400 nan 0.3000 0.0013
## 8 0.9186 nan 0.3000 0.0048
## 9 0.9090 nan 0.3000 0.0026
## 10 0.9020 nan 0.3000 -0.0053
## 20 0.8245 nan 0.3000 -0.0004
## 40 0.7543 nan 0.3000 -0.0078
## 60 0.6947 nan 0.3000 -0.0061
## 80 0.6348 nan 0.3000 -0.0035
## 100 0.5908 nan 0.3000 -0.0055
## 120 0.5593 nan 0.3000 -0.0065
## 140 0.5172 nan 0.3000 -0.0016
## 160 0.4806 nan 0.3000 -0.0024
## 180 0.4445 nan 0.3000 -0.0026
## 200 0.4165 nan 0.3000 -0.0015
## 220 0.3918 nan 0.3000 -0.0011
## 240 0.3719 nan 0.3000 -0.0021
## 260 0.3588 nan 0.3000 -0.0007
## 280 0.3403 nan 0.3000 -0.0009
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## 320 0.3028 nan 0.3000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1600 nan 0.3000 0.0531
## 2 1.0852 nan 0.3000 0.0206
## 3 1.0433 nan 0.3000 0.0115
## 4 0.9968 nan 0.3000 0.0151
## 5 0.9636 nan 0.3000 0.0104
## 6 0.9391 nan 0.3000 -0.0018
## 7 0.9142 nan 0.3000 -0.0017
## 8 0.9070 nan 0.3000 -0.0106
## 9 0.8926 nan 0.3000 -0.0029
## 10 0.8757 nan 0.3000 0.0049
## 20 0.8055 nan 0.3000 -0.0048
## 40 0.6897 nan 0.3000 -0.0103
## 60 0.5890 nan 0.3000 -0.0024
## 80 0.4915 nan 0.3000 -0.0034
## 100 0.4459 nan 0.3000 -0.0061
## 120 0.4052 nan 0.3000 -0.0048
## 140 0.3584 nan 0.3000 -0.0019
## 160 0.3210 nan 0.3000 -0.0018
## 180 0.2828 nan 0.3000 -0.0016
## 200 0.2487 nan 0.3000 -0.0023
## 220 0.2254 nan 0.3000 -0.0012
## 240 0.2027 nan 0.3000 -0.0020
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## 280 0.1701 nan 0.3000 -0.0016
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## 320 0.1433 nan 0.3000 -0.0012
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## 380 0.1100 nan 0.3000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1625 nan 0.3000 0.0537
## 2 1.0814 nan 0.3000 0.0324
## 3 1.0348 nan 0.3000 0.0107
## 4 0.9882 nan 0.3000 0.0132
## 5 0.9545 nan 0.3000 0.0089
## 6 0.9304 nan 0.3000 -0.0010
## 7 0.9118 nan 0.3000 -0.0010
## 8 0.8972 nan 0.3000 0.0009
## 9 0.8811 nan 0.3000 -0.0009
## 10 0.8663 nan 0.3000 0.0012
## 20 0.7816 nan 0.3000 -0.0027
## 40 0.6581 nan 0.3000 -0.0072
## 60 0.5724 nan 0.3000 -0.0043
## 80 0.5060 nan 0.3000 -0.0044
## 100 0.4510 nan 0.3000 -0.0059
## 120 0.4032 nan 0.3000 -0.0020
## 140 0.3567 nan 0.3000 -0.0029
## 160 0.3220 nan 0.3000 -0.0027
## 180 0.2850 nan 0.3000 -0.0045
## 200 0.2617 nan 0.3000 -0.0008
## 220 0.2324 nan 0.3000 -0.0009
## 240 0.2120 nan 0.3000 -0.0015
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## 280 0.1783 nan 0.3000 -0.0023
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## 320 0.1481 nan 0.3000 -0.0009
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## 360 0.1234 nan 0.3000 -0.0015
## 380 0.1141 nan 0.3000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1621 nan 0.3000 0.0512
## 2 1.0648 nan 0.3000 0.0449
## 3 1.0208 nan 0.3000 0.0157
## 4 0.9868 nan 0.3000 0.0008
## 5 0.9551 nan 0.3000 0.0056
## 6 0.9201 nan 0.3000 0.0126
## 7 0.9032 nan 0.3000 0.0004
## 8 0.8882 nan 0.3000 -0.0013
## 9 0.8809 nan 0.3000 -0.0044
## 10 0.8650 nan 0.3000 -0.0037
## 20 0.7718 nan 0.3000 -0.0071
## 40 0.6563 nan 0.3000 -0.0041
## 60 0.5745 nan 0.3000 -0.0013
## 80 0.5182 nan 0.3000 -0.0042
## 100 0.4472 nan 0.3000 -0.0015
## 120 0.4504 nan 0.3000 -0.0503
## 140 0.3420 nan 0.3000 -0.0027
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## 180 0.2660 nan 0.3000 -0.0015
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## 240 0.1920 nan 0.3000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1618 nan 0.5000 0.0541
## 2 1.0952 nan 0.5000 0.0229
## 3 1.0391 nan 0.5000 0.0233
## 4 1.0171 nan 0.5000 0.0044
## 5 0.9780 nan 0.5000 0.0146
## 6 0.9635 nan 0.5000 0.0037
## 7 0.9534 nan 0.5000 -0.0003
## 8 0.9381 nan 0.5000 -0.0016
## 9 0.9306 nan 0.5000 -0.0004
## 10 0.9194 nan 0.5000 0.0027
## 20 0.8641 nan 0.5000 -0.0088
## 40 0.8212 nan 0.5000 -0.0147
## 60 0.9354 nan 0.5000 -0.0059
## 80 0.9268 nan 0.5000 -0.0007
## 100 0.9192 nan 0.5000 -0.0007
## 120 0.9051 nan 0.5000 -0.0069
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 -0.0003
## 180 inf nan 0.5000 nan
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## 440 inf nan 0.5000 0.0000
## 460 inf nan 0.5000 -0.0016
## 480 inf nan 0.5000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1649 nan 0.5000 0.0478
## 2 1.1018 nan 0.5000 0.0195
## 3 1.0494 nan 0.5000 0.0220
## 4 1.0060 nan 0.5000 0.0141
## 5 0.9844 nan 0.5000 0.0088
## 6 0.9749 nan 0.5000 -0.0075
## 7 0.9674 nan 0.5000 -0.0022
## 8 0.9520 nan 0.5000 -0.0009
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## 10 0.9310 nan 0.5000 -0.0013
## 20 0.8942 nan 0.5000 -0.0143
## 40 0.8178 nan 0.5000 -0.0061
## 60 0.7901 nan 0.5000 -0.0040
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## 100 0.7327 nan 0.5000 -0.0027
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## 140 0.6879 nan 0.5000 -0.0092
## 160 0.6660 nan 0.5000 -0.0019
## 180 0.6595 nan 0.5000 -0.0028
## 200 0.6408 nan 0.5000 -0.0036
## 220 0.6406 nan 0.5000 -0.0014
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## 260 0.6173 nan 0.5000 -0.0015
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## 300 0.5940 nan 0.5000 -0.0053
## 320 0.5866 nan 0.5000 -0.0017
## 340 0.5740 nan 0.5000 -0.0012
## 360 0.5606 nan 0.5000 -0.0028
## 380 0.5573 nan 0.5000 -0.0011
## 400 0.5442 nan 0.5000 -0.0028
## 420 0.5387 nan 0.5000 -0.0075
## 440 0.5268 nan 0.5000 -0.0025
## 460 0.5212 nan 0.5000 -0.0048
## 480 0.5034 nan 0.5000 -0.0005
## 500 0.5009 nan 0.5000 -0.0054
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1617 nan 0.5000 0.0603
## 2 1.1046 nan 0.5000 0.0180
## 3 1.0566 nan 0.5000 0.0134
## 4 1.0398 nan 0.5000 0.0007
## 5 0.9933 nan 0.5000 0.0144
## 6 0.9843 nan 0.5000 -0.0007
## 7 0.9614 nan 0.5000 0.0061
## 8 0.9463 nan 0.5000 0.0017
## 9 0.9347 nan 0.5000 0.0041
## 10 0.9277 nan 0.5000 0.0012
## 20 0.8705 nan 0.5000 -0.0009
## 40 0.8224 nan 0.5000 -0.0007
## 60 0.7808 nan 0.5000 -0.0003
## 80 0.7667 nan 0.5000 -0.0068
## 100 0.7440 nan 0.5000 -0.0050
## 120 0.7152 nan 0.5000 -0.0042
## 140 0.7077 nan 0.5000 -0.0074
## 160 0.6850 nan 0.5000 -0.0034
## 180 0.6734 nan 0.5000 -0.0021
## 200 0.6636 nan 0.5000 -0.0019
## 220 0.6492 nan 0.5000 -0.0055
## 240 0.6392 nan 0.5000 -0.0052
## 260 0.6271 nan 0.5000 -0.0063
## 280 0.6182 nan 0.5000 -0.0003
## 300 0.6111 nan 0.5000 -0.0103
## 320 0.5966 nan 0.5000 -0.0060
## 340 0.5906 nan 0.5000 -0.0064
## 360 0.5783 nan 0.5000 -0.0038
## 380 0.5601 nan 0.5000 -0.0019
## 400 0.5476 nan 0.5000 -0.0035
## 420 0.5338 nan 0.5000 -0.0035
## 440 0.5268 nan 0.5000 -0.0049
## 460 0.5242 nan 0.5000 -0.0022
## 480 0.5148 nan 0.5000 -0.0027
## 500 0.5094 nan 0.5000 -0.0056
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1207 nan 0.5000 0.0815
## 2 1.0391 nan 0.5000 0.0311
## 3 0.9924 nan 0.5000 0.0150
## 4 0.9626 nan 0.5000 0.0042
## 5 0.9322 nan 0.5000 0.0057
## 6 0.9024 nan 0.5000 -0.0001
## 7 0.9048 nan 0.5000 -0.0158
## 8 0.8881 nan 0.5000 -0.0038
## 9 0.8796 nan 0.5000 -0.0051
## 10 0.8718 nan 0.5000 -0.0055
## 20 inf nan 0.5000 -inf
## 40 inf nan 0.5000 nan
## 60 inf nan 0.5000 -0.0068
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 -0.0047
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 -0.0077
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1281 nan 0.5000 0.0703
## 2 1.0456 nan 0.5000 0.0234
## 3 0.9818 nan 0.5000 0.0206
## 4 0.9578 nan 0.5000 -0.0000
## 5 0.9333 nan 0.5000 -0.0000
## 6 0.9227 nan 0.5000 -0.0042
## 7 0.9032 nan 0.5000 -0.0013
## 8 0.8929 nan 0.5000 -0.0082
## 9 0.8765 nan 0.5000 0.0053
## 10 0.8536 nan 0.5000 0.0022
## 20 0.7827 nan 0.5000 -0.0150
## 40 0.6791 nan 0.5000 -0.0125
## 60 0.6049 nan 0.5000 -0.0066
## 80 0.5661 nan 0.5000 -0.0096
## 100 0.4978 nan 0.5000 -0.0054
## 120 0.4302 nan 0.5000 -0.0030
## 140 0.4052 nan 0.5000 -0.0052
## 160 0.3682 nan 0.5000 -0.0065
## 180 0.3363 nan 0.5000 -0.0123
## 200 0.3132 nan 0.5000 -0.0016
## 220 0.2924 nan 0.5000 -0.0026
## 240 0.2618 nan 0.5000 -0.0013
## 260 0.2419 nan 0.5000 -0.0046
## 280 0.2260 nan 0.5000 -0.0050
## 300 0.2058 nan 0.5000 -0.0014
## 320 0.1875 nan 0.5000 -0.0020
## 340 0.1660 nan 0.5000 -0.0021
## 360 0.1538 nan 0.5000 -0.0019
## 380 0.1421 nan 0.5000 -0.0020
## 400 0.1302 nan 0.5000 0.0001
## 420 0.1223 nan 0.5000 -0.0036
## 440 0.1129 nan 0.5000 -0.0009
## 460 0.1019 nan 0.5000 -0.0014
## 480 0.0978 nan 0.5000 -0.0006
## 500 0.0920 nan 0.5000 -0.0019
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1394 nan 0.5000 0.0679
## 2 1.0278 nan 0.5000 0.0398
## 3 0.9900 nan 0.5000 0.0056
## 4 0.9689 nan 0.5000 -0.0042
## 5 0.9431 nan 0.5000 0.0040
## 6 0.9331 nan 0.5000 -0.0085
## 7 0.9068 nan 0.5000 0.0011
## 8 0.8909 nan 0.5000 0.0020
## 9 0.8838 nan 0.5000 -0.0109
## 10 0.8743 nan 0.5000 0.0001
## 20 0.7809 nan 0.5000 -0.0042
## 40 0.6763 nan 0.5000 -0.0095
## 60 0.6236 nan 0.5000 -0.0077
## 80 0.5463 nan 0.5000 -0.0096
## 100 0.5093 nan 0.5000 -0.0066
## 120 0.4812 nan 0.5000 -0.0054
## 140 0.4423 nan 0.5000 -0.0069
## 160 0.3984 nan 0.5000 -0.0022
## 180 0.3547 nan 0.5000 -0.0021
## 200 0.3198 nan 0.5000 -0.0033
## 220 0.2877 nan 0.5000 -0.0000
## 240 0.2576 nan 0.5000 0.0001
## 260 0.2391 nan 0.5000 -0.0041
## 280 0.2165 nan 0.5000 -0.0028
## 300 0.2010 nan 0.5000 -0.0005
## 320 0.1884 nan 0.5000 -0.0020
## 340 0.1774 nan 0.5000 -0.0019
## 360 0.1618 nan 0.5000 -0.0012
## 380 0.1508 nan 0.5000 -0.0031
## 400 0.1396 nan 0.5000 -0.0013
## 420 0.1310 nan 0.5000 -0.0011
## 440 0.1235 nan 0.5000 -0.0010
## 460 0.1120 nan 0.5000 -0.0006
## 480 0.1054 nan 0.5000 -0.0048
## 500 0.0961 nan 0.5000 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0830 nan 0.5000 0.0868
## 2 1.0067 nan 0.5000 0.0231
## 3 0.9628 nan 0.5000 0.0072
## 4 0.9328 nan 0.5000 -0.0096
## 5 0.9005 nan 0.5000 -0.0018
## 6 0.8772 nan 0.5000 -0.0038
## 7 0.8523 nan 0.5000 -0.0014
## 8 0.8445 nan 0.5000 -0.0082
## 9 0.8250 nan 0.5000 -0.0067
## 10 0.8223 nan 0.5000 -0.0094
## 20 0.7310 nan 0.5000 -0.0124
## 40 0.6137 nan 0.5000 -0.0135
## 60 inf nan 0.5000 nan
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
## 500 inf nan 0.5000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1104 nan 0.5000 0.0813
## 2 1.0105 nan 0.5000 0.0396
## 3 0.9653 nan 0.5000 0.0094
## 4 0.9375 nan 0.5000 0.0042
## 5 0.9179 nan 0.5000 -0.0121
## 6 0.8992 nan 0.5000 -0.0077
## 7 0.8805 nan 0.5000 -0.0062
## 8 0.8593 nan 0.5000 -0.0091
## 9 0.8471 nan 0.5000 -0.0073
## 10 0.8449 nan 0.5000 -0.0158
## 20 0.7767 nan 0.5000 -0.0384
## 40 598317428663792278808224688444.0000 nan 0.5000 -0.0152
## 60 598317428663792278808224688444.0000 nan 0.5000 -0.0096
## 80 598317428663792278808224688444.0000 nan 0.5000 -0.0172
## 100 598317428663792278808224688444.0000 nan 0.5000 -0.0039
## 120 598317428663792278808224688444.0000 nan 0.5000 0.0000
## 140 598317428663792278808224688444.0000 nan 0.5000 -0.0026
## 160 598317428663792278808224688444.0000 nan 0.5000 -0.0064
## 180 598317428663792278808224688444.0000 nan 0.5000 -0.0051
## 200 598317428663792278808224688444.0000 nan 0.5000 -0.0013
## 220 598317428663792278808224688444.0000 nan 0.5000 -0.0058
## 240 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 -0.0003
## 260 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 0.0001
## 280 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 -0.0052
## 300 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 -0.0007
## 320 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 0.0001
## 340 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 -0.0006
## 360 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 -0.0031
## 380 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 0.0002
## 400 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 0.0001
## 420 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 -0.0025
## 440 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 -0.0000
## 460 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 -0.0006
## 480 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 -0.0034
## 500 37684657820914872976028820888800062420824862680266446046460.0000 nan 0.5000 -0.0039
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0829 nan 0.5000 0.0869
## 2 0.9967 nan 0.5000 0.0387
## 3 0.9470 nan 0.5000 0.0120
## 4 0.9146 nan 0.5000 -0.0070
## 5 0.8973 nan 0.5000 -0.0094
## 6 0.8754 nan 0.5000 -0.0083
## 7 0.8661 nan 0.5000 -0.0123
## 8 0.8478 nan 0.5000 -0.0050
## 9 0.8264 nan 0.5000 -0.0053
## 10 0.8269 nan 0.5000 -0.0179
## 20 0.7646 nan 0.5000 -0.0075
## 40 0.6251 nan 0.5000 -0.0088
## 60 0.4927 nan 0.5000 -0.0041
## 80 0.4018 nan 0.5000 -0.0057
## 100 0.3346 nan 0.5000 -0.0057
## 120 0.2794 nan 0.5000 -0.0022
## 140 0.2261 nan 0.5000 -0.0049
## 160 0.1854 nan 0.5000 -0.0059
## 180 0.1610 nan 0.5000 -0.0033
## 200 0.1316 nan 0.5000 -0.0010
## 220 0.1100 nan 0.5000 -0.0010
## 240 0.0914 nan 0.5000 -0.0006
## 260 0.0814 nan 0.5000 -0.0010
## 280 0.0719 nan 0.5000 -0.0009
## 300 0.0606 nan 0.5000 -0.0009
## 320 0.0538 nan 0.5000 -0.0010
## 340 0.0459 nan 0.5000 -0.0003
## 360 0.0405 nan 0.5000 -0.0006
## 380 0.0366 nan 0.5000 -0.0007
## 400 0.0316 nan 0.5000 -0.0006
## 420 0.0274 nan 0.5000 -0.0003
## 440 0.0244 nan 0.5000 -0.0003
## 460 0.0212 nan 0.5000 -0.0004
## 480 0.0184 nan 0.5000 -0.0001
## 500 0.0166 nan 0.5000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1207 nan 1.0000 0.0872
## 2 1.0557 nan 1.0000 0.0316
## 3 1.0205 nan 1.0000 -0.0017
## 4 0.9848 nan 1.0000 0.0093
## 5 0.9823 nan 1.0000 -0.0170
## 6 0.9689 nan 1.0000 -0.0018
## 7 0.9796 nan 1.0000 -0.0368
## 8 0.9606 nan 1.0000 -0.0157
## 9 0.9654 nan 1.0000 -0.0216
## 10 0.9620 nan 1.0000 -0.0149
## 20 0.9422 nan 1.0000 -0.0126
## 40 1061.9910 nan 1.0000 -0.0095
## 60 1061.9738 nan 1.0000 -0.0006
## 80 1132.0075 nan 1.0000 -0.0030
## 100 1131.9758 nan 1.0000 -0.0034
## 120 1131.7719 nan 1.0000 0.0043
## 140 1131.7721 nan 1.0000 0.0024
## 160 1131.7498 nan 1.0000 -0.0008
## 180 1131.7411 nan 1.0000 -0.0185
## 200 1131.7481 nan 1.0000 -0.0106
## 220 1131.7596 nan 1.0000 -0.0036
## 240 1131.7978 nan 1.0000 0.0005
## 260 1132.5797 nan 1.0000 -0.0034
## 280 1132.5530 nan 1.0000 0.0008
## 300 1132.5443 nan 1.0000 -0.0047
## 320 1132.5287 nan 1.0000 -0.0000
## 340 1132.5292 nan 1.0000 0.0021
## 360 1132.5243 nan 1.0000 0.0004
## 380 1132.5254 nan 1.0000 -0.0018
## 400 1991.6770 nan 1.0000 -0.0232
## 420 1991.6520 nan 1.0000 -0.0018
## 440 1991.6467 nan 1.0000 0.0002
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1289 nan 1.0000 0.0678
## 2 1.0692 nan 1.0000 0.0046
## 3 1.0020 nan 1.0000 0.0221
## 4 0.9637 nan 1.0000 -0.0022
## 5 0.9463 nan 1.0000 0.0001
## 6 0.9390 nan 1.0000 -0.0035
## 7 0.9458 nan 1.0000 -0.0175
## 8 0.9676 nan 1.0000 -0.0336
## 9 0.9568 nan 1.0000 -0.0033
## 10 0.9573 nan 1.0000 -0.0315
## 20 0.9370 nan 1.0000 -0.0102
## 40 0.8657 nan 1.0000 -0.0277
## 60 0.7966 nan 1.0000 -0.0149
## 80 0.7640 nan 1.0000 -0.0105
## 100 0.7898 nan 1.0000 -0.0308
## 120 1.2617 nan 1.0000 -0.3252
## 140 2882499.8476 nan 1.0000 -0.0005
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1331 nan 1.0000 0.0664
## 2 1.0707 nan 1.0000 0.0202
## 3 1.0179 nan 1.0000 0.0292
## 4 0.9980 nan 1.0000 0.0029
## 5 1.0037 nan 1.0000 -0.0156
## 6 0.9980 nan 1.0000 -0.0102
## 7 0.9968 nan 1.0000 -0.0165
## 8 0.9838 nan 1.0000 -0.0049
## 9 0.9921 nan 1.0000 -0.0237
## 10 0.9913 nan 1.0000 -0.0158
## 20 0.9319 nan 1.0000 -0.0036
## 40 3160436.0324 nan 1.0000 -0.0100
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0514 nan 1.0000 0.1096
## 2 0.9985 nan 1.0000 -0.0183
## 3 0.9581 nan 1.0000 0.0094
## 4 0.9412 nan 1.0000 -0.0133
## 5 0.9333 nan 1.0000 -0.0204
## 6 0.9369 nan 1.0000 -0.0255
## 7 0.9344 nan 1.0000 -0.0320
## 8 0.9224 nan 1.0000 -0.0140
## 9 0.9170 nan 1.0000 -0.0129
## 10 0.9195 nan 1.0000 -0.0387
## 20 inf nan 1.0000 -inf
## 40 inf nan 1.0000 -0.0026
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0975 nan 1.0000 0.0956
## 2 1.0195 nan 1.0000 0.0000
## 3 0.9683 nan 1.0000 0.0180
## 4 0.9891 nan 1.0000 -0.0526
## 5 0.9622 nan 1.0000 0.0005
## 6 0.9556 nan 1.0000 -0.0170
## 7 0.9450 nan 1.0000 -0.0161
## 8 0.9240 nan 1.0000 -0.0055
## 9 0.9118 nan 1.0000 -0.0318
## 10 0.8905 nan 1.0000 -0.0033
## 20 4.8194 nan 1.0000 -0.0604
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
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## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0872 nan 1.0000 0.0669
## 2 1.0172 nan 1.0000 0.0239
## 3 0.9791 nan 1.0000 0.0013
## 4 0.9504 nan 1.0000 -0.0001
## 5 0.9353 nan 1.0000 -0.0083
## 6 0.9300 nan 1.0000 -0.0312
## 7 0.9009 nan 1.0000 -0.0145
## 8 0.9220 nan 1.0000 -0.0529
## 9 0.8750 nan 1.0000 0.0065
## 10 0.8598 nan 1.0000 -0.0172
## 20 0.8000 nan 1.0000 -0.0206
## 40 0.7806 nan 1.0000 0.0050
## 60 0.9344 nan 1.0000 -0.0727
## 80 0.9053 nan 1.0000 -0.0236
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0965 nan 1.0000 0.0573
## 2 0.9804 nan 1.0000 0.0392
## 3 0.9676 nan 1.0000 -0.0211
## 4 0.9680 nan 1.0000 -0.0411
## 5 0.9726 nan 1.0000 -0.0412
## 6 0.9586 nan 1.0000 -0.0344
## 7 0.9706 nan 1.0000 -0.0538
## 8 0.9879 nan 1.0000 -0.0662
## 9 0.9451 nan 1.0000 -0.0275
## 10 0.9346 nan 1.0000 -0.0393
## 20 inf nan 1.0000 -0.0645
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 -0.0586
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0614 nan 1.0000 0.0753
## 2 0.9893 nan 1.0000 -0.0002
## 3 0.9612 nan 1.0000 -0.0157
## 4 0.9528 nan 1.0000 -0.0257
## 5 0.9489 nan 1.0000 -0.0367
## 6 0.9428 nan 1.0000 -0.0349
## 7 0.9263 nan 1.0000 -0.0119
## 8 0.9103 nan 1.0000 -0.0278
## 9 1.4521 nan 1.0000 -0.5902
## 10 inf nan 1.0000 -inf
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0180 nan 1.0000 0.1013
## 2 0.9809 nan 1.0000 -0.0314
## 3 0.9734 nan 1.0000 -0.0316
## 4 0.9442 nan 1.0000 -0.0184
## 5 0.9003 nan 1.0000 0.0129
## 6 0.8804 nan 1.0000 -0.0305
## 7 0.8996 nan 1.0000 -0.0368
## 8 0.8972 nan 1.0000 -0.0229
## 9 0.9071 nan 1.0000 -0.0435
## 10 0.9334 nan 1.0000 -0.0640
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
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## 40 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0001
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0001
## 10 1.2898 nan 0.0010 0.0001
## 20 1.2862 nan 0.0010 0.0002
## 40 1.2794 nan 0.0010 0.0001
## 60 1.2728 nan 0.0010 0.0002
## 80 1.2662 nan 0.0010 0.0001
## 100 1.2601 nan 0.0010 0.0001
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## 140 1.2483 nan 0.0010 0.0001
## 160 1.2430 nan 0.0010 0.0001
## 180 1.2377 nan 0.0010 0.0001
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## 240 1.2225 nan 0.0010 0.0001
## 260 1.2176 nan 0.0010 0.0001
## 280 1.2129 nan 0.0010 0.0001
## 300 1.2082 nan 0.0010 0.0001
## 320 1.2036 nan 0.0010 0.0001
## 340 1.1994 nan 0.0010 0.0001
## 360 1.1952 nan 0.0010 0.0001
## 380 1.1912 nan 0.0010 0.0001
## 400 1.1870 nan 0.0010 0.0001
## 420 1.1831 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2927 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0001
## 4 1.2920 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0001
## 6 1.2913 nan 0.0010 0.0002
## 7 1.2910 nan 0.0010 0.0002
## 8 1.2906 nan 0.0010 0.0002
## 9 1.2902 nan 0.0010 0.0002
## 10 1.2899 nan 0.0010 0.0002
## 20 1.2863 nan 0.0010 0.0002
## 40 1.2793 nan 0.0010 0.0002
## 60 1.2728 nan 0.0010 0.0002
## 80 1.2662 nan 0.0010 0.0001
## 100 1.2600 nan 0.0010 0.0001
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## 140 1.2483 nan 0.0010 0.0001
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## 220 1.2270 nan 0.0010 0.0001
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## 260 1.2172 nan 0.0010 0.0001
## 280 1.2125 nan 0.0010 0.0001
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## 320 1.2035 nan 0.0010 0.0001
## 340 1.1991 nan 0.0010 0.0001
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## 380 1.1908 nan 0.0010 0.0001
## 400 1.1868 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0001
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2905 nan 0.0010 0.0002
## 9 1.2902 nan 0.0010 0.0001
## 10 1.2899 nan 0.0010 0.0002
## 20 1.2865 nan 0.0010 0.0002
## 40 1.2795 nan 0.0010 0.0002
## 60 1.2727 nan 0.0010 0.0001
## 80 1.2664 nan 0.0010 0.0001
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## 140 1.2483 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2911 nan 0.0010 0.0002
## 6 1.2906 nan 0.0010 0.0002
## 7 1.2902 nan 0.0010 0.0002
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## 10 1.2888 nan 0.0010 0.0002
## 20 1.2843 nan 0.0010 0.0002
## 40 1.2754 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
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## 10 1.2887 nan 0.0010 0.0002
## 20 1.2842 nan 0.0010 0.0002
## 40 1.2754 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2921 nan 0.0010 0.0002
## 4 1.2916 nan 0.0010 0.0002
## 5 1.2911 nan 0.0010 0.0002
## 6 1.2907 nan 0.0010 0.0002
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## 40 1.2754 nan 0.0010 0.0002
## 60 1.2669 nan 0.0010 0.0002
## 80 1.2585 nan 0.0010 0.0002
## 100 1.2505 nan 0.0010 0.0002
## 120 1.2429 nan 0.0010 0.0001
## 140 1.2355 nan 0.0010 0.0002
## 160 1.2281 nan 0.0010 0.0002
## 180 1.2211 nan 0.0010 0.0002
## 200 1.2144 nan 0.0010 0.0001
## 220 1.2076 nan 0.0010 0.0001
## 240 1.2010 nan 0.0010 0.0001
## 260 1.1948 nan 0.0010 0.0001
## 280 1.1886 nan 0.0010 0.0001
## 300 1.1826 nan 0.0010 0.0001
## 320 1.1768 nan 0.0010 0.0001
## 340 1.1709 nan 0.0010 0.0001
## 360 1.1652 nan 0.0010 0.0001
## 380 1.1597 nan 0.0010 0.0001
## 400 1.1543 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0002
## 5 1.2905 nan 0.0010 0.0003
## 6 1.2900 nan 0.0010 0.0002
## 7 1.2894 nan 0.0010 0.0002
## 8 1.2889 nan 0.0010 0.0002
## 9 1.2883 nan 0.0010 0.0002
## 10 1.2878 nan 0.0010 0.0003
## 20 1.2827 nan 0.0010 0.0002
## 40 1.2726 nan 0.0010 0.0002
## 60 1.2626 nan 0.0010 0.0002
## 80 1.2530 nan 0.0010 0.0002
## 100 1.2437 nan 0.0010 0.0002
## 120 1.2348 nan 0.0010 0.0002
## 140 1.2260 nan 0.0010 0.0002
## 160 1.2177 nan 0.0010 0.0001
## 180 1.2094 nan 0.0010 0.0002
## 200 1.2014 nan 0.0010 0.0001
## 220 1.1937 nan 0.0010 0.0001
## 240 1.1860 nan 0.0010 0.0002
## 260 1.1788 nan 0.0010 0.0002
## 280 1.1718 nan 0.0010 0.0002
## 300 1.1647 nan 0.0010 0.0001
## 320 1.1579 nan 0.0010 0.0001
## 340 1.1514 nan 0.0010 0.0001
## 360 1.1449 nan 0.0010 0.0002
## 380 1.1387 nan 0.0010 0.0001
## 400 1.1324 nan 0.0010 0.0001
## 420 1.1265 nan 0.0010 0.0001
## 440 1.1208 nan 0.0010 0.0001
## 460 1.1151 nan 0.0010 0.0001
## 480 1.1095 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2927 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0002
## 3 1.2916 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0003
## 5 1.2905 nan 0.0010 0.0002
## 6 1.2900 nan 0.0010 0.0003
## 7 1.2895 nan 0.0010 0.0002
## 8 1.2889 nan 0.0010 0.0003
## 9 1.2884 nan 0.0010 0.0003
## 10 1.2879 nan 0.0010 0.0002
## 20 1.2825 nan 0.0010 0.0003
## 40 1.2725 nan 0.0010 0.0002
## 60 1.2626 nan 0.0010 0.0002
## 80 1.2531 nan 0.0010 0.0002
## 100 1.2441 nan 0.0010 0.0002
## 120 1.2352 nan 0.0010 0.0002
## 140 1.2266 nan 0.0010 0.0001
## 160 1.2181 nan 0.0010 0.0002
## 180 1.2099 nan 0.0010 0.0002
## 200 1.2022 nan 0.0010 0.0002
## 220 1.1943 nan 0.0010 0.0002
## 240 1.1866 nan 0.0010 0.0002
## 260 1.1792 nan 0.0010 0.0001
## 280 1.1723 nan 0.0010 0.0002
## 300 1.1652 nan 0.0010 0.0001
## 320 1.1584 nan 0.0010 0.0001
## 340 1.1519 nan 0.0010 0.0001
## 360 1.1457 nan 0.0010 0.0002
## 380 1.1393 nan 0.0010 0.0001
## 400 1.1333 nan 0.0010 0.0001
## 420 1.1274 nan 0.0010 0.0001
## 440 1.1216 nan 0.0010 0.0001
## 460 1.1163 nan 0.0010 0.0001
## 480 1.1108 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0003
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0003
## 8 1.2891 nan 0.0010 0.0002
## 9 1.2885 nan 0.0010 0.0002
## 10 1.2879 nan 0.0010 0.0002
## 20 1.2827 nan 0.0010 0.0002
## 40 1.2723 nan 0.0010 0.0002
## 60 1.2627 nan 0.0010 0.0002
## 80 1.2527 nan 0.0010 0.0002
## 100 1.2437 nan 0.0010 0.0002
## 120 1.2350 nan 0.0010 0.0002
## 140 1.2264 nan 0.0010 0.0001
## 160 1.2180 nan 0.0010 0.0002
## 180 1.2096 nan 0.0010 0.0002
## 200 1.2017 nan 0.0010 0.0001
## 220 1.1939 nan 0.0010 0.0002
## 240 1.1863 nan 0.0010 0.0001
## 260 1.1789 nan 0.0010 0.0001
## 280 1.1718 nan 0.0010 0.0001
## 300 1.1646 nan 0.0010 0.0002
## 320 1.1580 nan 0.0010 0.0001
## 340 1.1513 nan 0.0010 0.0001
## 360 1.1448 nan 0.0010 0.0001
## 380 1.1386 nan 0.0010 0.0001
## 400 1.1327 nan 0.0010 0.0001
## 420 1.1266 nan 0.0010 0.0001
## 440 1.1208 nan 0.0010 0.0001
## 460 1.1152 nan 0.0010 0.0001
## 480 1.1097 nan 0.0010 0.0001
## 500 1.1044 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2592 nan 0.1000 0.0170
## 2 1.2257 nan 0.1000 0.0118
## 3 1.1995 nan 0.1000 0.0095
## 4 1.1788 nan 0.1000 0.0097
## 5 1.1607 nan 0.1000 0.0056
## 6 1.1466 nan 0.1000 0.0063
## 7 1.1292 nan 0.1000 0.0078
## 8 1.1138 nan 0.1000 0.0064
## 9 1.1022 nan 0.1000 0.0047
## 10 1.0895 nan 0.1000 0.0049
## 20 1.0042 nan 0.1000 0.0012
## 40 0.9223 nan 0.1000 0.0010
## 60 0.8827 nan 0.1000 -0.0013
## 80 0.8545 nan 0.1000 -0.0018
## 100 0.8404 nan 0.1000 -0.0007
## 120 0.8275 nan 0.1000 -0.0009
## 140 0.8130 nan 0.1000 -0.0006
## 160 0.8034 nan 0.1000 -0.0003
## 180 0.7935 nan 0.1000 -0.0008
## 200 0.7867 nan 0.1000 -0.0016
## 220 0.7778 nan 0.1000 -0.0011
## 240 0.7730 nan 0.1000 -0.0013
## 260 0.7663 nan 0.1000 -0.0008
## 280 0.7615 nan 0.1000 -0.0007
## 300 0.7568 nan 0.1000 -0.0006
## 320 0.7535 nan 0.1000 -0.0012
## 340 0.7469 nan 0.1000 -0.0008
## 360 0.7420 nan 0.1000 -0.0009
## 380 0.7379 nan 0.1000 -0.0016
## 400 0.7317 nan 0.1000 -0.0010
## 420 0.7277 nan 0.1000 -0.0016
## 440 0.7241 nan 0.1000 -0.0002
## 460 0.7184 nan 0.1000 -0.0007
## 480 0.7155 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2646 nan 0.1000 0.0136
## 2 1.2319 nan 0.1000 0.0143
## 3 1.2022 nan 0.1000 0.0102
## 4 1.1806 nan 0.1000 0.0100
## 5 1.1646 nan 0.1000 0.0084
## 6 1.1490 nan 0.1000 0.0070
## 7 1.1347 nan 0.1000 0.0058
## 8 1.1194 nan 0.1000 0.0054
## 9 1.1091 nan 0.1000 0.0039
## 10 1.0992 nan 0.1000 0.0029
## 20 1.0092 nan 0.1000 0.0013
## 40 0.9237 nan 0.1000 0.0001
## 60 0.8824 nan 0.1000 -0.0007
## 80 0.8566 nan 0.1000 0.0002
## 100 0.8401 nan 0.1000 -0.0007
## 120 0.8251 nan 0.1000 -0.0016
## 140 0.8190 nan 0.1000 -0.0011
## 160 0.8096 nan 0.1000 -0.0025
## 180 0.8008 nan 0.1000 -0.0002
## 200 0.7926 nan 0.1000 -0.0010
## 220 0.7860 nan 0.1000 -0.0014
## 240 0.7792 nan 0.1000 -0.0006
## 260 0.7736 nan 0.1000 -0.0005
## 280 0.7670 nan 0.1000 -0.0006
## 300 0.7623 nan 0.1000 -0.0006
## 320 0.7579 nan 0.1000 -0.0009
## 340 0.7529 nan 0.1000 -0.0012
## 360 0.7486 nan 0.1000 -0.0009
## 380 0.7423 nan 0.1000 -0.0011
## 400 0.7376 nan 0.1000 -0.0004
## 420 0.7323 nan 0.1000 -0.0005
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## 460 0.7226 nan 0.1000 -0.0003
## 480 0.7181 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2628 nan 0.1000 0.0168
## 2 1.2312 nan 0.1000 0.0146
## 3 1.2063 nan 0.1000 0.0118
## 4 1.1844 nan 0.1000 0.0090
## 5 1.1695 nan 0.1000 0.0070
## 6 1.1530 nan 0.1000 0.0072
## 7 1.1371 nan 0.1000 0.0039
## 8 1.1215 nan 0.1000 0.0077
## 9 1.1063 nan 0.1000 0.0039
## 10 1.0952 nan 0.1000 0.0036
## 20 1.0011 nan 0.1000 0.0019
## 40 0.9215 nan 0.1000 -0.0003
## 60 0.8826 nan 0.1000 -0.0009
## 80 0.8588 nan 0.1000 0.0001
## 100 0.8377 nan 0.1000 -0.0010
## 120 0.8241 nan 0.1000 -0.0007
## 140 0.8136 nan 0.1000 -0.0006
## 160 0.8033 nan 0.1000 -0.0008
## 180 0.7982 nan 0.1000 -0.0014
## 200 0.7915 nan 0.1000 -0.0007
## 220 0.7850 nan 0.1000 -0.0005
## 240 0.7765 nan 0.1000 -0.0006
## 260 0.7687 nan 0.1000 -0.0009
## 280 0.7630 nan 0.1000 -0.0013
## 300 0.7567 nan 0.1000 -0.0008
## 320 0.7517 nan 0.1000 -0.0008
## 340 0.7445 nan 0.1000 -0.0009
## 360 0.7393 nan 0.1000 -0.0014
## 380 0.7359 nan 0.1000 -0.0011
## 400 0.7308 nan 0.1000 -0.0008
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## 460 0.7186 nan 0.1000 -0.0015
## 480 0.7141 nan 0.1000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2479 nan 0.1000 0.0235
## 2 1.2137 nan 0.1000 0.0152
## 3 1.1820 nan 0.1000 0.0150
## 4 1.1558 nan 0.1000 0.0126
## 5 1.1292 nan 0.1000 0.0112
## 6 1.1063 nan 0.1000 0.0079
## 7 1.0865 nan 0.1000 0.0069
## 8 1.0688 nan 0.1000 0.0065
## 9 1.0526 nan 0.1000 0.0078
## 10 1.0376 nan 0.1000 0.0047
## 20 0.9410 nan 0.1000 0.0016
## 40 0.8586 nan 0.1000 -0.0010
## 60 0.8106 nan 0.1000 -0.0017
## 80 0.7799 nan 0.1000 -0.0014
## 100 0.7558 nan 0.1000 -0.0007
## 120 0.7309 nan 0.1000 0.0005
## 140 0.7049 nan 0.1000 -0.0009
## 160 0.6823 nan 0.1000 -0.0018
## 180 0.6626 nan 0.1000 -0.0007
## 200 0.6467 nan 0.1000 -0.0006
## 220 0.6277 nan 0.1000 -0.0004
## 240 0.6135 nan 0.1000 -0.0008
## 260 0.5943 nan 0.1000 -0.0012
## 280 0.5809 nan 0.1000 -0.0011
## 300 0.5653 nan 0.1000 -0.0012
## 320 0.5495 nan 0.1000 -0.0010
## 340 0.5330 nan 0.1000 -0.0006
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## 380 0.5080 nan 0.1000 -0.0014
## 400 0.4949 nan 0.1000 -0.0012
## 420 0.4843 nan 0.1000 -0.0003
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## 460 0.4635 nan 0.1000 -0.0011
## 480 0.4563 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2481 nan 0.1000 0.0189
## 2 1.2092 nan 0.1000 0.0181
## 3 1.1765 nan 0.1000 0.0134
## 4 1.1485 nan 0.1000 0.0133
## 5 1.1248 nan 0.1000 0.0076
## 6 1.1025 nan 0.1000 0.0095
## 7 1.0852 nan 0.1000 0.0056
## 8 1.0678 nan 0.1000 0.0075
## 9 1.0571 nan 0.1000 0.0030
## 10 1.0404 nan 0.1000 0.0069
## 20 0.9429 nan 0.1000 0.0019
## 40 0.8558 nan 0.1000 -0.0001
## 60 0.8131 nan 0.1000 0.0001
## 80 0.7805 nan 0.1000 -0.0027
## 100 0.7525 nan 0.1000 -0.0013
## 120 0.7270 nan 0.1000 -0.0005
## 140 0.7025 nan 0.1000 -0.0006
## 160 0.6819 nan 0.1000 -0.0002
## 180 0.6631 nan 0.1000 -0.0018
## 200 0.6426 nan 0.1000 -0.0012
## 220 0.6277 nan 0.1000 -0.0008
## 240 0.6135 nan 0.1000 -0.0012
## 260 0.5992 nan 0.1000 -0.0009
## 280 0.5833 nan 0.1000 -0.0007
## 300 0.5684 nan 0.1000 -0.0007
## 320 0.5549 nan 0.1000 -0.0005
## 340 0.5423 nan 0.1000 -0.0017
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## 380 0.5118 nan 0.1000 -0.0006
## 400 0.5031 nan 0.1000 -0.0006
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## 460 0.4686 nan 0.1000 -0.0007
## 480 0.4556 nan 0.1000 -0.0017
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2533 nan 0.1000 0.0215
## 2 1.2143 nan 0.1000 0.0161
## 3 1.1830 nan 0.1000 0.0152
## 4 1.1571 nan 0.1000 0.0097
## 5 1.1300 nan 0.1000 0.0124
## 6 1.1079 nan 0.1000 0.0057
## 7 1.0862 nan 0.1000 0.0071
## 8 1.0688 nan 0.1000 0.0066
## 9 1.0533 nan 0.1000 0.0047
## 10 1.0384 nan 0.1000 0.0070
## 20 0.9409 nan 0.1000 0.0001
## 40 0.8529 nan 0.1000 -0.0009
## 60 0.8105 nan 0.1000 -0.0011
## 80 0.7784 nan 0.1000 -0.0013
## 100 0.7536 nan 0.1000 -0.0010
## 120 0.7288 nan 0.1000 -0.0017
## 140 0.7076 nan 0.1000 -0.0009
## 160 0.6871 nan 0.1000 -0.0012
## 180 0.6678 nan 0.1000 -0.0009
## 200 0.6515 nan 0.1000 -0.0015
## 220 0.6356 nan 0.1000 -0.0006
## 240 0.6139 nan 0.1000 -0.0007
## 260 0.5984 nan 0.1000 -0.0005
## 280 0.5835 nan 0.1000 -0.0006
## 300 0.5708 nan 0.1000 -0.0008
## 320 0.5576 nan 0.1000 -0.0008
## 340 0.5438 nan 0.1000 -0.0013
## 360 0.5321 nan 0.1000 -0.0014
## 380 0.5216 nan 0.1000 -0.0013
## 400 0.5079 nan 0.1000 -0.0009
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## 440 0.4843 nan 0.1000 -0.0003
## 460 0.4745 nan 0.1000 -0.0010
## 480 0.4623 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2435 nan 0.1000 0.0228
## 2 1.2016 nan 0.1000 0.0188
## 3 1.1634 nan 0.1000 0.0179
## 4 1.1285 nan 0.1000 0.0117
## 5 1.1000 nan 0.1000 0.0067
## 6 1.0762 nan 0.1000 0.0091
## 7 1.0541 nan 0.1000 0.0093
## 8 1.0338 nan 0.1000 0.0073
## 9 1.0146 nan 0.1000 0.0082
## 10 1.0014 nan 0.1000 0.0051
## 20 0.9013 nan 0.1000 0.0001
## 40 0.7978 nan 0.1000 -0.0030
## 60 0.7419 nan 0.1000 -0.0014
## 80 0.6975 nan 0.1000 -0.0009
## 100 0.6622 nan 0.1000 -0.0009
## 120 0.6343 nan 0.1000 -0.0013
## 140 0.5969 nan 0.1000 -0.0011
## 160 0.5737 nan 0.1000 -0.0009
## 180 0.5474 nan 0.1000 -0.0008
## 200 0.5192 nan 0.1000 -0.0017
## 220 0.4975 nan 0.1000 -0.0010
## 240 0.4756 nan 0.1000 -0.0009
## 260 0.4565 nan 0.1000 -0.0020
## 280 0.4399 nan 0.1000 -0.0010
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## 320 0.4036 nan 0.1000 -0.0014
## 340 0.3883 nan 0.1000 -0.0008
## 360 0.3732 nan 0.1000 -0.0007
## 380 0.3572 nan 0.1000 -0.0011
## 400 0.3431 nan 0.1000 -0.0004
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## 460 0.3045 nan 0.1000 -0.0007
## 480 0.2955 nan 0.1000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2412 nan 0.1000 0.0216
## 2 1.1993 nan 0.1000 0.0167
## 3 1.1635 nan 0.1000 0.0141
## 4 1.1311 nan 0.1000 0.0136
## 5 1.1017 nan 0.1000 0.0112
## 6 1.0784 nan 0.1000 0.0093
## 7 1.0559 nan 0.1000 0.0090
## 8 1.0319 nan 0.1000 0.0087
## 9 1.0168 nan 0.1000 0.0041
## 10 1.0007 nan 0.1000 0.0064
## 20 0.9002 nan 0.1000 -0.0008
## 40 0.7985 nan 0.1000 0.0005
## 60 0.7461 nan 0.1000 -0.0024
## 80 0.7026 nan 0.1000 -0.0024
## 100 0.6697 nan 0.1000 -0.0017
## 120 0.6385 nan 0.1000 -0.0001
## 140 0.6049 nan 0.1000 -0.0006
## 160 0.5780 nan 0.1000 -0.0007
## 180 0.5490 nan 0.1000 -0.0010
## 200 0.5249 nan 0.1000 -0.0007
## 220 0.5002 nan 0.1000 -0.0007
## 240 0.4826 nan 0.1000 -0.0008
## 260 0.4663 nan 0.1000 -0.0019
## 280 0.4438 nan 0.1000 -0.0010
## 300 0.4257 nan 0.1000 -0.0012
## 320 0.4061 nan 0.1000 -0.0008
## 340 0.3899 nan 0.1000 -0.0018
## 360 0.3707 nan 0.1000 -0.0003
## 380 0.3593 nan 0.1000 -0.0017
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## 420 0.3347 nan 0.1000 -0.0016
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## 460 0.3083 nan 0.1000 -0.0007
## 480 0.2982 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2522 nan 0.1000 0.0166
## 2 1.2083 nan 0.1000 0.0162
## 3 1.1738 nan 0.1000 0.0159
## 4 1.1416 nan 0.1000 0.0146
## 5 1.1143 nan 0.1000 0.0097
## 6 1.0890 nan 0.1000 0.0090
## 7 1.0675 nan 0.1000 0.0093
## 8 1.0503 nan 0.1000 0.0061
## 9 1.0271 nan 0.1000 0.0088
## 10 1.0062 nan 0.1000 0.0054
## 20 0.8960 nan 0.1000 0.0019
## 40 0.8050 nan 0.1000 -0.0011
## 60 0.7520 nan 0.1000 -0.0018
## 80 0.7085 nan 0.1000 -0.0006
## 100 0.6707 nan 0.1000 -0.0012
## 120 0.6372 nan 0.1000 -0.0008
## 140 0.6031 nan 0.1000 -0.0011
## 160 0.5764 nan 0.1000 -0.0005
## 180 0.5489 nan 0.1000 -0.0006
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## 220 0.5042 nan 0.1000 -0.0007
## 240 0.4861 nan 0.1000 -0.0013
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2437 nan 0.2000 0.0183
## 2 1.1816 nan 0.2000 0.0244
## 3 1.1452 nan 0.2000 0.0163
## 4 1.1159 nan 0.2000 0.0117
## 5 1.0885 nan 0.2000 0.0120
## 6 1.0637 nan 0.2000 0.0121
## 7 1.0468 nan 0.2000 0.0076
## 8 1.0333 nan 0.2000 0.0048
## 9 1.0181 nan 0.2000 0.0037
## 10 1.0046 nan 0.2000 0.0054
## 20 0.9248 nan 0.2000 -0.0038
## 40 0.8596 nan 0.2000 0.0002
## 60 0.8390 nan 0.2000 -0.0030
## 80 0.8112 nan 0.2000 -0.0027
## 100 0.7947 nan 0.2000 -0.0011
## 120 0.7807 nan 0.2000 -0.0033
## 140 0.7718 nan 0.2000 -0.0010
## 160 0.7562 nan 0.2000 -0.0015
## 180 0.7460 nan 0.2000 -0.0019
## 200 0.7339 nan 0.2000 -0.0008
## 220 0.7265 nan 0.2000 -0.0024
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## 280 0.6999 nan 0.2000 -0.0010
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## 320 0.6897 nan 0.2000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2362 nan 0.2000 0.0231
## 2 1.1906 nan 0.2000 0.0156
## 3 1.1527 nan 0.2000 0.0170
## 4 1.1238 nan 0.2000 0.0117
## 5 1.1015 nan 0.2000 0.0082
## 6 1.0820 nan 0.2000 0.0074
## 7 1.0663 nan 0.2000 0.0046
## 8 1.0489 nan 0.2000 0.0076
## 9 1.0389 nan 0.2000 0.0028
## 10 1.0197 nan 0.2000 0.0075
## 20 0.9330 nan 0.2000 -0.0011
## 40 0.8725 nan 0.2000 -0.0013
## 60 0.8302 nan 0.2000 -0.0007
## 80 0.8103 nan 0.2000 -0.0034
## 100 0.7964 nan 0.2000 -0.0018
## 120 0.7836 nan 0.2000 -0.0016
## 140 0.7686 nan 0.2000 -0.0013
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## 180 0.7513 nan 0.2000 -0.0042
## 200 0.7427 nan 0.2000 -0.0023
## 220 0.7357 nan 0.2000 -0.0029
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## 280 0.7148 nan 0.2000 -0.0042
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## 360 0.6829 nan 0.2000 -0.0017
## 380 0.6708 nan 0.2000 -0.0013
## 400 0.6653 nan 0.2000 -0.0009
## 420 0.6595 nan 0.2000 -0.0025
## 440 0.6530 nan 0.2000 -0.0005
## 460 0.6455 nan 0.2000 -0.0004
## 480 0.6441 nan 0.2000 -0.0018
## 500 0.6376 nan 0.2000 -0.0010
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2315 nan 0.2000 0.0266
## 2 1.1838 nan 0.2000 0.0232
## 3 1.1487 nan 0.2000 0.0184
## 4 1.1208 nan 0.2000 0.0117
## 5 1.0963 nan 0.2000 0.0067
## 6 1.0669 nan 0.2000 0.0089
## 7 1.0445 nan 0.2000 0.0074
## 8 1.0310 nan 0.2000 0.0026
## 9 1.0177 nan 0.2000 0.0027
## 10 1.0004 nan 0.2000 0.0068
## 20 0.9255 nan 0.2000 0.0001
## 40 0.8614 nan 0.2000 -0.0008
## 60 0.8267 nan 0.2000 -0.0026
## 80 0.8043 nan 0.2000 -0.0020
## 100 0.7846 nan 0.2000 -0.0021
## 120 0.7756 nan 0.2000 -0.0018
## 140 0.7614 nan 0.2000 -0.0001
## 160 0.7546 nan 0.2000 -0.0019
## 180 0.7435 nan 0.2000 -0.0015
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## 220 0.7211 nan 0.2000 -0.0011
## 240 0.7093 nan 0.2000 -0.0016
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## 280 0.6936 nan 0.2000 -0.0003
## 300 0.6879 nan 0.2000 -0.0004
## 320 0.6833 nan 0.2000 -0.0019
## 340 0.6779 nan 0.2000 -0.0036
## 360 0.6717 nan 0.2000 -0.0020
## 380 0.6638 nan 0.2000 -0.0010
## 400 0.6592 nan 0.2000 -0.0006
## 420 0.6514 nan 0.2000 -0.0014
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## 460 0.6405 nan 0.2000 -0.0017
## 480 0.6335 nan 0.2000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2183 nan 0.2000 0.0311
## 2 1.1611 nan 0.2000 0.0213
## 3 1.1054 nan 0.2000 0.0236
## 4 1.0710 nan 0.2000 0.0116
## 5 1.0435 nan 0.2000 0.0094
## 6 1.0110 nan 0.2000 0.0111
## 7 0.9849 nan 0.2000 0.0068
## 8 0.9708 nan 0.2000 0.0038
## 9 0.9575 nan 0.2000 0.0049
## 10 0.9486 nan 0.2000 -0.0049
## 20 0.8601 nan 0.2000 -0.0001
## 40 0.7809 nan 0.2000 -0.0034
## 60 0.7232 nan 0.2000 -0.0037
## 80 0.6810 nan 0.2000 -0.0017
## 100 0.6443 nan 0.2000 -0.0037
## 120 0.6165 nan 0.2000 -0.0035
## 140 0.5836 nan 0.2000 -0.0030
## 160 0.5600 nan 0.2000 -0.0016
## 180 0.5351 nan 0.2000 -0.0018
## 200 0.5093 nan 0.2000 -0.0016
## 220 0.4871 nan 0.2000 -0.0028
## 240 0.4647 nan 0.2000 -0.0011
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## 280 0.4309 nan 0.2000 -0.0016
## 300 0.4104 nan 0.2000 -0.0037
## 320 0.3926 nan 0.2000 -0.0029
## 340 0.3770 nan 0.2000 -0.0006
## 360 0.3628 nan 0.2000 -0.0022
## 380 0.3462 nan 0.2000 -0.0018
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## 420 0.3155 nan 0.2000 -0.0009
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## 480 0.2803 nan 0.2000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2158 nan 0.2000 0.0348
## 2 1.1459 nan 0.2000 0.0293
## 3 1.1093 nan 0.2000 0.0147
## 4 1.0637 nan 0.2000 0.0196
## 5 1.0272 nan 0.2000 0.0141
## 6 1.0011 nan 0.2000 0.0107
## 7 0.9893 nan 0.2000 0.0011
## 8 0.9694 nan 0.2000 0.0058
## 9 0.9553 nan 0.2000 0.0014
## 10 0.9480 nan 0.2000 -0.0018
## 20 0.8623 nan 0.2000 0.0002
## 40 0.7823 nan 0.2000 -0.0025
## 60 0.7179 nan 0.2000 -0.0015
## 80 0.6835 nan 0.2000 -0.0015
## 100 0.6472 nan 0.2000 -0.0006
## 120 0.6225 nan 0.2000 -0.0022
## 140 0.5996 nan 0.2000 -0.0012
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## 180 0.5514 nan 0.2000 -0.0036
## 200 0.5247 nan 0.2000 -0.0022
## 220 0.5043 nan 0.2000 -0.0011
## 240 0.4820 nan 0.2000 -0.0000
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## 280 0.4502 nan 0.2000 -0.0022
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## 320 0.4173 nan 0.2000 -0.0024
## 340 0.4009 nan 0.2000 -0.0007
## 360 0.3828 nan 0.2000 -0.0018
## 380 0.3673 nan 0.2000 0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2165 nan 0.2000 0.0367
## 2 1.1613 nan 0.2000 0.0178
## 3 1.1182 nan 0.2000 0.0193
## 4 1.0732 nan 0.2000 0.0152
## 5 1.0357 nan 0.2000 0.0100
## 6 1.0128 nan 0.2000 0.0079
## 7 0.9844 nan 0.2000 0.0113
## 8 0.9649 nan 0.2000 0.0064
## 9 0.9511 nan 0.2000 0.0023
## 10 0.9390 nan 0.2000 0.0001
## 20 0.8568 nan 0.2000 -0.0008
## 40 0.7802 nan 0.2000 -0.0020
## 60 0.7325 nan 0.2000 -0.0007
## 80 0.6931 nan 0.2000 -0.0038
## 100 0.6536 nan 0.2000 -0.0029
## 120 0.6266 nan 0.2000 -0.0007
## 140 0.5899 nan 0.2000 -0.0031
## 160 0.5621 nan 0.2000 -0.0025
## 180 0.5359 nan 0.2000 -0.0003
## 200 0.5124 nan 0.2000 -0.0040
## 220 0.4912 nan 0.2000 -0.0022
## 240 0.4693 nan 0.2000 -0.0022
## 260 0.4498 nan 0.2000 -0.0001
## 280 0.4297 nan 0.2000 -0.0023
## 300 0.4109 nan 0.2000 -0.0037
## 320 0.3905 nan 0.2000 -0.0015
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## 380 0.3514 nan 0.2000 -0.0016
## 400 0.3388 nan 0.2000 -0.0013
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## 480 0.2958 nan 0.2000 -0.0026
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1978 nan 0.2000 0.0407
## 2 1.1329 nan 0.2000 0.0258
## 3 1.0870 nan 0.2000 0.0180
## 4 1.0381 nan 0.2000 0.0188
## 5 1.0093 nan 0.2000 0.0027
## 6 0.9865 nan 0.2000 0.0030
## 7 0.9596 nan 0.2000 0.0095
## 8 0.9365 nan 0.2000 0.0057
## 9 0.9175 nan 0.2000 0.0024
## 10 0.9054 nan 0.2000 -0.0027
## 20 0.8100 nan 0.2000 0.0000
## 40 0.7185 nan 0.2000 -0.0025
## 60 0.6423 nan 0.2000 0.0001
## 80 0.5868 nan 0.2000 -0.0037
## 100 0.5245 nan 0.2000 -0.0005
## 120 0.4812 nan 0.2000 -0.0028
## 140 0.4385 nan 0.2000 -0.0023
## 160 0.4001 nan 0.2000 -0.0025
## 180 0.3669 nan 0.2000 -0.0002
## 200 0.3355 nan 0.2000 -0.0015
## 220 0.3085 nan 0.2000 -0.0015
## 240 0.2878 nan 0.2000 -0.0032
## 260 0.2731 nan 0.2000 -0.0010
## 280 0.2546 nan 0.2000 -0.0019
## 300 0.2395 nan 0.2000 -0.0014
## 320 0.2265 nan 0.2000 0.0002
## 340 0.2144 nan 0.2000 -0.0010
## 360 0.2032 nan 0.2000 -0.0011
## 380 0.1913 nan 0.2000 -0.0009
## 400 0.1805 nan 0.2000 -0.0015
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## 480 0.1457 nan 0.2000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2008 nan 0.2000 0.0358
## 2 1.1351 nan 0.2000 0.0300
## 3 1.0872 nan 0.2000 0.0142
## 4 1.0417 nan 0.2000 0.0135
## 5 1.0109 nan 0.2000 0.0099
## 6 0.9925 nan 0.2000 0.0021
## 7 0.9648 nan 0.2000 0.0103
## 8 0.9479 nan 0.2000 0.0043
## 9 0.9332 nan 0.2000 -0.0005
## 10 0.9191 nan 0.2000 0.0016
## 20 0.8154 nan 0.2000 0.0002
## 40 0.6935 nan 0.2000 -0.0012
## 60 0.6311 nan 0.2000 -0.0024
## 80 0.5703 nan 0.2000 -0.0049
## 100 0.5223 nan 0.2000 0.0002
## 120 0.4864 nan 0.2000 -0.0039
## 140 0.4525 nan 0.2000 -0.0021
## 160 0.4199 nan 0.2000 -0.0014
## 180 0.3863 nan 0.2000 -0.0012
## 200 0.3582 nan 0.2000 -0.0010
## 220 0.3302 nan 0.2000 -0.0014
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## 380 0.1904 nan 0.2000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2053 nan 0.2000 0.0318
## 2 1.1315 nan 0.2000 0.0257
## 3 1.0693 nan 0.2000 0.0290
## 4 1.0306 nan 0.2000 0.0102
## 5 0.9964 nan 0.2000 0.0096
## 6 0.9648 nan 0.2000 0.0111
## 7 0.9432 nan 0.2000 0.0060
## 8 0.9241 nan 0.2000 0.0069
## 9 0.9128 nan 0.2000 -0.0005
## 10 0.8958 nan 0.2000 0.0028
## 20 0.8127 nan 0.2000 -0.0030
## 40 0.7147 nan 0.2000 -0.0035
## 60 0.6476 nan 0.2000 -0.0028
## 80 0.5852 nan 0.2000 -0.0005
## 100 0.5313 nan 0.2000 -0.0042
## 120 0.4890 nan 0.2000 -0.0029
## 140 0.4602 nan 0.2000 -0.0020
## 160 0.4230 nan 0.2000 -0.0015
## 180 0.3900 nan 0.2000 -0.0028
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## 220 0.3473 nan 0.2000 -0.0022
## 240 0.3178 nan 0.2000 -0.0033
## 260 0.2978 nan 0.2000 -0.0009
## 280 0.2715 nan 0.2000 -0.0015
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## 340 0.2235 nan 0.2000 0.0000
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## 380 0.1973 nan 0.2000 -0.0010
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## 460 0.1527 nan 0.2000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2062 nan 0.3000 0.0435
## 2 1.1527 nan 0.3000 0.0231
## 3 1.1109 nan 0.3000 0.0172
## 4 1.0745 nan 0.3000 0.0143
## 5 1.0444 nan 0.3000 0.0123
## 6 1.0175 nan 0.3000 0.0118
## 7 1.0050 nan 0.3000 -0.0012
## 8 0.9867 nan 0.3000 0.0038
## 9 0.9776 nan 0.3000 0.0022
## 10 0.9612 nan 0.3000 0.0058
## 20 0.8930 nan 0.3000 -0.0030
## 40 0.8393 nan 0.3000 -0.0030
## 60 0.8142 nan 0.3000 -0.0012
## 80 0.7858 nan 0.3000 0.0001
## 100 0.7706 nan 0.3000 -0.0032
## 120 0.7533 nan 0.3000 -0.0031
## 140 0.7431 nan 0.3000 -0.0055
## 160 0.7264 nan 0.3000 -0.0017
## 180 0.7156 nan 0.3000 -0.0012
## 200 0.7098 nan 0.3000 -0.0007
## 220 0.6995 nan 0.3000 -0.0025
## 240 0.6869 nan 0.3000 -0.0050
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## 280 0.6630 nan 0.3000 -0.0038
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## 380 0.6157 nan 0.3000 -0.0034
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1961 nan 0.3000 0.0425
## 2 1.1333 nan 0.3000 0.0236
## 3 1.0986 nan 0.3000 0.0147
## 4 1.0657 nan 0.3000 0.0154
## 5 1.0341 nan 0.3000 0.0114
## 6 1.0211 nan 0.3000 0.0023
## 7 1.0046 nan 0.3000 0.0045
## 8 0.9794 nan 0.3000 0.0091
## 9 0.9666 nan 0.3000 0.0011
## 10 0.9505 nan 0.3000 0.0066
## 20 0.8719 nan 0.3000 -0.0001
## 40 0.8243 nan 0.3000 -0.0007
## 60 0.8042 nan 0.3000 -0.0021
## 80 0.7811 nan 0.3000 -0.0055
## 100 0.7612 nan 0.3000 -0.0033
## 120 0.7508 nan 0.3000 -0.0027
## 140 0.7344 nan 0.3000 -0.0059
## 160 0.7205 nan 0.3000 -0.0038
## 180 0.7054 nan 0.3000 -0.0063
## 200 0.6914 nan 0.3000 -0.0045
## 220 0.6770 nan 0.3000 -0.0016
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## 320 0.6251 nan 0.3000 -0.0032
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## 380 0.6019 nan 0.3000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2087 nan 0.3000 0.0361
## 2 1.1622 nan 0.3000 0.0195
## 3 1.0986 nan 0.3000 0.0280
## 4 1.0635 nan 0.3000 0.0142
## 5 1.0294 nan 0.3000 0.0133
## 6 1.0112 nan 0.3000 0.0045
## 7 0.9975 nan 0.3000 -0.0034
## 8 0.9810 nan 0.3000 0.0053
## 9 0.9616 nan 0.3000 0.0043
## 10 0.9549 nan 0.3000 -0.0011
## 20 0.8815 nan 0.3000 -0.0027
## 40 0.8408 nan 0.3000 -0.0021
## 60 0.8091 nan 0.3000 -0.0008
## 80 0.7886 nan 0.3000 -0.0012
## 100 0.7747 nan 0.3000 -0.0044
## 120 0.7587 nan 0.3000 -0.0043
## 140 0.7488 nan 0.3000 -0.0063
## 160 0.7415 nan 0.3000 -0.0002
## 180 0.7210 nan 0.3000 -0.0014
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## 220 0.6996 nan 0.3000 -0.0003
## 240 0.6872 nan 0.3000 -0.0036
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## 280 0.6674 nan 0.3000 -0.0030
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## 320 0.6501 nan 0.3000 -0.0039
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1822 nan 0.3000 0.0544
## 2 1.1064 nan 0.3000 0.0340
## 3 1.0535 nan 0.3000 0.0169
## 4 1.0127 nan 0.3000 0.0106
## 5 0.9815 nan 0.3000 0.0102
## 6 0.9562 nan 0.3000 0.0090
## 7 0.9473 nan 0.3000 -0.0018
## 8 0.9288 nan 0.3000 0.0038
## 9 0.9187 nan 0.3000 -0.0056
## 10 0.9015 nan 0.3000 0.0023
## 20 0.8268 nan 0.3000 -0.0027
## 40 0.7413 nan 0.3000 -0.0027
## 60 0.6833 nan 0.3000 -0.0036
## 80 0.6356 nan 0.3000 -0.0034
## 100 0.5925 nan 0.3000 -0.0031
## 120 0.5475 nan 0.3000 -0.0028
## 140 0.5026 nan 0.3000 -0.0061
## 160 0.4797 nan 0.3000 -0.0061
## 180 0.4520 nan 0.3000 -0.0039
## 200 0.4092 nan 0.3000 -0.0029
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## 280 0.3106 nan 0.3000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1776 nan 0.3000 0.0431
## 2 1.1056 nan 0.3000 0.0309
## 3 1.0518 nan 0.3000 0.0204
## 4 1.0122 nan 0.3000 0.0137
## 5 0.9877 nan 0.3000 0.0048
## 6 0.9628 nan 0.3000 0.0024
## 7 0.9434 nan 0.3000 0.0070
## 8 0.9219 nan 0.3000 0.0029
## 9 0.9083 nan 0.3000 0.0026
## 10 0.8974 nan 0.3000 -0.0003
## 20 0.8238 nan 0.3000 -0.0035
## 40 0.7272 nan 0.3000 -0.0031
## 60 0.6668 nan 0.3000 -0.0060
## 80 0.6235 nan 0.3000 -0.0053
## 100 0.5835 nan 0.3000 -0.0077
## 120 0.5380 nan 0.3000 -0.0004
## 140 0.5019 nan 0.3000 -0.0036
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## 180 0.4466 nan 0.3000 -0.0031
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## 240 0.3683 nan 0.3000 -0.0037
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1878 nan 0.3000 0.0384
## 2 1.1245 nan 0.3000 0.0225
## 3 1.0616 nan 0.3000 0.0269
## 4 1.0229 nan 0.3000 0.0190
## 5 0.9828 nan 0.3000 0.0134
## 6 0.9529 nan 0.3000 0.0113
## 7 0.9322 nan 0.3000 0.0060
## 8 0.9206 nan 0.3000 0.0009
## 9 0.9096 nan 0.3000 -0.0028
## 10 0.8922 nan 0.3000 0.0036
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## 40 0.7218 nan 0.3000 -0.0023
## 60 0.6614 nan 0.3000 -0.0064
## 80 0.5994 nan 0.3000 -0.0038
## 100 0.5683 nan 0.3000 -0.0030
## 120 0.5248 nan 0.3000 0.0006
## 140 0.4873 nan 0.3000 -0.0047
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## 180 0.4217 nan 0.3000 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1557 nan 0.3000 0.0547
## 2 1.0569 nan 0.3000 0.0420
## 3 1.0004 nan 0.3000 0.0234
## 4 0.9587 nan 0.3000 0.0103
## 5 0.9416 nan 0.3000 -0.0043
## 6 0.9186 nan 0.3000 -0.0029
## 7 0.8947 nan 0.3000 0.0057
## 8 0.8757 nan 0.3000 -0.0008
## 9 0.8600 nan 0.3000 -0.0001
## 10 0.8522 nan 0.3000 -0.0015
## 20 0.7712 nan 0.3000 -0.0078
## 40 0.6651 nan 0.3000 -0.0029
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## 140 0.3337 nan 0.3000 -0.0029
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1522 nan 0.3000 0.0593
## 2 1.0709 nan 0.3000 0.0330
## 3 1.0236 nan 0.3000 0.0124
## 4 0.9786 nan 0.3000 0.0118
## 5 0.9471 nan 0.3000 0.0122
## 6 0.9242 nan 0.3000 0.0041
## 7 0.9054 nan 0.3000 -0.0012
## 8 0.8822 nan 0.3000 0.0033
## 9 0.8672 nan 0.3000 -0.0015
## 10 0.8514 nan 0.3000 -0.0063
## 20 0.7694 nan 0.3000 -0.0080
## 40 0.6616 nan 0.3000 -0.0071
## 60 0.5787 nan 0.3000 -0.0053
## 80 0.5022 nan 0.3000 -0.0040
## 100 0.4510 nan 0.3000 -0.0083
## 120 0.4080 nan 0.3000 -0.0055
## 140 0.3698 nan 0.3000 -0.0054
## 160 0.3255 nan 0.3000 -0.0039
## 180 0.2948 nan 0.3000 -0.0036
## 200 0.2608 nan 0.3000 -0.0009
## 220 0.2374 nan 0.3000 -0.0035
## 240 0.2157 nan 0.3000 -0.0017
## 260 0.1924 nan 0.3000 -0.0003
## 280 0.1742 nan 0.3000 -0.0003
## 300 0.1583 nan 0.3000 -0.0024
## 320 0.1429 nan 0.3000 -0.0011
## 340 0.1325 nan 0.3000 -0.0002
## 360 0.1204 nan 0.3000 -0.0024
## 380 0.1091 nan 0.3000 -0.0004
## 400 0.1000 nan 0.3000 -0.0008
## 420 0.0935 nan 0.3000 -0.0013
## 440 0.0861 nan 0.3000 -0.0010
## 460 0.0784 nan 0.3000 -0.0005
## 480 0.0725 nan 0.3000 -0.0011
## 500 0.0676 nan 0.3000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1599 nan 0.3000 0.0553
## 2 1.0772 nan 0.3000 0.0392
## 3 1.0190 nan 0.3000 0.0153
## 4 0.9822 nan 0.3000 0.0072
## 5 0.9506 nan 0.3000 0.0039
## 6 0.9227 nan 0.3000 0.0080
## 7 0.9090 nan 0.3000 -0.0051
## 8 0.9051 nan 0.3000 -0.0093
## 9 0.8940 nan 0.3000 -0.0055
## 10 0.8730 nan 0.3000 0.0018
## 20 0.7776 nan 0.3000 -0.0025
## 40 0.6603 nan 0.3000 -0.0030
## 60 0.5770 nan 0.3000 -0.0074
## 80 0.5087 nan 0.3000 -0.0028
## 100 0.4543 nan 0.3000 -0.0022
## 120 0.3977 nan 0.3000 -0.0050
## 140 0.3506 nan 0.3000 -0.0051
## 160 0.3123 nan 0.3000 -0.0025
## 180 0.2813 nan 0.3000 -0.0050
## 200 0.2553 nan 0.3000 -0.0043
## 220 0.2287 nan 0.3000 -0.0022
## 240 0.2076 nan 0.3000 -0.0009
## 260 0.1863 nan 0.3000 -0.0019
## 280 0.1679 nan 0.3000 -0.0001
## 300 0.1537 nan 0.3000 -0.0007
## 320 0.1430 nan 0.3000 -0.0015
## 340 0.1318 nan 0.3000 -0.0014
## 360 0.1228 nan 0.3000 -0.0012
## 380 0.1101 nan 0.3000 -0.0004
## 400 0.1015 nan 0.3000 -0.0006
## 420 0.0955 nan 0.3000 -0.0012
## 440 0.0857 nan 0.3000 -0.0006
## 460 0.0788 nan 0.3000 -0.0011
## 480 0.0734 nan 0.3000 -0.0007
## 500 0.0669 nan 0.3000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1685 nan 0.5000 0.0669
## 2 1.1006 nan 0.5000 0.0305
## 3 1.0405 nan 0.5000 0.0247
## 4 1.0009 nan 0.5000 0.0174
## 5 0.9833 nan 0.5000 0.0060
## 6 0.9563 nan 0.5000 0.0081
## 7 0.9418 nan 0.5000 0.0011
## 8 0.9303 nan 0.5000 -0.0003
## 9 0.9163 nan 0.5000 0.0011
## 10 0.9046 nan 0.5000 -0.0036
## 20 0.8573 nan 0.5000 0.0005
## 40 0.8314 nan 0.5000 -0.0065
## 60 1.1512 nan 0.5000 -0.0031
## 80 1.1361 nan 0.5000 -0.0082
## 100 1.1165 nan 0.5000 -0.0076
## 120 1.0943 nan 0.5000 -0.0003
## 140 1.0778 nan 0.5000 -0.0022
## 160 1.0679 nan 0.5000 -0.0075
## 180 1.0540 nan 0.5000 -0.0034
## 200 1.0527 nan 0.5000 -0.0007
## 220 1.0425 nan 0.5000 0.0000
## 240 1.0272 nan 0.5000 -0.0005
## 260 1.0185 nan 0.5000 -0.0015
## 280 1.0079 nan 0.5000 0.0004
## 300 0.9984 nan 0.5000 -0.0008
## 320 0.9975 nan 0.5000 -0.0001
## 340 0.9922 nan 0.5000 -0.0037
## 360 0.9888 nan 0.5000 0.0001
## 380 0.9807 nan 0.5000 -0.0015
## 400 0.9694 nan 0.5000 -0.0068
## 420 0.9708 nan 0.5000 -0.0026
## 440 0.9715 nan 0.5000 -0.0063
## 460 0.9652 nan 0.5000 -0.0068
## 480 0.9566 nan 0.5000 -0.0047
## 500 0.9453 nan 0.5000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1782 nan 0.5000 0.0705
## 2 1.1086 nan 0.5000 0.0225
## 3 1.0870 nan 0.5000 -0.0057
## 4 1.0340 nan 0.5000 0.0248
## 5 1.0032 nan 0.5000 0.0131
## 6 0.9796 nan 0.5000 0.0054
## 7 0.9620 nan 0.5000 -0.0001
## 8 0.9557 nan 0.5000 -0.0040
## 9 0.9347 nan 0.5000 0.0058
## 10 0.9273 nan 0.5000 0.0023
## 20 0.8569 nan 0.5000 -0.0065
## 40 0.8144 nan 0.5000 0.0017
## 60 0.7916 nan 0.5000 -0.0060
## 80 0.7815 nan 0.5000 -0.0105
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
## 500 inf nan 0.5000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1519 nan 0.5000 0.0594
## 2 1.0901 nan 0.5000 0.0246
## 3 1.0474 nan 0.5000 0.0213
## 4 0.9964 nan 0.5000 0.0217
## 5 0.9753 nan 0.5000 0.0053
## 6 0.9471 nan 0.5000 0.0105
## 7 0.9341 nan 0.5000 -0.0016
## 8 0.9220 nan 0.5000 -0.0002
## 9 0.9123 nan 0.5000 0.0027
## 10 0.9078 nan 0.5000 -0.0033
## 20 0.8466 nan 0.5000 -0.0021
## 40 0.8041 nan 0.5000 -0.0070
## 60 0.7924 nan 0.5000 -0.0047
## 80 0.7575 nan 0.5000 -0.0023
## 100 0.7320 nan 0.5000 -0.0029
## 120 0.7052 nan 0.5000 -0.0090
## 140 0.6923 nan 0.5000 -0.0026
## 160 0.6883 nan 0.5000 -0.0061
## 180 0.6720 nan 0.5000 -0.0059
## 200 0.6643 nan 0.5000 -0.0044
## 220 0.6560 nan 0.5000 -0.0080
## 240 0.6273 nan 0.5000 -0.0094
## 260 0.6120 nan 0.5000 -0.0044
## 280 0.6229 nan 0.5000 -0.0067
## 300 0.6130 nan 0.5000 -0.0022
## 320 0.6029 nan 0.5000 -0.0101
## 340 0.5899 nan 0.5000 -0.0063
## 360 0.5717 nan 0.5000 -0.0018
## 380 0.5679 nan 0.5000 -0.0058
## 400 0.5600 nan 0.5000 -0.0051
## 420 0.5546 nan 0.5000 -0.0015
## 440 0.5400 nan 0.5000 -0.0029
## 460 0.5270 nan 0.5000 -0.0033
## 480 0.5190 nan 0.5000 -0.0063
## 500 0.5060 nan 0.5000 -0.0046
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1164 nan 0.5000 0.0713
## 2 1.0214 nan 0.5000 0.0428
## 3 0.9733 nan 0.5000 0.0222
## 4 0.9387 nan 0.5000 0.0042
## 5 0.9184 nan 0.5000 0.0001
## 6 0.9050 nan 0.5000 -0.0050
## 7 0.8911 nan 0.5000 -0.0040
## 8 0.8718 nan 0.5000 -0.0047
## 9 0.8619 nan 0.5000 0.0020
## 10 0.8547 nan 0.5000 -0.0027
## 20 0.7708 nan 0.5000 -0.0092
## 40 inf nan 0.5000 nan
## 60 inf nan 0.5000 nan
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 -0.0008
## 160 inf nan 0.5000 -0.0001
## 180 inf nan 0.5000 -0.0050
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
## 500 inf nan 0.5000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1338 nan 0.5000 0.0733
## 2 1.0275 nan 0.5000 0.0281
## 3 0.9730 nan 0.5000 0.0213
## 4 0.9450 nan 0.5000 0.0022
## 5 0.9124 nan 0.5000 0.0066
## 6 0.9019 nan 0.5000 -0.0054
## 7 0.8797 nan 0.5000 -0.0150
## 8 0.8649 nan 0.5000 0.0043
## 9 0.8533 nan 0.5000 -0.0056
## 10 0.8371 nan 0.5000 0.0018
## 20 0.7768 nan 0.5000 -0.0069
## 40 0.6832 nan 0.5000 -0.0111
## 60 0.6154 nan 0.5000 -0.0074
## 80 0.5514 nan 0.5000 -0.0037
## 100 0.5073 nan 0.5000 -0.0068
## 120 0.4638 nan 0.5000 -0.0025
## 140 0.4134 nan 0.5000 -0.0059
## 160 0.3872 nan 0.5000 -0.0054
## 180 0.3365 nan 0.5000 -0.0080
## 200 0.3105 nan 0.5000 -0.0023
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
## 500 inf nan 0.5000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1320 nan 0.5000 0.0793
## 2 1.0587 nan 0.5000 0.0281
## 3 1.0091 nan 0.5000 0.0185
## 4 0.9772 nan 0.5000 0.0077
## 5 0.9543 nan 0.5000 -0.0027
## 6 0.9161 nan 0.5000 0.0157
## 7 0.8988 nan 0.5000 0.0010
## 8 0.8862 nan 0.5000 0.0008
## 9 0.8750 nan 0.5000 -0.0046
## 10 0.8484 nan 0.5000 0.0024
## 20 0.7844 nan 0.5000 -0.0064
## 40 0.6894 nan 0.5000 -0.0062
## 60 0.6177 nan 0.5000 -0.0083
## 80 0.5480 nan 0.5000 -0.0048
## 100 0.5180 nan 0.5000 -0.0085
## 120 0.4527 nan 0.5000 -0.0110
## 140 0.4070 nan 0.5000 -0.0086
## 160 0.3783 nan 0.5000 -0.0038
## 180 0.3304 nan 0.5000 -0.0038
## 200 0.3095 nan 0.5000 -0.0048
## 220 0.2848 nan 0.5000 -0.0006
## 240 0.2577 nan 0.5000 -0.0050
## 260 0.2337 nan 0.5000 -0.0009
## 280 0.2157 nan 0.5000 -0.0045
## 300 0.1919 nan 0.5000 -0.0045
## 320 0.1747 nan 0.5000 -0.0016
## 340 0.1545 nan 0.5000 -0.0034
## 360 0.1436 nan 0.5000 -0.0005
## 380 0.1288 nan 0.5000 -0.0003
## 400 0.1179 nan 0.5000 -0.0019
## 420 0.1080 nan 0.5000 -0.0027
## 440 0.0976 nan 0.5000 -0.0001
## 460 0.0902 nan 0.5000 -0.0004
## 480 0.0859 nan 0.5000 -0.0005
## 500 0.0800 nan 0.5000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1081 nan 0.5000 0.0853
## 2 1.0239 nan 0.5000 0.0257
## 3 0.9520 nan 0.5000 0.0251
## 4 0.9409 nan 0.5000 -0.0087
## 5 0.9138 nan 0.5000 -0.0124
## 6 0.8848 nan 0.5000 -0.0042
## 7 0.8730 nan 0.5000 -0.0188
## 8 0.8568 nan 0.5000 -0.0094
## 9 0.8344 nan 0.5000 0.0010
## 10 0.8252 nan 0.5000 -0.0153
## 20 0.7252 nan 0.5000 -0.0183
## 40 0.5937 nan 0.5000 -0.0092
## 60 1.1033 nan 0.5000 -0.0034
## 80 1213.2023 nan 0.5000 -0.0011
## 100 1213.1596 nan 0.5000 -0.0023
## 120 1213.1129 nan 0.5000 -0.0057
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
## 500 inf nan 0.5000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0963 nan 0.5000 0.0911
## 2 0.9914 nan 0.5000 0.0319
## 3 0.9354 nan 0.5000 0.0201
## 4 0.9167 nan 0.5000 -0.0074
## 5 0.8916 nan 0.5000 -0.0055
## 6 0.8810 nan 0.5000 -0.0087
## 7 0.8547 nan 0.5000 0.0011
## 8 0.8448 nan 0.5000 -0.0142
## 9 0.8311 nan 0.5000 -0.0084
## 10 0.8201 nan 0.5000 -0.0130
## 20 0.7064 nan 0.5000 -0.0053
## 40 0.5953 nan 0.5000 -0.0095
## 60 0.4990 nan 0.5000 -0.0071
## 80 0.4028 nan 0.5000 -0.0064
## 100 0.3441 nan 0.5000 -0.0020
## 120 0.2734 nan 0.5000 0.0002
## 140 0.2276 nan 0.5000 -0.0042
## 160 0.1961 nan 0.5000 -0.0042
## 180 0.1616 nan 0.5000 -0.0023
## 200 0.1461 nan 0.5000 -0.0021
## 220 0.1224 nan 0.5000 -0.0025
## 240 0.1040 nan 0.5000 -0.0012
## 260 0.0936 nan 0.5000 -0.0011
## 280 0.0818 nan 0.5000 -0.0007
## 300 0.0718 nan 0.5000 -0.0003
## 320 0.0640 nan 0.5000 -0.0016
## 340 0.0570 nan 0.5000 -0.0003
## 360 0.0490 nan 0.5000 -0.0008
## 380 0.0428 nan 0.5000 -0.0005
## 400 0.0379 nan 0.5000 -0.0005
## 420 0.0341 nan 0.5000 -0.0002
## 440 0.0316 nan 0.5000 -0.0002
## 460 0.0283 nan 0.5000 -0.0002
## 480 0.0259 nan 0.5000 -0.0007
## 500 0.0229 nan 0.5000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1175 nan 0.5000 0.0708
## 2 1.0075 nan 0.5000 0.0456
## 3 0.9499 nan 0.5000 0.0238
## 4 0.9249 nan 0.5000 -0.0015
## 5 0.9051 nan 0.5000 -0.0061
## 6 0.8774 nan 0.5000 0.0010
## 7 0.8619 nan 0.5000 -0.0093
## 8 0.8372 nan 0.5000 0.0017
## 9 0.8235 nan 0.5000 -0.0103
## 10 0.8038 nan 0.5000 -0.0043
## 20 0.7243 nan 0.5000 -0.0150
## 40 0.5821 nan 0.5000 -0.0099
## 60 0.4815 nan 0.5000 -0.0117
## 80 0.4027 nan 0.5000 -0.0024
## 100 0.3356 nan 0.5000 -0.0098
## 120 0.2729 nan 0.5000 -0.0047
## 140 0.2339 nan 0.5000 -0.0024
## 160 0.1929 nan 0.5000 -0.0020
## 180 0.1646 nan 0.5000 -0.0019
## 200 0.1472 nan 0.5000 -0.0023
## 220 0.1264 nan 0.5000 -0.0018
## 240 0.1068 nan 0.5000 -0.0022
## 260 0.0944 nan 0.5000 -0.0004
## 280 0.0814 nan 0.5000 -0.0010
## 300 0.0715 nan 0.5000 -0.0011
## 320 0.0628 nan 0.5000 -0.0009
## 340 0.0543 nan 0.5000 -0.0003
## 360 0.0467 nan 0.5000 -0.0006
## 380 0.0412 nan 0.5000 -0.0010
## 400 0.0358 nan 0.5000 -0.0001
## 420 0.0312 nan 0.5000 -0.0001
## 440 0.0271 nan 0.5000 -0.0003
## 460 0.0238 nan 0.5000 -0.0002
## 480 0.0218 nan 0.5000 -0.0003
## 500 0.0194 nan 0.5000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1316 nan 1.0000 0.0739
## 2 1.0630 nan 1.0000 0.0232
## 3 1.0307 nan 1.0000 -0.0110
## 4 1.0178 nan 1.0000 -0.0152
## 5 1.0002 nan 1.0000 -0.0061
## 6 0.9898 nan 1.0000 -0.0016
## 7 0.9749 nan 1.0000 -0.0040
## 8 0.9715 nan 1.0000 -0.0158
## 9 0.9630 nan 1.0000 -0.0071
## 10 0.9713 nan 1.0000 -0.0291
## 20 0.8712 nan 1.0000 -0.0044
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 -0.0038
## 220 inf nan 1.0000 -0.0148
## 240 inf nan 1.0000 -0.0013
## 260 inf nan 1.0000 -0.0114
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1195 nan 1.0000 0.0741
## 2 1.0637 nan 1.0000 0.0137
## 3 1.0555 nan 1.0000 -0.0182
## 4 1.0221 nan 1.0000 0.0070
## 5 0.9722 nan 1.0000 0.0008
## 6 0.9429 nan 1.0000 0.0059
## 7 0.9346 nan 1.0000 -0.0094
## 8 0.9069 nan 1.0000 0.0075
## 9 0.9002 nan 1.0000 -0.0051
## 10 0.8912 nan 1.0000 -0.0032
## 20 0.8758 nan 1.0000 -0.0190
## 40 0.9016 nan 1.0000 -0.0850
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 0.0026
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1206 nan 1.0000 0.0785
## 2 1.0993 nan 1.0000 -0.0335
## 3 1.0241 nan 1.0000 0.0180
## 4 1.0130 nan 1.0000 -0.0061
## 5 0.9537 nan 1.0000 0.0273
## 6 0.9547 nan 1.0000 -0.0144
## 7 0.9633 nan 1.0000 -0.0386
## 8 0.9373 nan 1.0000 -0.0052
## 9 0.9249 nan 1.0000 -0.0156
## 10 0.9140 nan 1.0000 -0.0118
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0699 nan 1.0000 0.1288
## 2 0.9547 nan 1.0000 0.0499
## 3 0.9245 nan 1.0000 0.0004
## 4 0.9413 nan 1.0000 -0.0391
## 5 0.9196 nan 1.0000 -0.0118
## 6 0.9203 nan 1.0000 -0.0372
## 7 0.9024 nan 1.0000 -0.0047
## 8 0.9392 nan 1.0000 -0.0610
## 9 0.9181 nan 1.0000 -0.0221
## 10 0.9002 nan 1.0000 0.0002
## 20 0.8762 nan 1.0000 -0.0511
## 40 657504192971.6241 nan 1.0000 -0.0335
## 60 657504192971.5759 nan 1.0000 -0.0659
## 80 657504192971.4921 nan 1.0000 -0.0011
## 100 657504192971.4919 nan 1.0000 -0.0010
## 120 657504192971.4971 nan 1.0000 -0.0035
## 140 657504192971.5203 nan 1.0000 0.0011
## 160 657504192972.7058 nan 1.0000 -0.0070
## 180 657504192972.6395 nan 1.0000 0.0016
## 200 657504192972.6331 nan 1.0000 0.0000
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1109 nan 1.0000 0.0662
## 2 0.9968 nan 1.0000 0.0408
## 3 0.9737 nan 1.0000 -0.0171
## 4 0.9739 nan 1.0000 -0.0193
## 5 0.9617 nan 1.0000 -0.0130
## 6 0.9544 nan 1.0000 -0.0342
## 7 0.9162 nan 1.0000 -0.0033
## 8 0.9071 nan 1.0000 -0.0214
## 9 0.9509 nan 1.0000 -0.0731
## 10 0.9745 nan 1.0000 -0.0607
## 20 2.7832 nan 1.0000 -0.0825
## 40 inf nan 1.0000 -inf
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0802 nan 1.0000 0.0778
## 2 1.0093 nan 1.0000 0.0155
## 3 0.9905 nan 1.0000 -0.0171
## 4 0.9506 nan 1.0000 0.0092
## 5 0.9284 nan 1.0000 -0.0047
## 6 0.9047 nan 1.0000 -0.0121
## 7 0.9060 nan 1.0000 -0.0247
## 8 0.9020 nan 1.0000 -0.0246
## 9 0.9058 nan 1.0000 -0.0368
## 10 0.9147 nan 1.0000 -0.0439
## 20 0.8679 nan 1.0000 -0.0438
## 40 248.2200 nan 1.0000 0.0030
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0430 nan 1.0000 0.1236
## 2 0.9401 nan 1.0000 0.0260
## 3 0.9286 nan 1.0000 -0.0389
## 4 0.9065 nan 1.0000 -0.0166
## 5 0.9973 nan 1.0000 -0.1371
## 6 inf nan 1.0000 -0.0247
## 7 inf nan 1.0000 nan
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## 160 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 180 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 200 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 220 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 240 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 260 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 280 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 300 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 320 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 340 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 360 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 380 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 400 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 420 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 440 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 460 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 480 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
## 500 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000 nan 1.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0018 nan 1.0000 0.1338
## 2 0.9210 nan 1.0000 0.0240
## 3 0.9158 nan 1.0000 -0.0243
## 4 0.9154 nan 1.0000 -0.0316
## 5 0.9760 nan 1.0000 -0.0927
## 6 0.9909 nan 1.0000 -0.0794
## 7 0.9679 nan 1.0000 -0.0122
## 8 0.9646 nan 1.0000 -0.0351
## 9 0.9504 nan 1.0000 -0.0269
## 10 0.9761 nan 1.0000 -0.0647
## 20 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0392 nan 1.0000 0.1204
## 2 0.9654 nan 1.0000 0.0141
## 3 0.9321 nan 1.0000 -0.0129
## 4 0.9048 nan 1.0000 -0.0114
## 5 0.9204 nan 1.0000 -0.0438
## 6 0.8950 nan 1.0000 -0.0116
## 7 0.8905 nan 1.0000 -0.0313
## 8 0.8570 nan 1.0000 -0.0287
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## 20 0.7495 nan 1.0000 -0.0130
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## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
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## 3 1.2935 nan 0.0010 0.0001
## 4 1.2931 nan 0.0010 0.0002
## 5 1.2927 nan 0.0010 0.0002
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## 7 1.2919 nan 0.0010 0.0002
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## 10 1.2907 nan 0.0010 0.0002
## 20 1.2871 nan 0.0010 0.0002
## 40 1.2801 nan 0.0010 0.0002
## 60 1.2735 nan 0.0010 0.0002
## 80 1.2669 nan 0.0010 0.0001
## 100 1.2606 nan 0.0010 0.0001
## 120 1.2544 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
## 2 1.2938 nan 0.0010 0.0001
## 3 1.2934 nan 0.0010 0.0002
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## 5 1.2926 nan 0.0010 0.0002
## 6 1.2922 nan 0.0010 0.0002
## 7 1.2918 nan 0.0010 0.0002
## 8 1.2915 nan 0.0010 0.0002
## 9 1.2911 nan 0.0010 0.0002
## 10 1.2907 nan 0.0010 0.0001
## 20 1.2871 nan 0.0010 0.0002
## 40 1.2798 nan 0.0010 0.0002
## 60 1.2733 nan 0.0010 0.0001
## 80 1.2667 nan 0.0010 0.0002
## 100 1.2602 nan 0.0010 0.0001
## 120 1.2540 nan 0.0010 0.0001
## 140 1.2479 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2942 nan 0.0010 0.0002
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## 3 1.2934 nan 0.0010 0.0002
## 4 1.2930 nan 0.0010 0.0001
## 5 1.2927 nan 0.0010 0.0002
## 6 1.2923 nan 0.0010 0.0001
## 7 1.2919 nan 0.0010 0.0002
## 8 1.2915 nan 0.0010 0.0002
## 9 1.2911 nan 0.0010 0.0002
## 10 1.2908 nan 0.0010 0.0001
## 20 1.2874 nan 0.0010 0.0002
## 40 1.2805 nan 0.0010 0.0002
## 60 1.2736 nan 0.0010 0.0001
## 80 1.2671 nan 0.0010 0.0002
## 100 1.2607 nan 0.0010 0.0002
## 120 1.2543 nan 0.0010 0.0001
## 140 1.2485 nan 0.0010 0.0001
## 160 1.2428 nan 0.0010 0.0001
## 180 1.2371 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
## 2 1.2936 nan 0.0010 0.0002
## 3 1.2931 nan 0.0010 0.0002
## 4 1.2926 nan 0.0010 0.0002
## 5 1.2921 nan 0.0010 0.0002
## 6 1.2916 nan 0.0010 0.0002
## 7 1.2912 nan 0.0010 0.0002
## 8 1.2906 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2896 nan 0.0010 0.0002
## 20 1.2850 nan 0.0010 0.0002
## 40 1.2759 nan 0.0010 0.0002
## 60 1.2670 nan 0.0010 0.0002
## 80 1.2586 nan 0.0010 0.0002
## 100 1.2502 nan 0.0010 0.0002
## 120 1.2421 nan 0.0010 0.0002
## 140 1.2344 nan 0.0010 0.0002
## 160 1.2269 nan 0.0010 0.0002
## 180 1.2196 nan 0.0010 0.0002
## 200 1.2128 nan 0.0010 0.0001
## 220 1.2061 nan 0.0010 0.0001
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## 380 1.1571 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
## 2 1.2935 nan 0.0010 0.0002
## 3 1.2930 nan 0.0010 0.0002
## 4 1.2926 nan 0.0010 0.0002
## 5 1.2922 nan 0.0010 0.0002
## 6 1.2916 nan 0.0010 0.0002
## 7 1.2912 nan 0.0010 0.0002
## 8 1.2907 nan 0.0010 0.0002
## 9 1.2902 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2852 nan 0.0010 0.0002
## 40 1.2761 nan 0.0010 0.0002
## 60 1.2672 nan 0.0010 0.0002
## 80 1.2586 nan 0.0010 0.0002
## 100 1.2503 nan 0.0010 0.0002
## 120 1.2421 nan 0.0010 0.0001
## 140 1.2342 nan 0.0010 0.0002
## 160 1.2268 nan 0.0010 0.0001
## 180 1.2194 nan 0.0010 0.0002
## 200 1.2122 nan 0.0010 0.0002
## 220 1.2056 nan 0.0010 0.0001
## 240 1.1990 nan 0.0010 0.0001
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## 380 1.1566 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
## 2 1.2936 nan 0.0010 0.0002
## 3 1.2930 nan 0.0010 0.0002
## 4 1.2926 nan 0.0010 0.0002
## 5 1.2921 nan 0.0010 0.0002
## 6 1.2916 nan 0.0010 0.0002
## 7 1.2912 nan 0.0010 0.0002
## 8 1.2907 nan 0.0010 0.0002
## 9 1.2902 nan 0.0010 0.0002
## 10 1.2898 nan 0.0010 0.0002
## 20 1.2851 nan 0.0010 0.0002
## 40 1.2759 nan 0.0010 0.0002
## 60 1.2672 nan 0.0010 0.0002
## 80 1.2588 nan 0.0010 0.0002
## 100 1.2505 nan 0.0010 0.0002
## 120 1.2427 nan 0.0010 0.0001
## 140 1.2350 nan 0.0010 0.0002
## 160 1.2277 nan 0.0010 0.0002
## 180 1.2204 nan 0.0010 0.0002
## 200 1.2136 nan 0.0010 0.0002
## 220 1.2066 nan 0.0010 0.0001
## 240 1.2002 nan 0.0010 0.0001
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## 280 1.1872 nan 0.0010 0.0001
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## 380 1.1577 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
## 2 1.2935 nan 0.0010 0.0002
## 3 1.2929 nan 0.0010 0.0002
## 4 1.2925 nan 0.0010 0.0002
## 5 1.2919 nan 0.0010 0.0003
## 6 1.2914 nan 0.0010 0.0002
## 7 1.2909 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0003
## 9 1.2899 nan 0.0010 0.0002
## 10 1.2893 nan 0.0010 0.0003
## 20 1.2841 nan 0.0010 0.0002
## 40 1.2736 nan 0.0010 0.0002
## 60 1.2635 nan 0.0010 0.0002
## 80 1.2538 nan 0.0010 0.0002
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## 140 1.2257 nan 0.0010 0.0002
## 160 1.2171 nan 0.0010 0.0002
## 180 1.2087 nan 0.0010 0.0002
## 200 1.2006 nan 0.0010 0.0002
## 220 1.1930 nan 0.0010 0.0001
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## 380 1.1373 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0003
## 2 1.2935 nan 0.0010 0.0002
## 3 1.2929 nan 0.0010 0.0003
## 4 1.2924 nan 0.0010 0.0002
## 5 1.2918 nan 0.0010 0.0002
## 6 1.2913 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2902 nan 0.0010 0.0003
## 9 1.2896 nan 0.0010 0.0003
## 10 1.2891 nan 0.0010 0.0003
## 20 1.2835 nan 0.0010 0.0003
## 40 1.2729 nan 0.0010 0.0002
## 60 1.2627 nan 0.0010 0.0002
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## 140 1.2256 nan 0.0010 0.0002
## 160 1.2172 nan 0.0010 0.0002
## 180 1.2086 nan 0.0010 0.0002
## 200 1.2006 nan 0.0010 0.0002
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## 280 1.1703 nan 0.0010 0.0001
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## 320 1.1561 nan 0.0010 0.0001
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## 380 1.1366 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0003
## 2 1.2935 nan 0.0010 0.0002
## 3 1.2929 nan 0.0010 0.0002
## 4 1.2923 nan 0.0010 0.0003
## 5 1.2917 nan 0.0010 0.0003
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2901 nan 0.0010 0.0002
## 9 1.2895 nan 0.0010 0.0003
## 10 1.2889 nan 0.0010 0.0002
## 20 1.2835 nan 0.0010 0.0003
## 40 1.2732 nan 0.0010 0.0002
## 60 1.2630 nan 0.0010 0.0002
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## 140 1.2260 nan 0.0010 0.0002
## 160 1.2173 nan 0.0010 0.0002
## 180 1.2090 nan 0.0010 0.0002
## 200 1.2008 nan 0.0010 0.0002
## 220 1.1929 nan 0.0010 0.0002
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## 280 1.1708 nan 0.0010 0.0001
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## 380 1.1373 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2567 nan 0.1000 0.0148
## 2 1.2296 nan 0.1000 0.0105
## 3 1.2035 nan 0.1000 0.0116
## 4 1.1839 nan 0.1000 0.0105
## 5 1.1671 nan 0.1000 0.0052
## 6 1.1491 nan 0.1000 0.0061
## 7 1.1320 nan 0.1000 0.0062
## 8 1.1171 nan 0.1000 0.0060
## 9 1.1014 nan 0.1000 0.0055
## 10 1.0897 nan 0.1000 0.0052
## 20 1.0023 nan 0.1000 0.0018
## 40 0.9192 nan 0.1000 0.0006
## 60 0.8817 nan 0.1000 -0.0005
## 80 0.8557 nan 0.1000 0.0000
## 100 0.8372 nan 0.1000 0.0001
## 120 0.8259 nan 0.1000 -0.0007
## 140 0.8143 nan 0.1000 -0.0011
## 160 0.8052 nan 0.1000 -0.0011
## 180 0.7968 nan 0.1000 -0.0009
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## 220 0.7788 nan 0.1000 -0.0008
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## 280 0.7588 nan 0.1000 -0.0013
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## 320 0.7489 nan 0.1000 -0.0008
## 340 0.7415 nan 0.1000 -0.0016
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## 380 0.7313 nan 0.1000 -0.0003
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## 480 0.7093 nan 0.1000 -0.0019
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2569 nan 0.1000 0.0140
## 2 1.2282 nan 0.1000 0.0110
## 3 1.2038 nan 0.1000 0.0123
## 4 1.1800 nan 0.1000 0.0108
## 5 1.1638 nan 0.1000 0.0076
## 6 1.1490 nan 0.1000 0.0059
## 7 1.1315 nan 0.1000 0.0065
## 8 1.1209 nan 0.1000 0.0036
## 9 1.1061 nan 0.1000 0.0055
## 10 1.0927 nan 0.1000 0.0069
## 20 1.0033 nan 0.1000 0.0022
## 40 0.9220 nan 0.1000 -0.0004
## 60 0.8835 nan 0.1000 -0.0009
## 80 0.8570 nan 0.1000 -0.0004
## 100 0.8425 nan 0.1000 -0.0008
## 120 0.8264 nan 0.1000 -0.0002
## 140 0.8172 nan 0.1000 -0.0016
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## 180 0.8031 nan 0.1000 -0.0012
## 200 0.7940 nan 0.1000 -0.0017
## 220 0.7846 nan 0.1000 -0.0003
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## 280 0.7633 nan 0.1000 -0.0003
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## 320 0.7500 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2576 nan 0.1000 0.0183
## 2 1.2219 nan 0.1000 0.0144
## 3 1.2042 nan 0.1000 0.0080
## 4 1.1818 nan 0.1000 0.0108
## 5 1.1634 nan 0.1000 0.0075
## 6 1.1486 nan 0.1000 0.0070
## 7 1.1328 nan 0.1000 0.0060
## 8 1.1204 nan 0.1000 0.0061
## 9 1.1037 nan 0.1000 0.0061
## 10 1.0906 nan 0.1000 0.0064
## 20 1.0038 nan 0.1000 0.0018
## 40 0.9225 nan 0.1000 -0.0006
## 60 0.8785 nan 0.1000 -0.0001
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## 120 0.8213 nan 0.1000 -0.0011
## 140 0.8107 nan 0.1000 -0.0007
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## 180 0.7913 nan 0.1000 -0.0014
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## 240 0.7646 nan 0.1000 -0.0002
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## 280 0.7500 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2453 nan 0.1000 0.0213
## 2 1.2101 nan 0.1000 0.0173
## 3 1.1750 nan 0.1000 0.0141
## 4 1.1484 nan 0.1000 0.0105
## 5 1.1252 nan 0.1000 0.0107
## 6 1.1004 nan 0.1000 0.0103
## 7 1.0799 nan 0.1000 0.0102
## 8 1.0640 nan 0.1000 0.0037
## 9 1.0457 nan 0.1000 0.0071
## 10 1.0370 nan 0.1000 0.0006
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## 40 0.8532 nan 0.1000 -0.0002
## 60 0.8043 nan 0.1000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2544 nan 0.1000 0.0195
## 2 1.2170 nan 0.1000 0.0175
## 3 1.1833 nan 0.1000 0.0136
## 4 1.1496 nan 0.1000 0.0136
## 5 1.1223 nan 0.1000 0.0118
## 6 1.0990 nan 0.1000 0.0104
## 7 1.0760 nan 0.1000 0.0094
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## 60 0.8023 nan 0.1000 -0.0023
## 80 0.7635 nan 0.1000 0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2488 nan 0.1000 0.0213
## 2 1.2125 nan 0.1000 0.0161
## 3 1.1784 nan 0.1000 0.0125
## 4 1.1521 nan 0.1000 0.0128
## 5 1.1227 nan 0.1000 0.0114
## 6 1.1018 nan 0.1000 0.0089
## 7 1.0844 nan 0.1000 0.0055
## 8 1.0650 nan 0.1000 0.0072
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## 10 1.0285 nan 0.1000 0.0068
## 20 0.9372 nan 0.1000 0.0009
## 40 0.8509 nan 0.1000 -0.0016
## 60 0.8079 nan 0.1000 -0.0011
## 80 0.7706 nan 0.1000 -0.0010
## 100 0.7422 nan 0.1000 -0.0019
## 120 0.7169 nan 0.1000 -0.0018
## 140 0.6903 nan 0.1000 -0.0005
## 160 0.6656 nan 0.1000 -0.0009
## 180 0.6496 nan 0.1000 -0.0015
## 200 0.6311 nan 0.1000 -0.0013
## 220 0.6093 nan 0.1000 -0.0006
## 240 0.5942 nan 0.1000 -0.0020
## 260 0.5781 nan 0.1000 -0.0010
## 280 0.5652 nan 0.1000 -0.0015
## 300 0.5518 nan 0.1000 -0.0009
## 320 0.5364 nan 0.1000 -0.0010
## 340 0.5227 nan 0.1000 -0.0007
## 360 0.5080 nan 0.1000 -0.0017
## 380 0.4963 nan 0.1000 -0.0007
## 400 0.4869 nan 0.1000 -0.0006
## 420 0.4792 nan 0.1000 -0.0009
## 440 0.4687 nan 0.1000 -0.0010
## 460 0.4576 nan 0.1000 -0.0010
## 480 0.4441 nan 0.1000 -0.0005
## 500 0.4332 nan 0.1000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2408 nan 0.1000 0.0200
## 2 1.1950 nan 0.1000 0.0201
## 3 1.1563 nan 0.1000 0.0165
## 4 1.1237 nan 0.1000 0.0112
## 5 1.0964 nan 0.1000 0.0115
## 6 1.0719 nan 0.1000 0.0109
## 7 1.0510 nan 0.1000 0.0105
## 8 1.0323 nan 0.1000 0.0082
## 9 1.0147 nan 0.1000 0.0041
## 10 0.9956 nan 0.1000 0.0053
## 20 0.8902 nan 0.1000 -0.0001
## 40 0.7922 nan 0.1000 0.0003
## 60 0.7412 nan 0.1000 -0.0010
## 80 0.6933 nan 0.1000 -0.0010
## 100 0.6495 nan 0.1000 -0.0021
## 120 0.6153 nan 0.1000 -0.0007
## 140 0.5862 nan 0.1000 -0.0010
## 160 0.5566 nan 0.1000 -0.0001
## 180 0.5321 nan 0.1000 -0.0008
## 200 0.5095 nan 0.1000 -0.0005
## 220 0.4910 nan 0.1000 -0.0018
## 240 0.4705 nan 0.1000 -0.0012
## 260 0.4494 nan 0.1000 -0.0006
## 280 0.4297 nan 0.1000 -0.0004
## 300 0.4177 nan 0.1000 -0.0004
## 320 0.4019 nan 0.1000 -0.0010
## 340 0.3848 nan 0.1000 -0.0006
## 360 0.3702 nan 0.1000 -0.0009
## 380 0.3550 nan 0.1000 -0.0014
## 400 0.3430 nan 0.1000 -0.0019
## 420 0.3336 nan 0.1000 -0.0010
## 440 0.3225 nan 0.1000 0.0000
## 460 0.3128 nan 0.1000 -0.0005
## 480 0.3021 nan 0.1000 -0.0005
## 500 0.2908 nan 0.1000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2400 nan 0.1000 0.0249
## 2 1.2022 nan 0.1000 0.0137
## 3 1.1621 nan 0.1000 0.0148
## 4 1.1245 nan 0.1000 0.0146
## 5 1.0968 nan 0.1000 0.0112
## 6 1.0749 nan 0.1000 0.0087
## 7 1.0562 nan 0.1000 0.0080
## 8 1.0357 nan 0.1000 0.0084
## 9 1.0191 nan 0.1000 0.0054
## 10 0.9964 nan 0.1000 0.0072
## 20 0.8960 nan 0.1000 -0.0001
## 40 0.8033 nan 0.1000 -0.0022
## 60 0.7462 nan 0.1000 -0.0005
## 80 0.6966 nan 0.1000 -0.0004
## 100 0.6574 nan 0.1000 -0.0011
## 120 0.6205 nan 0.1000 -0.0002
## 140 0.5862 nan 0.1000 -0.0004
## 160 0.5550 nan 0.1000 -0.0008
## 180 0.5241 nan 0.1000 -0.0008
## 200 0.5004 nan 0.1000 -0.0011
## 220 0.4798 nan 0.1000 -0.0022
## 240 0.4589 nan 0.1000 -0.0015
## 260 0.4389 nan 0.1000 -0.0005
## 280 0.4234 nan 0.1000 -0.0009
## 300 0.4046 nan 0.1000 -0.0008
## 320 0.3878 nan 0.1000 -0.0009
## 340 0.3743 nan 0.1000 -0.0008
## 360 0.3606 nan 0.1000 -0.0010
## 380 0.3453 nan 0.1000 -0.0008
## 400 0.3306 nan 0.1000 -0.0009
## 420 0.3187 nan 0.1000 -0.0010
## 440 0.3071 nan 0.1000 -0.0005
## 460 0.2943 nan 0.1000 -0.0010
## 480 0.2827 nan 0.1000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2455 nan 0.1000 0.0243
## 2 1.2056 nan 0.1000 0.0144
## 3 1.1664 nan 0.1000 0.0172
## 4 1.1351 nan 0.1000 0.0139
## 5 1.1040 nan 0.1000 0.0130
## 6 1.0787 nan 0.1000 0.0128
## 7 1.0577 nan 0.1000 0.0071
## 8 1.0342 nan 0.1000 0.0080
## 9 1.0182 nan 0.1000 0.0050
## 10 1.0040 nan 0.1000 0.0044
## 20 0.8968 nan 0.1000 0.0012
## 40 0.7932 nan 0.1000 -0.0009
## 60 0.7385 nan 0.1000 -0.0008
## 80 0.6945 nan 0.1000 -0.0013
## 100 0.6604 nan 0.1000 -0.0010
## 120 0.6297 nan 0.1000 -0.0010
## 140 0.5972 nan 0.1000 -0.0010
## 160 0.5670 nan 0.1000 -0.0014
## 180 0.5459 nan 0.1000 -0.0020
## 200 0.5144 nan 0.1000 -0.0012
## 220 0.4943 nan 0.1000 -0.0002
## 240 0.4717 nan 0.1000 -0.0003
## 260 0.4486 nan 0.1000 -0.0007
## 280 0.4320 nan 0.1000 -0.0019
## 300 0.4109 nan 0.1000 -0.0007
## 320 0.3918 nan 0.1000 -0.0011
## 340 0.3794 nan 0.1000 -0.0008
## 360 0.3643 nan 0.1000 -0.0010
## 380 0.3512 nan 0.1000 -0.0006
## 400 0.3370 nan 0.1000 -0.0014
## 420 0.3236 nan 0.1000 -0.0007
## 440 0.3117 nan 0.1000 -0.0006
## 460 0.2995 nan 0.1000 0.0001
## 480 0.2880 nan 0.1000 -0.0011
## 500 0.2751 nan 0.1000 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2268 nan 0.2000 0.0292
## 2 1.1887 nan 0.2000 0.0195
## 3 1.1450 nan 0.2000 0.0150
## 4 1.1144 nan 0.2000 0.0114
## 5 1.0929 nan 0.2000 0.0061
## 6 1.0655 nan 0.2000 0.0091
## 7 1.0520 nan 0.2000 0.0020
## 8 1.0311 nan 0.2000 0.0099
## 9 1.0145 nan 0.2000 0.0064
## 10 0.9990 nan 0.2000 0.0054
## 20 0.9212 nan 0.2000 -0.0008
## 40 0.8607 nan 0.2000 -0.0012
## 60 0.8286 nan 0.2000 -0.0020
## 80 0.8047 nan 0.2000 -0.0026
## 100 0.7896 nan 0.2000 -0.0045
## 120 0.7722 nan 0.2000 -0.0011
## 140 0.7584 nan 0.2000 -0.0023
## 160 0.7429 nan 0.2000 -0.0016
## 180 0.7302 nan 0.2000 -0.0007
## 200 0.7222 nan 0.2000 -0.0027
## 220 0.7166 nan 0.2000 -0.0035
## 240 0.7072 nan 0.2000 -0.0030
## 260 0.6928 nan 0.2000 -0.0009
## 280 0.6857 nan 0.2000 -0.0022
## 300 0.6758 nan 0.2000 -0.0016
## 320 0.6672 nan 0.2000 -0.0020
## 340 0.6600 nan 0.2000 -0.0031
## 360 0.6528 nan 0.2000 -0.0009
## 380 0.6486 nan 0.2000 -0.0009
## 400 0.6422 nan 0.2000 -0.0018
## 420 0.6397 nan 0.2000 -0.0006
## 440 0.6349 nan 0.2000 -0.0010
## 460 0.6307 nan 0.2000 -0.0011
## 480 0.6244 nan 0.2000 -0.0001
## 500 0.6182 nan 0.2000 -0.0013
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2250 nan 0.2000 0.0309
## 2 1.1873 nan 0.2000 0.0199
## 3 1.1461 nan 0.2000 0.0150
## 4 1.1154 nan 0.2000 0.0110
## 5 1.0874 nan 0.2000 0.0103
## 6 1.0664 nan 0.2000 0.0070
## 7 1.0521 nan 0.2000 0.0041
## 8 1.0300 nan 0.2000 0.0087
## 9 1.0140 nan 0.2000 0.0073
## 10 1.0060 nan 0.2000 0.0006
## 20 0.9148 nan 0.2000 0.0005
## 40 0.8636 nan 0.2000 -0.0027
## 60 0.8231 nan 0.2000 -0.0000
## 80 0.7998 nan 0.2000 -0.0020
## 100 0.7851 nan 0.2000 -0.0020
## 120 0.7717 nan 0.2000 -0.0036
## 140 0.7559 nan 0.2000 -0.0015
## 160 0.7479 nan 0.2000 -0.0036
## 180 0.7341 nan 0.2000 -0.0015
## 200 0.7258 nan 0.2000 -0.0025
## 220 0.7138 nan 0.2000 -0.0019
## 240 0.7036 nan 0.2000 -0.0012
## 260 0.6936 nan 0.2000 -0.0018
## 280 0.6850 nan 0.2000 -0.0025
## 300 0.6770 nan 0.2000 -0.0008
## 320 0.6703 nan 0.2000 -0.0010
## 340 0.6654 nan 0.2000 -0.0027
## 360 0.6592 nan 0.2000 -0.0008
## 380 0.6486 nan 0.2000 -0.0010
## 400 0.6444 nan 0.2000 -0.0012
## 420 0.6376 nan 0.2000 -0.0007
## 440 0.6355 nan 0.2000 -0.0024
## 460 0.6286 nan 0.2000 -0.0011
## 480 0.6224 nan 0.2000 -0.0015
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2325 nan 0.2000 0.0298
## 2 1.1902 nan 0.2000 0.0197
## 3 1.1517 nan 0.2000 0.0150
## 4 1.1192 nan 0.2000 0.0110
## 5 1.0991 nan 0.2000 0.0073
## 6 1.0755 nan 0.2000 0.0078
## 7 1.0523 nan 0.2000 0.0116
## 8 1.0348 nan 0.2000 0.0031
## 9 1.0195 nan 0.2000 0.0074
## 10 1.0016 nan 0.2000 0.0058
## 20 0.9199 nan 0.2000 -0.0016
## 40 0.8653 nan 0.2000 -0.0027
## 60 0.8387 nan 0.2000 -0.0026
## 80 0.8141 nan 0.2000 -0.0022
## 100 0.7979 nan 0.2000 -0.0014
## 120 0.7795 nan 0.2000 -0.0028
## 140 0.7684 nan 0.2000 -0.0025
## 160 0.7558 nan 0.2000 -0.0030
## 180 0.7485 nan 0.2000 -0.0022
## 200 0.7363 nan 0.2000 -0.0018
## 220 0.7232 nan 0.2000 -0.0013
## 240 0.7141 nan 0.2000 -0.0016
## 260 0.7047 nan 0.2000 -0.0029
## 280 0.6970 nan 0.2000 -0.0010
## 300 0.6919 nan 0.2000 -0.0016
## 320 0.6881 nan 0.2000 -0.0040
## 340 0.6794 nan 0.2000 -0.0018
## 360 0.6713 nan 0.2000 -0.0005
## 380 0.6664 nan 0.2000 -0.0009
## 400 0.6560 nan 0.2000 -0.0017
## 420 0.6498 nan 0.2000 -0.0016
## 440 0.6433 nan 0.2000 -0.0032
## 460 0.6380 nan 0.2000 -0.0007
## 480 0.6312 nan 0.2000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2115 nan 0.2000 0.0301
## 2 1.1418 nan 0.2000 0.0277
## 3 1.0928 nan 0.2000 0.0233
## 4 1.0576 nan 0.2000 0.0135
## 5 1.0275 nan 0.2000 0.0130
## 6 1.0091 nan 0.2000 0.0067
## 7 0.9861 nan 0.2000 0.0075
## 8 0.9730 nan 0.2000 0.0034
## 9 0.9609 nan 0.2000 0.0028
## 10 0.9527 nan 0.2000 -0.0021
## 20 0.8576 nan 0.2000 -0.0034
## 40 0.7552 nan 0.2000 -0.0039
## 60 0.7074 nan 0.2000 -0.0026
## 80 0.6658 nan 0.2000 -0.0016
## 100 0.6326 nan 0.2000 -0.0006
## 120 0.6031 nan 0.2000 -0.0009
## 140 0.5798 nan 0.2000 -0.0038
## 160 0.5477 nan 0.2000 -0.0028
## 180 0.5136 nan 0.2000 -0.0016
## 200 0.4848 nan 0.2000 -0.0019
## 220 0.4584 nan 0.2000 -0.0028
## 240 0.4385 nan 0.2000 -0.0007
## 260 0.4164 nan 0.2000 -0.0014
## 280 0.3997 nan 0.2000 -0.0012
## 300 0.3790 nan 0.2000 -0.0026
## 320 0.3647 nan 0.2000 -0.0017
## 340 0.3463 nan 0.2000 0.0000
## 360 0.3308 nan 0.2000 -0.0015
## 380 0.3160 nan 0.2000 -0.0010
## 400 0.3046 nan 0.2000 -0.0020
## 420 0.2948 nan 0.2000 -0.0007
## 440 0.2827 nan 0.2000 -0.0007
## 460 0.2683 nan 0.2000 -0.0015
## 480 0.2597 nan 0.2000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2092 nan 0.2000 0.0345
## 2 1.1468 nan 0.2000 0.0287
## 3 1.1004 nan 0.2000 0.0176
## 4 1.0654 nan 0.2000 0.0140
## 5 1.0381 nan 0.2000 0.0115
## 6 1.0136 nan 0.2000 0.0060
## 7 0.9814 nan 0.2000 0.0109
## 8 0.9606 nan 0.2000 0.0072
## 9 0.9466 nan 0.2000 0.0039
## 10 0.9391 nan 0.2000 -0.0003
## 20 0.8476 nan 0.2000 -0.0012
## 40 0.7753 nan 0.2000 -0.0006
## 60 0.7134 nan 0.2000 -0.0025
## 80 0.6770 nan 0.2000 -0.0013
## 100 0.6419 nan 0.2000 -0.0045
## 120 0.6102 nan 0.2000 -0.0030
## 140 0.5809 nan 0.2000 -0.0028
## 160 0.5582 nan 0.2000 -0.0017
## 180 0.5269 nan 0.2000 -0.0028
## 200 0.5019 nan 0.2000 -0.0011
## 220 0.4775 nan 0.2000 -0.0017
## 240 0.4587 nan 0.2000 -0.0017
## 260 0.4379 nan 0.2000 -0.0017
## 280 0.4174 nan 0.2000 -0.0022
## 300 0.4029 nan 0.2000 -0.0010
## 320 0.3879 nan 0.2000 -0.0017
## 340 0.3714 nan 0.2000 -0.0016
## 360 0.3529 nan 0.2000 -0.0017
## 380 0.3399 nan 0.2000 -0.0008
## 400 0.3255 nan 0.2000 -0.0009
## 420 0.3152 nan 0.2000 -0.0017
## 440 0.3061 nan 0.2000 -0.0010
## 460 0.2983 nan 0.2000 -0.0021
## 480 0.2866 nan 0.2000 -0.0016
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2144 nan 0.2000 0.0365
## 2 1.1580 nan 0.2000 0.0250
## 3 1.1135 nan 0.2000 0.0127
## 4 1.0757 nan 0.2000 0.0163
## 5 1.0407 nan 0.2000 0.0138
## 6 1.0117 nan 0.2000 0.0105
## 7 0.9923 nan 0.2000 0.0062
## 8 0.9702 nan 0.2000 0.0070
## 9 0.9564 nan 0.2000 0.0047
## 10 0.9472 nan 0.2000 -0.0004
## 20 0.8591 nan 0.2000 -0.0013
## 40 0.8011 nan 0.2000 -0.0034
## 60 0.7571 nan 0.2000 -0.0061
## 80 0.7070 nan 0.2000 -0.0057
## 100 0.6597 nan 0.2000 -0.0029
## 120 0.6231 nan 0.2000 -0.0002
## 140 0.5962 nan 0.2000 -0.0038
## 160 0.5599 nan 0.2000 -0.0008
## 180 0.5331 nan 0.2000 -0.0023
## 200 0.5113 nan 0.2000 -0.0020
## 220 0.4969 nan 0.2000 -0.0036
## 240 0.4759 nan 0.2000 -0.0016
## 260 0.4524 nan 0.2000 -0.0020
## 280 0.4372 nan 0.2000 -0.0022
## 300 0.4149 nan 0.2000 -0.0014
## 320 0.3994 nan 0.2000 -0.0011
## 340 0.3860 nan 0.2000 -0.0037
## 360 0.3754 nan 0.2000 -0.0015
## 380 0.3597 nan 0.2000 -0.0025
## 400 0.3466 nan 0.2000 -0.0019
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## 440 0.3186 nan 0.2000 -0.0017
## 460 0.3061 nan 0.2000 -0.0009
## 480 0.2910 nan 0.2000 -0.0006
## 500 0.2804 nan 0.2000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1971 nan 0.2000 0.0402
## 2 1.1222 nan 0.2000 0.0313
## 3 1.0753 nan 0.2000 0.0203
## 4 1.0324 nan 0.2000 0.0188
## 5 0.9983 nan 0.2000 0.0079
## 6 0.9662 nan 0.2000 0.0096
## 7 0.9426 nan 0.2000 0.0074
## 8 0.9244 nan 0.2000 0.0025
## 9 0.9066 nan 0.2000 0.0032
## 10 0.8924 nan 0.2000 0.0013
## 20 0.8138 nan 0.2000 -0.0025
## 40 0.7203 nan 0.2000 -0.0049
## 60 0.6482 nan 0.2000 -0.0049
## 80 0.5782 nan 0.2000 -0.0017
## 100 0.5305 nan 0.2000 -0.0030
## 120 0.4884 nan 0.2000 -0.0027
## 140 0.4574 nan 0.2000 -0.0131
## 160 0.4033 nan 0.2000 -0.0014
## 180 0.3723 nan 0.2000 -0.0022
## 200 0.3452 nan 0.2000 -0.0021
## 220 0.3131 nan 0.2000 -0.0009
## 240 0.2888 nan 0.2000 -0.0004
## 260 0.2690 nan 0.2000 -0.0012
## 280 0.2532 nan 0.2000 -0.0007
## 300 0.2347 nan 0.2000 -0.0017
## 320 0.2164 nan 0.2000 -0.0001
## 340 0.2031 nan 0.2000 -0.0015
## 360 0.1915 nan 0.2000 -0.0013
## 380 0.1817 nan 0.2000 -0.0007
## 400 0.1705 nan 0.2000 -0.0004
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## 460 0.1405 nan 0.2000 -0.0003
## 480 0.1323 nan 0.2000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2111 nan 0.2000 0.0366
## 2 1.1384 nan 0.2000 0.0333
## 3 1.0835 nan 0.2000 0.0200
## 4 1.0390 nan 0.2000 0.0161
## 5 1.0025 nan 0.2000 0.0117
## 6 0.9668 nan 0.2000 0.0138
## 7 0.9451 nan 0.2000 0.0081
## 8 0.9249 nan 0.2000 0.0038
## 9 0.9050 nan 0.2000 0.0047
## 10 0.8899 nan 0.2000 0.0014
## 20 0.8020 nan 0.2000 -0.0017
## 40 0.6965 nan 0.2000 -0.0012
## 60 0.6327 nan 0.2000 -0.0038
## 80 0.5763 nan 0.2000 -0.0014
## 100 0.5161 nan 0.2000 -0.0022
## 120 0.4693 nan 0.2000 -0.0002
## 140 0.4274 nan 0.2000 -0.0013
## 160 0.3954 nan 0.2000 -0.0022
## 180 0.3680 nan 0.2000 -0.0006
## 200 0.3382 nan 0.2000 -0.0013
## 220 0.3098 nan 0.2000 0.0001
## 240 0.2861 nan 0.2000 -0.0016
## 260 0.2710 nan 0.2000 -0.0013
## 280 0.2498 nan 0.2000 -0.0014
## 300 0.2293 nan 0.2000 -0.0017
## 320 0.2125 nan 0.2000 -0.0014
## 340 0.1976 nan 0.2000 -0.0015
## 360 0.1830 nan 0.2000 -0.0007
## 380 0.1705 nan 0.2000 -0.0011
## 400 0.1574 nan 0.2000 -0.0007
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## 460 0.1275 nan 0.2000 -0.0002
## 480 0.1210 nan 0.2000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2042 nan 0.2000 0.0436
## 2 1.1328 nan 0.2000 0.0323
## 3 1.0745 nan 0.2000 0.0234
## 4 1.0329 nan 0.2000 0.0212
## 5 0.9929 nan 0.2000 0.0127
## 6 0.9632 nan 0.2000 0.0099
## 7 0.9373 nan 0.2000 0.0092
## 8 0.9135 nan 0.2000 0.0085
## 9 0.8963 nan 0.2000 0.0021
## 10 0.8829 nan 0.2000 0.0020
## 20 0.7956 nan 0.2000 0.0001
## 40 0.7064 nan 0.2000 -0.0024
## 60 0.6341 nan 0.2000 0.0001
## 80 0.5752 nan 0.2000 -0.0025
## 100 0.5193 nan 0.2000 -0.0010
## 120 0.4742 nan 0.2000 -0.0013
## 140 0.4360 nan 0.2000 -0.0034
## 160 0.4006 nan 0.2000 -0.0017
## 180 0.3692 nan 0.2000 -0.0013
## 200 0.3494 nan 0.2000 -0.0038
## 220 0.3189 nan 0.2000 -0.0012
## 240 0.2943 nan 0.2000 -0.0016
## 260 0.2750 nan 0.2000 -0.0029
## 280 0.2534 nan 0.2000 -0.0017
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## 320 0.2191 nan 0.2000 -0.0009
## 340 0.2033 nan 0.2000 -0.0017
## 360 0.1887 nan 0.2000 -0.0012
## 380 0.1756 nan 0.2000 -0.0007
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## 460 0.1354 nan 0.2000 -0.0005
## 480 0.1278 nan 0.2000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2031 nan 0.3000 0.0360
## 2 1.1533 nan 0.3000 0.0244
## 3 1.1077 nan 0.3000 0.0202
## 4 1.0786 nan 0.3000 0.0092
## 5 1.0487 nan 0.3000 0.0089
## 6 1.0130 nan 0.3000 0.0126
## 7 1.0023 nan 0.3000 0.0009
## 8 0.9824 nan 0.3000 0.0060
## 9 0.9585 nan 0.3000 0.0071
## 10 0.9493 nan 0.3000 -0.0004
## 20 0.8863 nan 0.3000 -0.0016
## 40 0.8268 nan 0.3000 -0.0013
## 60 0.7955 nan 0.3000 -0.0033
## 80 0.7788 nan 0.3000 -0.0046
## 100 0.7603 nan 0.3000 -0.0013
## 120 0.7504 nan 0.3000 -0.0025
## 140 0.7327 nan 0.3000 -0.0043
## 160 0.7190 nan 0.3000 -0.0027
## 180 0.7054 nan 0.3000 -0.0019
## 200 0.6904 nan 0.3000 -0.0008
## 220 0.6774 nan 0.3000 -0.0020
## 240 0.6670 nan 0.3000 -0.0028
## 260 0.6559 nan 0.3000 -0.0029
## 280 0.6439 nan 0.3000 -0.0027
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## 320 0.6295 nan 0.3000 -0.0014
## 340 0.6197 nan 0.3000 -0.0031
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## 380 0.6041 nan 0.3000 -0.0045
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## 440 0.5893 nan 0.3000 -0.0013
## 460 0.5835 nan 0.3000 -0.0016
## 480 0.5788 nan 0.3000 -0.0032
## 500 0.5712 nan 0.3000 -0.0042
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1957 nan 0.3000 0.0455
## 2 1.1451 nan 0.3000 0.0183
## 3 1.1053 nan 0.3000 0.0179
## 4 1.0699 nan 0.3000 0.0162
## 5 1.0408 nan 0.3000 0.0107
## 6 1.0187 nan 0.3000 0.0068
## 7 0.9984 nan 0.3000 0.0081
## 8 0.9812 nan 0.3000 0.0066
## 9 0.9681 nan 0.3000 0.0000
## 10 0.9527 nan 0.3000 0.0032
## 20 0.8972 nan 0.3000 -0.0009
## 40 0.8314 nan 0.3000 -0.0009
## 60 0.8013 nan 0.3000 -0.0040
## 80 0.7778 nan 0.3000 -0.0040
## 100 0.7613 nan 0.3000 -0.0026
## 120 0.7504 nan 0.3000 -0.0032
## 140 0.7315 nan 0.3000 -0.0010
## 160 0.7210 nan 0.3000 -0.0018
## 180 0.7159 nan 0.3000 -0.0084
## 200 0.7036 nan 0.3000 -0.0021
## 220 0.6978 nan 0.3000 -0.0023
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## 260 0.6751 nan 0.3000 -0.0062
## 280 0.6612 nan 0.3000 -0.0018
## 300 0.6533 nan 0.3000 -0.0032
## 320 0.6403 nan 0.3000 -0.0030
## 340 0.6330 nan 0.3000 -0.0023
## 360 0.6200 nan 0.3000 -0.0007
## 380 0.6161 nan 0.3000 -0.0015
## 400 0.6125 nan 0.3000 -0.0025
## 420 0.6020 nan 0.3000 -0.0036
## 440 0.5940 nan 0.3000 -0.0016
## 460 0.5801 nan 0.3000 -0.0018
## 480 0.5711 nan 0.3000 -0.0025
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1966 nan 0.3000 0.0419
## 2 1.1388 nan 0.3000 0.0282
## 3 1.1034 nan 0.3000 0.0102
## 4 1.0645 nan 0.3000 0.0141
## 5 1.0261 nan 0.3000 0.0137
## 6 0.9993 nan 0.3000 0.0086
## 7 0.9788 nan 0.3000 0.0025
## 8 0.9614 nan 0.3000 0.0068
## 9 0.9498 nan 0.3000 0.0019
## 10 0.9404 nan 0.3000 0.0005
## 20 0.8858 nan 0.3000 -0.0013
## 40 0.8277 nan 0.3000 -0.0017
## 60 0.8032 nan 0.3000 -0.0014
## 80 0.7803 nan 0.3000 -0.0027
## 100 0.7693 nan 0.3000 -0.0031
## 120 0.7489 nan 0.3000 -0.0021
## 140 0.7302 nan 0.3000 0.0000
## 160 0.7222 nan 0.3000 -0.0056
## 180 0.7061 nan 0.3000 -0.0043
## 200 0.6886 nan 0.3000 -0.0011
## 220 0.6822 nan 0.3000 -0.0008
## 240 0.6789 nan 0.3000 -0.0025
## 260 0.6689 nan 0.3000 -0.0023
## 280 0.6639 nan 0.3000 -0.0042
## 300 0.6445 nan 0.3000 -0.0017
## 320 0.6414 nan 0.3000 -0.0088
## 340 0.6298 nan 0.3000 -0.0015
## 360 0.6210 nan 0.3000 -0.0033
## 380 0.6144 nan 0.3000 -0.0016
## 400 0.6064 nan 0.3000 -0.0035
## 420 0.5961 nan 0.3000 -0.0013
## 440 0.5878 nan 0.3000 -0.0016
## 460 0.5807 nan 0.3000 -0.0024
## 480 0.5749 nan 0.3000 -0.0030
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1728 nan 0.3000 0.0530
## 2 1.0990 nan 0.3000 0.0275
## 3 1.0480 nan 0.3000 0.0192
## 4 1.0044 nan 0.3000 0.0160
## 5 0.9700 nan 0.3000 0.0122
## 6 0.9435 nan 0.3000 0.0106
## 7 0.9304 nan 0.3000 0.0020
## 8 0.9195 nan 0.3000 -0.0031
## 9 0.9061 nan 0.3000 0.0015
## 10 0.8937 nan 0.3000 -0.0006
## 20 0.8362 nan 0.3000 -0.0048
## 40 0.7392 nan 0.3000 -0.0031
## 60 0.6675 nan 0.3000 -0.0032
## 80 0.6075 nan 0.3000 -0.0024
## 100 0.5483 nan 0.3000 -0.0012
## 120 0.5084 nan 0.3000 -0.0039
## 140 0.4749 nan 0.3000 -0.0024
## 160 0.4421 nan 0.3000 -0.0027
## 180 0.4213 nan 0.3000 -0.0016
## 200 0.3962 nan 0.3000 -0.0045
## 220 0.3697 nan 0.3000 -0.0007
## 240 0.3487 nan 0.3000 -0.0024
## 260 0.3255 nan 0.3000 -0.0027
## 280 0.3092 nan 0.3000 -0.0031
## 300 0.2870 nan 0.3000 -0.0002
## 320 0.2679 nan 0.3000 -0.0026
## 340 0.2503 nan 0.3000 -0.0012
## 360 0.2322 nan 0.3000 -0.0008
## 380 0.2160 nan 0.3000 -0.0016
## 400 0.2080 nan 0.3000 -0.0004
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## 480 0.1694 nan 0.3000 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1792 nan 0.3000 0.0576
## 2 1.0946 nan 0.3000 0.0360
## 3 1.0454 nan 0.3000 0.0176
## 4 1.0080 nan 0.3000 0.0123
## 5 0.9743 nan 0.3000 0.0066
## 6 0.9485 nan 0.3000 0.0075
## 7 0.9286 nan 0.3000 0.0073
## 8 0.9139 nan 0.3000 -0.0001
## 9 0.8961 nan 0.3000 0.0027
## 10 0.8881 nan 0.3000 -0.0046
## 20 0.8128 nan 0.3000 -0.0028
## 40 0.7345 nan 0.3000 -0.0032
## 60 0.6617 nan 0.3000 -0.0014
## 80 0.6245 nan 0.3000 -0.0029
## 100 0.5834 nan 0.3000 -0.0031
## 120 0.5350 nan 0.3000 -0.0029
## 140 0.5005 nan 0.3000 -0.0031
## 160 0.4675 nan 0.3000 -0.0020
## 180 0.4436 nan 0.3000 -0.0057
## 200 0.4118 nan 0.3000 -0.0008
## 220 0.3878 nan 0.3000 -0.0008
## 240 0.3647 nan 0.3000 -0.0038
## 260 0.3413 nan 0.3000 -0.0012
## 280 0.3217 nan 0.3000 -0.0020
## 300 0.3049 nan 0.3000 -0.0026
## 320 0.2811 nan 0.3000 -0.0024
## 340 0.2674 nan 0.3000 -0.0016
## 360 0.2560 nan 0.3000 -0.0014
## 380 0.2395 nan 0.3000 -0.0017
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## 440 0.2019 nan 0.3000 -0.0008
## 460 0.1942 nan 0.3000 -0.0003
## 480 0.1803 nan 0.3000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1613 nan 0.3000 0.0512
## 2 1.0905 nan 0.3000 0.0202
## 3 1.0293 nan 0.3000 0.0202
## 4 0.9827 nan 0.3000 0.0204
## 5 0.9608 nan 0.3000 0.0049
## 6 0.9395 nan 0.3000 0.0023
## 7 0.9229 nan 0.3000 0.0037
## 8 0.9121 nan 0.3000 -0.0034
## 9 0.9029 nan 0.3000 -0.0015
## 10 0.8882 nan 0.3000 0.0053
## 20 0.8221 nan 0.3000 -0.0003
## 40 0.7462 nan 0.3000 -0.0049
## 60 0.6758 nan 0.3000 -0.0032
## 80 0.6186 nan 0.3000 -0.0018
## 100 0.5705 nan 0.3000 -0.0044
## 120 0.5314 nan 0.3000 -0.0001
## 140 0.5012 nan 0.3000 -0.0024
## 160 0.4748 nan 0.3000 -0.0044
## 180 0.4479 nan 0.3000 -0.0030
## 200 0.4213 nan 0.3000 -0.0008
## 220 0.3965 nan 0.3000 -0.0008
## 240 0.3765 nan 0.3000 -0.0024
## 260 0.3519 nan 0.3000 -0.0028
## 280 0.3299 nan 0.3000 -0.0056
## 300 0.3123 nan 0.3000 -0.0023
## 320 0.2970 nan 0.3000 -0.0024
## 340 0.2821 nan 0.3000 -0.0031
## 360 0.2750 nan 0.3000 -0.0011
## 380 0.2570 nan 0.3000 -0.0010
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## 460 0.2026 nan 0.3000 -0.0014
## 480 0.1921 nan 0.3000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1604 nan 0.3000 0.0617
## 2 1.0731 nan 0.3000 0.0284
## 3 1.0231 nan 0.3000 0.0154
## 4 0.9839 nan 0.3000 0.0091
## 5 0.9446 nan 0.3000 0.0103
## 6 0.9203 nan 0.3000 0.0005
## 7 0.8958 nan 0.3000 0.0043
## 8 0.8781 nan 0.3000 -0.0010
## 9 0.8653 nan 0.3000 -0.0028
## 10 0.8427 nan 0.3000 -0.0003
## 20 0.7659 nan 0.3000 -0.0056
## 40 0.6436 nan 0.3000 -0.0074
## 60 0.5529 nan 0.3000 -0.0064
## 80 0.4838 nan 0.3000 -0.0048
## 100 0.4305 nan 0.3000 -0.0023
## 120 0.3726 nan 0.3000 -0.0016
## 140 0.3264 nan 0.3000 -0.0047
## 160 0.2834 nan 0.3000 -0.0015
## 180 0.2517 nan 0.3000 -0.0026
## 200 0.2281 nan 0.3000 -0.0020
## 220 0.2074 nan 0.3000 -0.0007
## 240 0.1888 nan 0.3000 -0.0022
## 260 0.1727 nan 0.3000 -0.0022
## 280 0.1568 nan 0.3000 -0.0005
## 300 0.1439 nan 0.3000 -0.0010
## 320 0.1341 nan 0.3000 -0.0006
## 340 0.1230 nan 0.3000 -0.0007
## 360 0.1110 nan 0.3000 -0.0015
## 380 0.1017 nan 0.3000 -0.0011
## 400 0.0943 nan 0.3000 -0.0003
## 420 0.0862 nan 0.3000 -0.0009
## 440 0.0799 nan 0.3000 -0.0003
## 460 0.0740 nan 0.3000 -0.0003
## 480 0.0705 nan 0.3000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1597 nan 0.3000 0.0612
## 2 1.0729 nan 0.3000 0.0388
## 3 1.0107 nan 0.3000 0.0261
## 4 0.9777 nan 0.3000 0.0047
## 5 0.9394 nan 0.3000 0.0178
## 6 0.9174 nan 0.3000 -0.0005
## 7 0.8932 nan 0.3000 0.0040
## 8 0.8772 nan 0.3000 0.0002
## 9 0.8615 nan 0.3000 0.0009
## 10 0.8445 nan 0.3000 0.0011
## 20 0.7455 nan 0.3000 -0.0053
## 40 0.6286 nan 0.3000 -0.0015
## 60 0.5428 nan 0.3000 -0.0042
## 80 0.4650 nan 0.3000 -0.0010
## 100 0.4072 nan 0.3000 -0.0020
## 120 0.3835 nan 0.3000 -0.0037
## 140 0.3378 nan 0.3000 -0.0030
## 160 0.3070 nan 0.3000 -0.0004
## 180 0.2663 nan 0.3000 -0.0029
## 200 0.2363 nan 0.3000 -0.0023
## 220 0.2105 nan 0.3000 -0.0004
## 240 0.1896 nan 0.3000 -0.0017
## 260 0.1737 nan 0.3000 -0.0018
## 280 0.1560 nan 0.3000 -0.0019
## 300 0.1429 nan 0.3000 -0.0011
## 320 0.1305 nan 0.3000 -0.0011
## 340 0.1197 nan 0.3000 -0.0014
## 360 0.1101 nan 0.3000 -0.0013
## 380 0.0970 nan 0.3000 -0.0010
## 400 0.0876 nan 0.3000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1584 nan 0.3000 0.0493
## 2 1.0573 nan 0.3000 0.0384
## 3 1.0081 nan 0.3000 0.0184
## 4 0.9572 nan 0.3000 0.0118
## 5 0.9252 nan 0.3000 0.0103
## 6 0.9037 nan 0.3000 0.0019
## 7 0.8798 nan 0.3000 0.0048
## 8 0.8582 nan 0.3000 0.0029
## 9 0.8414 nan 0.3000 0.0041
## 10 0.8269 nan 0.3000 0.0006
## 20 0.7465 nan 0.3000 0.0002
## 40 0.6384 nan 0.3000 -0.0027
## 60 0.5559 nan 0.3000 -0.0017
## 80 0.4944 nan 0.3000 -0.0027
## 100 0.4173 nan 0.3000 -0.0053
## 120 0.3729 nan 0.3000 -0.0027
## 140 0.3326 nan 0.3000 -0.0019
## 160 0.2868 nan 0.3000 -0.0014
## 180 0.2571 nan 0.3000 -0.0011
## 200 0.2304 nan 0.3000 -0.0013
## 220 0.2084 nan 0.3000 -0.0017
## 240 0.1884 nan 0.3000 -0.0000
## 260 0.1664 nan 0.3000 -0.0015
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## 320 0.1243 nan 0.3000 -0.0017
## 340 0.1139 nan 0.3000 -0.0009
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## 380 0.0954 nan 0.3000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1405 nan 0.5000 0.0276
## 2 1.0905 nan 0.5000 0.0232
## 3 1.0697 nan 0.5000 -0.0068
## 4 1.0277 nan 0.5000 0.0151
## 5 1.0050 nan 0.5000 0.0061
## 6 0.9807 nan 0.5000 0.0049
## 7 0.9683 nan 0.5000 -0.0078
## 8 0.9551 nan 0.5000 0.0038
## 9 0.9519 nan 0.5000 -0.0035
## 10 0.9428 nan 0.5000 -0.0041
## 20 0.8720 nan 0.5000 -0.0042
## 40 0.8208 nan 0.5000 -0.0041
## 60 0.7806 nan 0.5000 -0.0018
## 80 0.8032 nan 0.5000 -0.0027
## 100 0.7139 nan 0.5000 0.0003
## 120 0.6843 nan 0.5000 -0.0064
## 140 0.6699 nan 0.5000 -0.0049
## 160 0.6546 nan 0.5000 -0.0002
## 180 0.6341 nan 0.5000 -0.0065
## 200 0.6292 nan 0.5000 -0.0053
## 220 0.6159 nan 0.5000 -0.0030
## 240 0.6007 nan 0.5000 -0.0040
## 260 0.5791 nan 0.5000 -0.0038
## 280 0.5666 nan 0.5000 -0.0025
## 300 0.5502 nan 0.5000 -0.0022
## 320 0.5415 nan 0.5000 -0.0057
## 340 0.5395 nan 0.5000 -0.0031
## 360 0.5228 nan 0.5000 -0.0046
## 380 0.5100 nan 0.5000 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1533 nan 0.5000 0.0539
## 2 1.0963 nan 0.5000 0.0226
## 3 1.0528 nan 0.5000 0.0202
## 4 1.0090 nan 0.5000 0.0185
## 5 0.9797 nan 0.5000 -0.0010
## 6 0.9660 nan 0.5000 -0.0006
## 7 0.9461 nan 0.5000 0.0036
## 8 0.9339 nan 0.5000 -0.0032
## 9 0.9291 nan 0.5000 -0.0041
## 10 0.9153 nan 0.5000 -0.0019
## 20 0.8869 nan 0.5000 -0.0086
## 40 0.8206 nan 0.5000 -0.0086
## 60 0.7918 nan 0.5000 -0.0019
## 80 0.7679 nan 0.5000 -0.0042
## 100 0.7400 nan 0.5000 -0.0018
## 120 0.7156 nan 0.5000 0.0012
## 140 0.6972 nan 0.5000 -0.0043
## 160 0.6866 nan 0.5000 -0.0055
## 180 0.6664 nan 0.5000 -0.0027
## 200 0.6487 nan 0.5000 -0.0068
## 220 0.6315 nan 0.5000 -0.0079
## 240 0.6089 nan 0.5000 -0.0065
## 260 0.5997 nan 0.5000 0.0010
## 280 0.5862 nan 0.5000 -0.0023
## 300 0.5690 nan 0.5000 -0.0035
## 320 0.5641 nan 0.5000 -0.0031
## 340 0.5566 nan 0.5000 -0.0098
## 360 0.5477 nan 0.5000 -0.0039
## 380 0.5380 nan 0.5000 -0.0025
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## 460 0.5038 nan 0.5000 -0.0033
## 480 0.4932 nan 0.5000 -0.0082
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1500 nan 0.5000 0.0630
## 2 1.0874 nan 0.5000 0.0231
## 3 1.0350 nan 0.5000 0.0200
## 4 1.0020 nan 0.5000 -0.0005
## 5 0.9643 nan 0.5000 0.0178
## 6 0.9608 nan 0.5000 -0.0107
## 7 0.9432 nan 0.5000 -0.0019
## 8 0.9355 nan 0.5000 0.0018
## 9 0.9374 nan 0.5000 -0.0141
## 10 0.9233 nan 0.5000 0.0006
## 20 0.8553 nan 0.5000 -0.0030
## 40 0.7997 nan 0.5000 -0.0063
## 60 0.7811 nan 0.5000 -0.0018
## 80 0.7557 nan 0.5000 -0.0082
## 100 0.7330 nan 0.5000 -0.0045
## 120 0.7275 nan 0.5000 -0.0070
## 140 0.7097 nan 0.5000 -0.0049
## 160 0.6841 nan 0.5000 -0.0015
## 180 0.6542 nan 0.5000 -0.0013
## 200 0.6323 nan 0.5000 0.0001
## 220 0.6256 nan 0.5000 -0.0072
## 240 0.6139 nan 0.5000 -0.0023
## 260 0.6074 nan 0.5000 -0.0055
## 280 0.5948 nan 0.5000 -0.0063
## 300 0.5857 nan 0.5000 -0.0022
## 320 0.5831 nan 0.5000 -0.0032
## 340 0.5713 nan 0.5000 -0.0065
## 360 0.5592 nan 0.5000 -0.0061
## 380 0.5517 nan 0.5000 -0.0027
## 400 0.5432 nan 0.5000 -0.0029
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## 460 0.5188 nan 0.5000 -0.0024
## 480 0.5038 nan 0.5000 -0.0073
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1249 nan 0.5000 0.0888
## 2 1.0366 nan 0.5000 0.0356
## 3 0.9711 nan 0.5000 0.0148
## 4 0.9384 nan 0.5000 0.0050
## 5 0.9227 nan 0.5000 -0.0094
## 6 0.9104 nan 0.5000 -0.0011
## 7 0.8936 nan 0.5000 0.0024
## 8 0.8817 nan 0.5000 -0.0061
## 9 0.8691 nan 0.5000 0.0014
## 10 0.8568 nan 0.5000 -0.0048
## 20 0.7890 nan 0.5000 -0.0019
## 40 0.6709 nan 0.5000 -0.0069
## 60 0.5792 nan 0.5000 -0.0042
## 80 0.5127 nan 0.5000 -0.0074
## 100 0.4569 nan 0.5000 -0.0047
## 120 0.4177 nan 0.5000 0.0003
## 140 0.3777 nan 0.5000 -0.0056
## 160 0.3391 nan 0.5000 -0.0073
## 180 0.9009 nan 0.5000 -0.0049
## 200 0.8725 nan 0.5000 -0.0015
## 220 0.8153 nan 0.5000 -0.0025
## 240 0.7882 nan 0.5000 -0.0024
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1346 nan 0.5000 0.0702
## 2 1.0398 nan 0.5000 0.0492
## 3 0.9951 nan 0.5000 0.0122
## 4 0.9514 nan 0.5000 0.0115
## 5 0.9170 nan 0.5000 0.0056
## 6 0.8955 nan 0.5000 0.0019
## 7 0.8776 nan 0.5000 0.0003
## 8 0.8611 nan 0.5000 -0.0035
## 9 0.8504 nan 0.5000 -0.0001
## 10 0.8444 nan 0.5000 -0.0066
## 20 0.7623 nan 0.5000 -0.0030
## 40 0.6762 nan 0.5000 -0.0035
## 60 0.6089 nan 0.5000 -0.0055
## 80 0.5403 nan 0.5000 -0.0152
## 100 0.4874 nan 0.5000 -0.0077
## 120 0.4435 nan 0.5000 -0.0081
## 140 0.3958 nan 0.5000 -0.0121
## 160 0.3731 nan 0.5000 -0.0075
## 180 0.3300 nan 0.5000 -0.0024
## 200 0.2865 nan 0.5000 -0.0050
## 220 0.2523 nan 0.5000 -0.0024
## 240 0.2287 nan 0.5000 -0.0050
## 260 0.2087 nan 0.5000 0.0001
## 280 0.1882 nan 0.5000 -0.0033
## 300 0.1727 nan 0.5000 -0.0014
## 320 0.1612 nan 0.5000 -0.0001
## 340 0.1444 nan 0.5000 -0.0036
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1222 nan 0.5000 0.0776
## 2 1.0239 nan 0.5000 0.0407
## 3 0.9830 nan 0.5000 0.0017
## 4 0.9412 nan 0.5000 0.0184
## 5 0.9165 nan 0.5000 0.0036
## 6 0.8995 nan 0.5000 0.0045
## 7 0.8919 nan 0.5000 -0.0080
## 8 0.8812 nan 0.5000 -0.0139
## 9 0.8649 nan 0.5000 0.0018
## 10 0.8614 nan 0.5000 -0.0088
## 20 0.7958 nan 0.5000 -0.0037
## 40 0.6811 nan 0.5000 -0.0086
## 60 0.6038 nan 0.5000 -0.0101
## 80 0.5356 nan 0.5000 -0.0121
## 100 0.4810 nan 0.5000 -0.0056
## 120 0.4230 nan 0.5000 -0.0031
## 140 0.3844 nan 0.5000 -0.0011
## 160 0.3561 nan 0.5000 -0.0039
## 180 0.3199 nan 0.5000 -0.0017
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## 320 0.1605 nan 0.5000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0909 nan 0.5000 0.0841
## 2 1.0127 nan 0.5000 0.0243
## 3 0.9465 nan 0.5000 0.0242
## 4 0.9034 nan 0.5000 0.0168
## 5 0.8889 nan 0.5000 -0.0068
## 6 0.8866 nan 0.5000 -0.0217
## 7 0.8585 nan 0.5000 0.0012
## 8 0.8423 nan 0.5000 -0.0034
## 9 0.8363 nan 0.5000 -0.0069
## 10 0.8206 nan 0.5000 -0.0156
## 20 0.7239 nan 0.5000 -0.0063
## 40 0.6065 nan 0.5000 -0.0041
## 60 0.7013 nan 0.5000 -0.0046
## 80 0.8719 nan 0.5000 -0.0034
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 -0.0000
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0995 nan 0.5000 0.0874
## 2 0.9964 nan 0.5000 0.0346
## 3 0.9258 nan 0.5000 0.0310
## 4 0.8957 nan 0.5000 0.0002
## 5 0.8701 nan 0.5000 0.0057
## 6 0.8491 nan 0.5000 -0.0093
## 7 0.8125 nan 0.5000 0.0059
## 8 0.7990 nan 0.5000 -0.0081
## 9 0.7906 nan 0.5000 -0.0149
## 10 0.7761 nan 0.5000 -0.0076
## 20 0.6780 nan 0.5000 -0.0040
## 40 0.5577 nan 0.5000 0.0003
## 60 0.4483 nan 0.5000 -0.0115
## 80 0.3901 nan 0.5000 -0.0088
## 100 0.3244 nan 0.5000 -0.0094
## 120 0.2779 nan 0.5000 -0.0036
## 140 0.2284 nan 0.5000 -0.0011
## 160 0.1905 nan 0.5000 -0.0070
## 180 0.1565 nan 0.5000 -0.0043
## 200 0.1360 nan 0.5000 -0.0016
## 220 0.1146 nan 0.5000 -0.0013
## 240 0.1026 nan 0.5000 -0.0014
## 260 0.0914 nan 0.5000 -0.0039
## 280 0.0764 nan 0.5000 0.0000
## 300 0.0647 nan 0.5000 -0.0000
## 320 0.0586 nan 0.5000 -0.0006
## 340 0.0504 nan 0.5000 -0.0003
## 360 0.0446 nan 0.5000 -0.0016
## 380 0.0392 nan 0.5000 -0.0011
## 400 0.0349 nan 0.5000 -0.0006
## 420 0.0315 nan 0.5000 -0.0006
## 440 0.0283 nan 0.5000 -0.0005
## 460 0.0256 nan 0.5000 -0.0005
## 480 0.0223 nan 0.5000 -0.0002
## 500 0.0195 nan 0.5000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0983 nan 0.5000 0.0953
## 2 0.9804 nan 0.5000 0.0543
## 3 0.9362 nan 0.5000 0.0033
## 4 0.8995 nan 0.5000 0.0037
## 5 0.8685 nan 0.5000 0.0037
## 6 0.8488 nan 0.5000 -0.0039
## 7 0.8435 nan 0.5000 -0.0116
## 8 0.8335 nan 0.5000 -0.0176
## 9 0.8144 nan 0.5000 0.0014
## 10 0.8129 nan 0.5000 -0.0248
## 20 0.7193 nan 0.5000 -0.0116
## 40 0.5934 nan 0.5000 -0.0182
## 60 0.5380 nan 0.5000 -0.0158
## 80 0.4438 nan 0.5000 -0.0077
## 100 0.3841 nan 0.5000 -0.0092
## 120 0.3187 nan 0.5000 -0.0076
## 140 0.2673 nan 0.5000 -0.0047
## 160 0.2254 nan 0.5000 -0.0022
## 180 0.1867 nan 0.5000 -0.0018
## 200 0.1585 nan 0.5000 -0.0017
## 220 0.1313 nan 0.5000 -0.0018
## 240 0.1131 nan 0.5000 -0.0003
## 260 0.0994 nan 0.5000 -0.0003
## 280 0.0850 nan 0.5000 -0.0007
## 300 0.0740 nan 0.5000 -0.0017
## 320 0.0665 nan 0.5000 -0.0023
## 340 0.0585 nan 0.5000 -0.0021
## 360 0.0499 nan 0.5000 -0.0004
## 380 0.0440 nan 0.5000 -0.0006
## 400 0.0385 nan 0.5000 -0.0004
## 420 0.0338 nan 0.5000 -0.0006
## 440 0.0296 nan 0.5000 -0.0003
## 460 0.0255 nan 0.5000 -0.0003
## 480 0.0226 nan 0.5000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1438 nan 1.0000 0.0411
## 2 1.0793 nan 1.0000 0.0258
## 3 1.0249 nan 1.0000 0.0161
## 4 1.0114 nan 1.0000 -0.0105
## 5 1.0120 nan 1.0000 -0.0213
## 6 1.0051 nan 1.0000 -0.0160
## 7 0.9812 nan 1.0000 -0.0007
## 8 0.9782 nan 1.0000 -0.0152
## 9 0.9440 nan 1.0000 0.0010
## 10 0.9478 nan 1.0000 -0.0300
## 20 0.9318 nan 1.0000 -0.0333
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1393 nan 1.0000 0.0523
## 2 1.0629 nan 1.0000 0.0245
## 3 0.9865 nan 1.0000 0.0403
## 4 0.9433 nan 1.0000 0.0197
## 5 0.9425 nan 1.0000 -0.0182
## 6 0.9176 nan 1.0000 0.0031
## 7 0.9066 nan 1.0000 -0.0139
## 8 0.8991 nan 1.0000 -0.0110
## 9 0.8851 nan 1.0000 -0.0027
## 10 0.8832 nan 1.0000 -0.0075
## 20 0.8358 nan 1.0000 -0.0257
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1128 nan 1.0000 0.0827
## 2 1.0538 nan 1.0000 0.0188
## 3 0.9975 nan 1.0000 0.0032
## 4 0.9748 nan 1.0000 -0.0049
## 5 0.9516 nan 1.0000 0.0044
## 6 0.9427 nan 1.0000 -0.0106
## 7 0.9595 nan 1.0000 -0.0403
## 8 0.9663 nan 1.0000 -0.0242
## 9 0.9700 nan 1.0000 -0.0192
## 10 1.0035 nan 1.0000 -0.0035
## 20 0.9774 nan 1.0000 -0.0698
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0628 nan 1.0000 0.0974
## 2 0.9924 nan 1.0000 0.0254
## 3 0.9893 nan 1.0000 -0.0265
## 4 0.9956 nan 1.0000 -0.0422
## 5 0.9795 nan 1.0000 -0.0280
## 6 0.9753 nan 1.0000 -0.0203
## 7 0.9667 nan 1.0000 -0.0171
## 8 9452.3197 nan 1.0000 -9451.4068
## 9 9452.2987 nan 1.0000 0.0049
## 10 9452.3019 nan 1.0000 -0.0301
## 20 9452.3393 nan 1.0000 -0.0380
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 -0.0464
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 inf
## 200 inf nan 1.0000 -0.1423
## 220 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0724 nan 1.0000 0.0901
## 2 0.9948 nan 1.0000 0.0139
## 3 0.9837 nan 1.0000 -0.0162
## 4 1.0734 nan 1.0000 -0.1145
## 5 1.0784 nan 1.0000 -0.0527
## 6 1.1603 nan 1.0000 -0.1277
## 7 2.3378 nan 1.0000 -0.9167
## 8 2.3338 nan 1.0000 -0.0164
## 9 2.3114 nan 1.0000 0.0076
## 10 2.3135 nan 1.0000 -0.0144
## 20 4.0213 nan 1.0000 -0.0277
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0573 nan 1.0000 0.1000
## 2 0.9729 nan 1.0000 0.0122
## 3 0.9500 nan 1.0000 -0.0126
## 4 0.9692 nan 1.0000 -0.0475
## 5 0.9361 nan 1.0000 0.0134
## 6 0.9351 nan 1.0000 -0.0305
## 7 0.9245 nan 1.0000 -0.0195
## 8 1.0211 nan 1.0000 -0.1151
## 9 164.2296 nan 1.0000 0.0214
## 10 164.2280 nan 1.0000 -0.0290
## 20 164.1512 nan 1.0000 0.0019
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0336 nan 1.0000 0.0669
## 2 0.9963 nan 1.0000 0.0027
## 3 0.9107 nan 1.0000 0.0305
## 4 0.9034 nan 1.0000 -0.0336
## 5 0.9483 nan 1.0000 -0.0901
## 6 0.9302 nan 1.0000 -0.0379
## 7 0.8866 nan 1.0000 -0.0143
## 8 0.8938 nan 1.0000 -0.0671
## 9 0.8846 nan 1.0000 -0.0201
## 10 0.8656 nan 1.0000 -0.0323
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0602 nan 1.0000 0.0934
## 2 0.9411 nan 1.0000 0.0267
## 3 0.8890 nan 1.0000 0.0122
## 4 0.8873 nan 1.0000 -0.0429
## 5 0.8731 nan 1.0000 -0.0158
## 6 0.8540 nan 1.0000 -0.0344
## 7 0.8265 nan 1.0000 -0.0074
## 8 0.8245 nan 1.0000 -0.0305
## 9 0.8151 nan 1.0000 -0.0123
## 10 0.8088 nan 1.0000 -0.0211
## 20 0.7556 nan 1.0000 0.0544
## 40 13.3954 nan 1.0000 -0.5009
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 300 inf nan 1.0000 inf
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0626 nan 1.0000 0.0801
## 2 1.0018 nan 1.0000 -0.0081
## 3 0.9083 nan 1.0000 0.0388
## 4 0.8861 nan 1.0000 -0.0227
## 5 1.0433 nan 1.0000 -0.1919
## 6 49.4796 nan 1.0000 -48.5403
## 7 49.4700 nan 1.0000 -0.0179
## 8 49.4542 nan 1.0000 -0.0060
## 9 49.4352 nan 1.0000 -0.0419
## 10 49.4264 nan 1.0000 -0.0644
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## 3 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 40 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## 40 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## 3 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## 3 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2914 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2906 nan 0.0010 0.0002
## 8 1.2903 nan 0.0010 0.0002
## 9 1.2899 nan 0.0010 0.0002
## 10 1.2895 nan 0.0010 0.0002
## 20 1.2859 nan 0.0010 0.0002
## 40 1.2782 nan 0.0010 0.0002
## 60 1.2706 nan 0.0010 0.0002
## 80 1.2635 nan 0.0010 0.0002
## 100 1.2567 nan 0.0010 0.0002
## 120 1.2502 nan 0.0010 0.0001
## 140 1.2439 nan 0.0010 0.0002
## 160 1.2378 nan 0.0010 0.0001
## 180 1.2319 nan 0.0010 0.0001
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## 380 1.1825 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2921 nan 0.0010 0.0002
## 4 1.2917 nan 0.0010 0.0002
## 5 1.2913 nan 0.0010 0.0002
## 6 1.2909 nan 0.0010 0.0002
## 7 1.2905 nan 0.0010 0.0002
## 8 1.2901 nan 0.0010 0.0002
## 9 1.2897 nan 0.0010 0.0002
## 10 1.2892 nan 0.0010 0.0002
## 20 1.2854 nan 0.0010 0.0002
## 40 1.2781 nan 0.0010 0.0002
## 60 1.2706 nan 0.0010 0.0002
## 80 1.2636 nan 0.0010 0.0002
## 100 1.2567 nan 0.0010 0.0001
## 120 1.2501 nan 0.0010 0.0001
## 140 1.2438 nan 0.0010 0.0001
## 160 1.2376 nan 0.0010 0.0001
## 180 1.2316 nan 0.0010 0.0001
## 200 1.2258 nan 0.0010 0.0001
## 220 1.2203 nan 0.0010 0.0001
## 240 1.2150 nan 0.0010 0.0001
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## 280 1.2049 nan 0.0010 0.0001
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## 320 1.1955 nan 0.0010 0.0001
## 340 1.1911 nan 0.0010 0.0001
## 360 1.1868 nan 0.0010 0.0001
## 380 1.1824 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0001
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2900 nan 0.0010 0.0002
## 10 1.2896 nan 0.0010 0.0002
## 20 1.2856 nan 0.0010 0.0002
## 40 1.2779 nan 0.0010 0.0002
## 60 1.2705 nan 0.0010 0.0002
## 80 1.2631 nan 0.0010 0.0002
## 100 1.2562 nan 0.0010 0.0002
## 120 1.2498 nan 0.0010 0.0001
## 140 1.2438 nan 0.0010 0.0001
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## 180 1.2319 nan 0.0010 0.0001
## 200 1.2261 nan 0.0010 0.0001
## 220 1.2206 nan 0.0010 0.0001
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## 260 1.2100 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2913 nan 0.0010 0.0003
## 5 1.2908 nan 0.0010 0.0002
## 6 1.2903 nan 0.0010 0.0002
## 7 1.2898 nan 0.0010 0.0002
## 8 1.2893 nan 0.0010 0.0002
## 9 1.2888 nan 0.0010 0.0002
## 10 1.2884 nan 0.0010 0.0002
## 20 1.2834 nan 0.0010 0.0002
## 40 1.2738 nan 0.0010 0.0002
## 60 1.2646 nan 0.0010 0.0002
## 80 1.2555 nan 0.0010 0.0002
## 100 1.2470 nan 0.0010 0.0002
## 120 1.2390 nan 0.0010 0.0002
## 140 1.2308 nan 0.0010 0.0002
## 160 1.2229 nan 0.0010 0.0002
## 180 1.2156 nan 0.0010 0.0002
## 200 1.2082 nan 0.0010 0.0002
## 220 1.2009 nan 0.0010 0.0002
## 240 1.1940 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2913 nan 0.0010 0.0002
## 5 1.2908 nan 0.0010 0.0002
## 6 1.2902 nan 0.0010 0.0002
## 7 1.2898 nan 0.0010 0.0002
## 8 1.2892 nan 0.0010 0.0002
## 9 1.2887 nan 0.0010 0.0002
## 10 1.2882 nan 0.0010 0.0002
## 20 1.2833 nan 0.0010 0.0002
## 40 1.2736 nan 0.0010 0.0002
## 60 1.2646 nan 0.0010 0.0002
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## 140 1.2309 nan 0.0010 0.0002
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## 180 1.2155 nan 0.0010 0.0002
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## 220 1.2012 nan 0.0010 0.0001
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## 380 1.1507 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2899 nan 0.0010 0.0002
## 8 1.2894 nan 0.0010 0.0002
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## 10 1.2884 nan 0.0010 0.0002
## 20 1.2835 nan 0.0010 0.0002
## 40 1.2739 nan 0.0010 0.0002
## 60 1.2645 nan 0.0010 0.0002
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## 140 1.2307 nan 0.0010 0.0001
## 160 1.2227 nan 0.0010 0.0002
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## 220 1.2010 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2927 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0002
## 3 1.2916 nan 0.0010 0.0002
## 4 1.2910 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2900 nan 0.0010 0.0002
## 7 1.2894 nan 0.0010 0.0003
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## 9 1.2883 nan 0.0010 0.0003
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## 20 1.2823 nan 0.0010 0.0002
## 40 1.2714 nan 0.0010 0.0003
## 60 1.2609 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2927 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2916 nan 0.0010 0.0003
## 4 1.2910 nan 0.0010 0.0003
## 5 1.2905 nan 0.0010 0.0002
## 6 1.2899 nan 0.0010 0.0002
## 7 1.2893 nan 0.0010 0.0003
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## 20 1.2819 nan 0.0010 0.0002
## 40 1.2712 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0003
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0003
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## 10 1.2878 nan 0.0010 0.0003
## 20 1.2821 nan 0.0010 0.0002
## 40 1.2712 nan 0.0010 0.0002
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## 340 1.1450 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2576 nan 0.1000 0.0187
## 2 1.2228 nan 0.1000 0.0149
## 3 1.2037 nan 0.1000 0.0074
## 4 1.1775 nan 0.1000 0.0099
## 5 1.1542 nan 0.1000 0.0091
## 6 1.1392 nan 0.1000 0.0050
## 7 1.1221 nan 0.1000 0.0066
## 8 1.1049 nan 0.1000 0.0077
## 9 1.0906 nan 0.1000 0.0059
## 10 1.0811 nan 0.1000 0.0035
## 20 0.9972 nan 0.1000 0.0007
## 40 0.9117 nan 0.1000 0.0003
## 60 0.8679 nan 0.1000 0.0001
## 80 0.8383 nan 0.1000 -0.0005
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## 140 0.7901 nan 0.1000 -0.0003
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## 180 0.7680 nan 0.1000 -0.0004
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## 240 0.7434 nan 0.1000 -0.0009
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## 460 0.6870 nan 0.1000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2576 nan 0.1000 0.0173
## 2 1.2249 nan 0.1000 0.0129
## 3 1.1974 nan 0.1000 0.0128
## 4 1.1776 nan 0.1000 0.0072
## 5 1.1534 nan 0.1000 0.0095
## 6 1.1363 nan 0.1000 0.0072
## 7 1.1189 nan 0.1000 0.0069
## 8 1.1040 nan 0.1000 0.0067
## 9 1.0914 nan 0.1000 0.0053
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## 20 0.9957 nan 0.1000 0.0026
## 40 0.9130 nan 0.1000 0.0005
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## 80 0.8433 nan 0.1000 -0.0010
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## 180 0.7722 nan 0.1000 -0.0003
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## 280 0.7344 nan 0.1000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2561 nan 0.1000 0.0167
## 2 1.2286 nan 0.1000 0.0142
## 3 1.1968 nan 0.1000 0.0111
## 4 1.1756 nan 0.1000 0.0100
## 5 1.1592 nan 0.1000 0.0056
## 6 1.1433 nan 0.1000 0.0077
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## 8 1.1134 nan 0.1000 0.0067
## 9 1.1006 nan 0.1000 0.0052
## 10 1.0853 nan 0.1000 0.0060
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## 320 0.7207 nan 0.1000 -0.0007
## 340 0.7162 nan 0.1000 -0.0013
## 360 0.7089 nan 0.1000 -0.0009
## 380 0.7033 nan 0.1000 -0.0002
## 400 0.6990 nan 0.1000 -0.0009
## 420 0.6948 nan 0.1000 -0.0006
## 440 0.6913 nan 0.1000 -0.0009
## 460 0.6883 nan 0.1000 -0.0011
## 480 0.6848 nan 0.1000 -0.0007
## 500 0.6810 nan 0.1000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2538 nan 0.1000 0.0171
## 2 1.2117 nan 0.1000 0.0181
## 3 1.1785 nan 0.1000 0.0139
## 4 1.1474 nan 0.1000 0.0136
## 5 1.1197 nan 0.1000 0.0103
## 6 1.0988 nan 0.1000 0.0101
## 7 1.0753 nan 0.1000 0.0097
## 8 1.0606 nan 0.1000 0.0054
## 9 1.0435 nan 0.1000 0.0039
## 10 1.0280 nan 0.1000 0.0029
## 20 0.9329 nan 0.1000 -0.0004
## 40 0.8406 nan 0.1000 -0.0002
## 60 0.7926 nan 0.1000 -0.0013
## 80 0.7622 nan 0.1000 -0.0023
## 100 0.7319 nan 0.1000 -0.0007
## 120 0.7049 nan 0.1000 -0.0004
## 140 0.6776 nan 0.1000 -0.0022
## 160 0.6598 nan 0.1000 -0.0010
## 180 0.6410 nan 0.1000 -0.0012
## 200 0.6183 nan 0.1000 -0.0019
## 220 0.5962 nan 0.1000 -0.0014
## 240 0.5781 nan 0.1000 -0.0001
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## 280 0.5458 nan 0.1000 -0.0014
## 300 0.5310 nan 0.1000 -0.0010
## 320 0.5152 nan 0.1000 -0.0003
## 340 0.5030 nan 0.1000 -0.0013
## 360 0.4930 nan 0.1000 -0.0008
## 380 0.4797 nan 0.1000 -0.0008
## 400 0.4665 nan 0.1000 -0.0004
## 420 0.4548 nan 0.1000 -0.0010
## 440 0.4411 nan 0.1000 -0.0007
## 460 0.4300 nan 0.1000 -0.0001
## 480 0.4204 nan 0.1000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2436 nan 0.1000 0.0212
## 2 1.2041 nan 0.1000 0.0198
## 3 1.1703 nan 0.1000 0.0153
## 4 1.1378 nan 0.1000 0.0137
## 5 1.1135 nan 0.1000 0.0106
## 6 1.0902 nan 0.1000 0.0089
## 7 1.0728 nan 0.1000 0.0064
## 8 1.0551 nan 0.1000 0.0047
## 9 1.0392 nan 0.1000 0.0063
## 10 1.0238 nan 0.1000 0.0043
## 20 0.9266 nan 0.1000 0.0011
## 40 0.8407 nan 0.1000 -0.0004
## 60 0.7922 nan 0.1000 -0.0011
## 80 0.7575 nan 0.1000 -0.0001
## 100 0.7297 nan 0.1000 -0.0018
## 120 0.7092 nan 0.1000 -0.0003
## 140 0.6878 nan 0.1000 -0.0017
## 160 0.6645 nan 0.1000 -0.0013
## 180 0.6427 nan 0.1000 -0.0004
## 200 0.6232 nan 0.1000 -0.0009
## 220 0.6061 nan 0.1000 -0.0009
## 240 0.5876 nan 0.1000 -0.0004
## 260 0.5721 nan 0.1000 -0.0015
## 280 0.5566 nan 0.1000 -0.0007
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## 320 0.5309 nan 0.1000 -0.0005
## 340 0.5161 nan 0.1000 -0.0016
## 360 0.5053 nan 0.1000 -0.0013
## 380 0.4908 nan 0.1000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2440 nan 0.1000 0.0216
## 2 1.2042 nan 0.1000 0.0190
## 3 1.1704 nan 0.1000 0.0141
## 4 1.1388 nan 0.1000 0.0145
## 5 1.1172 nan 0.1000 0.0097
## 6 1.0939 nan 0.1000 0.0099
## 7 1.0732 nan 0.1000 0.0079
## 8 1.0580 nan 0.1000 0.0054
## 9 1.0403 nan 0.1000 0.0067
## 10 1.0233 nan 0.1000 0.0075
## 20 0.9222 nan 0.1000 -0.0004
## 40 0.8451 nan 0.1000 -0.0006
## 60 0.7972 nan 0.1000 -0.0026
## 80 0.7628 nan 0.1000 -0.0005
## 100 0.7393 nan 0.1000 -0.0009
## 120 0.7141 nan 0.1000 -0.0012
## 140 0.6920 nan 0.1000 -0.0015
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## 180 0.6562 nan 0.1000 -0.0014
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## 220 0.6150 nan 0.1000 -0.0011
## 240 0.5954 nan 0.1000 -0.0001
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## 280 0.5653 nan 0.1000 -0.0009
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## 320 0.5366 nan 0.1000 -0.0003
## 340 0.5252 nan 0.1000 -0.0013
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## 380 0.5028 nan 0.1000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2474 nan 0.1000 0.0204
## 2 1.1979 nan 0.1000 0.0194
## 3 1.1618 nan 0.1000 0.0163
## 4 1.1260 nan 0.1000 0.0159
## 5 1.0947 nan 0.1000 0.0144
## 6 1.0701 nan 0.1000 0.0093
## 7 1.0478 nan 0.1000 0.0069
## 8 1.0256 nan 0.1000 0.0081
## 9 1.0084 nan 0.1000 0.0058
## 10 0.9912 nan 0.1000 0.0075
## 20 0.8883 nan 0.1000 0.0014
## 40 0.7888 nan 0.1000 -0.0015
## 60 0.7318 nan 0.1000 -0.0010
## 80 0.6836 nan 0.1000 -0.0014
## 100 0.6470 nan 0.1000 -0.0008
## 120 0.6154 nan 0.1000 -0.0007
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## 240 0.4532 nan 0.1000 -0.0006
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## 280 0.4145 nan 0.1000 -0.0001
## 300 0.3967 nan 0.1000 -0.0011
## 320 0.3835 nan 0.1000 -0.0008
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## 380 0.3405 nan 0.1000 -0.0013
## 400 0.3247 nan 0.1000 -0.0007
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## 460 0.2933 nan 0.1000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2428 nan 0.1000 0.0204
## 2 1.1946 nan 0.1000 0.0232
## 3 1.1512 nan 0.1000 0.0187
## 4 1.1230 nan 0.1000 0.0128
## 5 1.0993 nan 0.1000 0.0101
## 6 1.0704 nan 0.1000 0.0114
## 7 1.0474 nan 0.1000 0.0084
## 8 1.0263 nan 0.1000 0.0077
## 9 1.0061 nan 0.1000 0.0071
## 10 0.9914 nan 0.1000 0.0044
## 20 0.8874 nan 0.1000 -0.0002
## 40 0.7844 nan 0.1000 -0.0011
## 60 0.7272 nan 0.1000 -0.0025
## 80 0.6753 nan 0.1000 -0.0012
## 100 0.6390 nan 0.1000 -0.0012
## 120 0.6072 nan 0.1000 -0.0012
## 140 0.5798 nan 0.1000 -0.0013
## 160 0.5486 nan 0.1000 -0.0014
## 180 0.5279 nan 0.1000 -0.0016
## 200 0.5042 nan 0.1000 -0.0009
## 220 0.4842 nan 0.1000 -0.0011
## 240 0.4582 nan 0.1000 -0.0013
## 260 0.4371 nan 0.1000 -0.0004
## 280 0.4202 nan 0.1000 -0.0004
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## 320 0.3865 nan 0.1000 -0.0013
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## 380 0.3400 nan 0.1000 -0.0007
## 400 0.3276 nan 0.1000 -0.0010
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## 440 0.3042 nan 0.1000 -0.0015
## 460 0.2910 nan 0.1000 -0.0002
## 480 0.2795 nan 0.1000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2364 nan 0.1000 0.0224
## 2 1.1926 nan 0.1000 0.0178
## 3 1.1523 nan 0.1000 0.0167
## 4 1.1144 nan 0.1000 0.0136
## 5 1.0840 nan 0.1000 0.0131
## 6 1.0589 nan 0.1000 0.0107
## 7 1.0342 nan 0.1000 0.0068
## 8 1.0141 nan 0.1000 0.0085
## 9 0.9990 nan 0.1000 0.0057
## 10 0.9826 nan 0.1000 0.0052
## 20 0.8846 nan 0.1000 0.0011
## 40 0.7835 nan 0.1000 -0.0001
## 60 0.7228 nan 0.1000 -0.0014
## 80 0.6742 nan 0.1000 -0.0019
## 100 0.6413 nan 0.1000 -0.0020
## 120 0.6129 nan 0.1000 -0.0006
## 140 0.5827 nan 0.1000 -0.0014
## 160 0.5562 nan 0.1000 -0.0015
## 180 0.5332 nan 0.1000 -0.0019
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## 220 0.4896 nan 0.1000 -0.0006
## 240 0.4680 nan 0.1000 -0.0007
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## 280 0.4288 nan 0.1000 -0.0009
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## 380 0.3477 nan 0.1000 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2158 nan 0.2000 0.0309
## 2 1.1696 nan 0.2000 0.0232
## 3 1.1404 nan 0.2000 0.0112
## 4 1.1099 nan 0.2000 0.0135
## 5 1.0853 nan 0.2000 0.0052
## 6 1.0564 nan 0.2000 0.0107
## 7 1.0340 nan 0.2000 0.0088
## 8 1.0180 nan 0.2000 0.0058
## 9 1.0049 nan 0.2000 0.0033
## 10 0.9940 nan 0.2000 0.0033
## 20 0.9128 nan 0.2000 -0.0003
## 40 0.8449 nan 0.2000 -0.0011
## 60 0.8096 nan 0.2000 0.0005
## 80 0.7913 nan 0.2000 -0.0023
## 100 0.7703 nan 0.2000 -0.0009
## 120 0.7521 nan 0.2000 -0.0032
## 140 0.7389 nan 0.2000 -0.0039
## 160 0.7234 nan 0.2000 -0.0014
## 180 0.7143 nan 0.2000 -0.0014
## 200 0.7086 nan 0.2000 -0.0037
## 220 0.7006 nan 0.2000 -0.0041
## 240 0.6922 nan 0.2000 -0.0005
## 260 0.6825 nan 0.2000 -0.0012
## 280 0.6757 nan 0.2000 -0.0021
## 300 0.6723 nan 0.2000 -0.0029
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## 340 0.6604 nan 0.2000 -0.0017
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## 380 0.6502 nan 0.2000 -0.0032
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2207 nan 0.2000 0.0319
## 2 1.1727 nan 0.2000 0.0218
## 3 1.1412 nan 0.2000 0.0109
## 4 1.1031 nan 0.2000 0.0110
## 5 1.0750 nan 0.2000 0.0124
## 6 1.0512 nan 0.2000 0.0084
## 7 1.0344 nan 0.2000 0.0058
## 8 1.0236 nan 0.2000 0.0015
## 9 1.0052 nan 0.2000 0.0072
## 10 0.9900 nan 0.2000 0.0043
## 20 0.9057 nan 0.2000 -0.0005
## 40 0.8342 nan 0.2000 -0.0016
## 60 0.7984 nan 0.2000 -0.0040
## 80 0.7755 nan 0.2000 -0.0014
## 100 0.7561 nan 0.2000 -0.0022
## 120 0.7413 nan 0.2000 -0.0015
## 140 0.7304 nan 0.2000 -0.0023
## 160 0.7161 nan 0.2000 -0.0016
## 180 0.7080 nan 0.2000 -0.0028
## 200 0.6989 nan 0.2000 -0.0027
## 220 0.6879 nan 0.2000 -0.0025
## 240 0.6793 nan 0.2000 0.0001
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## 280 0.6673 nan 0.2000 -0.0037
## 300 0.6610 nan 0.2000 -0.0016
## 320 0.6549 nan 0.2000 -0.0014
## 340 0.6493 nan 0.2000 -0.0018
## 360 0.6428 nan 0.2000 -0.0007
## 380 0.6391 nan 0.2000 -0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2138 nan 0.2000 0.0335
## 2 1.1630 nan 0.2000 0.0206
## 3 1.1317 nan 0.2000 0.0150
## 4 1.1053 nan 0.2000 0.0079
## 5 1.0777 nan 0.2000 0.0085
## 6 1.0588 nan 0.2000 0.0070
## 7 1.0374 nan 0.2000 0.0095
## 8 1.0197 nan 0.2000 0.0072
## 9 1.0061 nan 0.2000 0.0031
## 10 0.9964 nan 0.2000 0.0016
## 20 0.9027 nan 0.2000 0.0003
## 40 0.8445 nan 0.2000 -0.0005
## 60 0.8044 nan 0.2000 -0.0014
## 80 0.7793 nan 0.2000 -0.0016
## 100 0.7601 nan 0.2000 -0.0028
## 120 0.7455 nan 0.2000 -0.0037
## 140 0.7374 nan 0.2000 -0.0015
## 160 0.7244 nan 0.2000 -0.0009
## 180 0.7151 nan 0.2000 -0.0011
## 200 0.6996 nan 0.2000 -0.0020
## 220 0.6932 nan 0.2000 -0.0017
## 240 0.6856 nan 0.2000 -0.0010
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## 280 0.6706 nan 0.2000 -0.0018
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## 320 0.6608 nan 0.2000 -0.0002
## 340 0.6537 nan 0.2000 -0.0031
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## 380 0.6442 nan 0.2000 -0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2127 nan 0.2000 0.0421
## 2 1.1466 nan 0.2000 0.0316
## 3 1.0956 nan 0.2000 0.0153
## 4 1.0519 nan 0.2000 0.0167
## 5 1.0156 nan 0.2000 0.0167
## 6 0.9893 nan 0.2000 0.0092
## 7 0.9676 nan 0.2000 0.0075
## 8 0.9493 nan 0.2000 0.0025
## 9 0.9353 nan 0.2000 0.0033
## 10 0.9204 nan 0.2000 0.0046
## 20 0.8350 nan 0.2000 -0.0050
## 40 0.7491 nan 0.2000 -0.0009
## 60 0.7030 nan 0.2000 -0.0032
## 80 0.6615 nan 0.2000 -0.0041
## 100 0.6286 nan 0.2000 -0.0058
## 120 0.5931 nan 0.2000 -0.0024
## 140 0.6483 nan 0.2000 -0.0048
## 160 844566.6075 nan 0.2000 -0.0022
## 180 844566.5864 nan 0.2000 -0.0012
## 200 844566.5517 nan 0.2000 -0.0013
## 220 844566.5364 nan 0.2000 -0.0012
## 240 844566.5218 nan 0.2000 -0.0018
## 260 844566.5072 nan 0.2000 -0.0019
## 280 844566.4857 nan 0.2000 -0.0030
## 300 844566.4738 nan 0.2000 -0.0012
## 320 844566.4638 nan 0.2000 -0.0016
## 340 844566.4515 nan 0.2000 -0.0010
## 360 844566.4438 nan 0.2000 -0.0014
## 380 844566.4374 nan 0.2000 -0.0033
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## 420 844566.4257 nan 0.2000 -0.0032
## 440 844566.4094 nan 0.2000 -0.0009
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## 480 844566.3856 nan 0.2000 0.0000
## 500 844566.3718 nan 0.2000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2046 nan 0.2000 0.0382
## 2 1.1405 nan 0.2000 0.0235
## 3 1.0969 nan 0.2000 0.0144
## 4 1.0616 nan 0.2000 0.0106
## 5 1.0317 nan 0.2000 0.0103
## 6 1.0072 nan 0.2000 0.0057
## 7 0.9869 nan 0.2000 0.0017
## 8 0.9639 nan 0.2000 0.0069
## 9 0.9448 nan 0.2000 0.0081
## 10 0.9352 nan 0.2000 0.0012
## 20 0.8420 nan 0.2000 0.0006
## 40 0.7601 nan 0.2000 -0.0018
## 60 0.7092 nan 0.2000 -0.0044
## 80 0.6720 nan 0.2000 -0.0019
## 100 0.6359 nan 0.2000 -0.0029
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## 180 0.5211 nan 0.2000 -0.0017
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## 220 0.4769 nan 0.2000 -0.0034
## 240 0.4574 nan 0.2000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2096 nan 0.2000 0.0383
## 2 1.1451 nan 0.2000 0.0277
## 3 1.0924 nan 0.2000 0.0229
## 4 1.0591 nan 0.2000 0.0116
## 5 1.0253 nan 0.2000 0.0112
## 6 1.0004 nan 0.2000 0.0063
## 7 0.9870 nan 0.2000 0.0001
## 8 0.9656 nan 0.2000 0.0080
## 9 0.9492 nan 0.2000 0.0016
## 10 0.9345 nan 0.2000 0.0025
## 20 0.8374 nan 0.2000 -0.0017
## 40 0.7613 nan 0.2000 -0.0015
## 60 0.7151 nan 0.2000 -0.0033
## 80 0.6689 nan 0.2000 -0.0020
## 100 0.6412 nan 0.2000 -0.0012
## 120 0.6100 nan 0.2000 -0.0011
## 140 0.5745 nan 0.2000 -0.0018
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## 180 0.5198 nan 0.2000 -0.0035
## 200 0.4932 nan 0.2000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1926 nan 0.2000 0.0413
## 2 1.1176 nan 0.2000 0.0348
## 3 1.0686 nan 0.2000 0.0165
## 4 1.0265 nan 0.2000 0.0111
## 5 0.9962 nan 0.2000 0.0059
## 6 0.9656 nan 0.2000 0.0076
## 7 0.9437 nan 0.2000 0.0057
## 8 0.9202 nan 0.2000 0.0067
## 9 0.9052 nan 0.2000 0.0027
## 10 0.8920 nan 0.2000 -0.0006
## 20 0.8053 nan 0.2000 -0.0027
## 40 0.6917 nan 0.2000 -0.0001
## 60 0.6081 nan 0.2000 -0.0009
## 80 0.5594 nan 0.2000 -0.0052
## 100 0.5124 nan 0.2000 -0.0018
## 120 0.4563 nan 0.2000 -0.0032
## 140 0.4214 nan 0.2000 -0.0018
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1946 nan 0.2000 0.0435
## 2 1.1095 nan 0.2000 0.0352
## 3 1.0553 nan 0.2000 0.0215
## 4 1.0170 nan 0.2000 0.0142
## 5 0.9829 nan 0.2000 0.0130
## 6 0.9637 nan 0.2000 0.0052
## 7 0.9470 nan 0.2000 0.0034
## 8 0.9237 nan 0.2000 0.0062
## 9 0.9142 nan 0.2000 -0.0024
## 10 0.8927 nan 0.2000 0.0065
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## 40 0.6882 nan 0.2000 -0.0000
## 60 0.6118 nan 0.2000 -0.0003
## 80 0.5609 nan 0.2000 -0.0034
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## 420 0.1461 nan 0.2000 -0.0001
## 440 0.1369 nan 0.2000 -0.0013
## 460 0.1294 nan 0.2000 -0.0001
## 480 0.1217 nan 0.2000 -0.0007
## 500 0.1143 nan 0.2000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1984 nan 0.2000 0.0364
## 2 1.1199 nan 0.2000 0.0366
## 3 1.0626 nan 0.2000 0.0224
## 4 1.0215 nan 0.2000 0.0116
## 5 0.9913 nan 0.2000 0.0067
## 6 0.9593 nan 0.2000 0.0108
## 7 0.9355 nan 0.2000 0.0028
## 8 0.9174 nan 0.2000 0.0051
## 9 0.9017 nan 0.2000 0.0034
## 10 0.8881 nan 0.2000 0.0011
## 20 0.8000 nan 0.2000 -0.0002
## 40 0.6881 nan 0.2000 -0.0020
## 60 0.6137 nan 0.2000 -0.0035
## 80 0.5487 nan 0.2000 -0.0019
## 100 0.4980 nan 0.2000 -0.0036
## 120 0.4521 nan 0.2000 -0.0019
## 140 0.4170 nan 0.2000 -0.0028
## 160 0.3843 nan 0.2000 -0.0019
## 180 0.3531 nan 0.2000 -0.0012
## 200 0.3297 nan 0.2000 -0.0036
## 220 0.3034 nan 0.2000 -0.0012
## 240 0.2764 nan 0.2000 -0.0026
## 260 0.2646 nan 0.2000 -0.0015
## 280 0.2445 nan 0.2000 -0.0023
## 300 0.2274 nan 0.2000 -0.0008
## 320 0.2078 nan 0.2000 -0.0013
## 340 0.1947 nan 0.2000 -0.0007
## 360 0.1825 nan 0.2000 -0.0009
## 380 0.1711 nan 0.2000 -0.0013
## 400 0.1625 nan 0.2000 -0.0008
## 420 0.1532 nan 0.2000 -0.0009
## 440 0.1417 nan 0.2000 -0.0014
## 460 0.1321 nan 0.2000 -0.0015
## 480 0.1247 nan 0.2000 -0.0010
## 500 0.1161 nan 0.2000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1914 nan 0.3000 0.0524
## 2 1.1401 nan 0.3000 0.0176
## 3 1.0940 nan 0.3000 0.0182
## 4 1.0611 nan 0.3000 0.0141
## 5 1.0228 nan 0.3000 0.0149
## 6 1.0026 nan 0.3000 0.0054
## 7 0.9924 nan 0.3000 0.0007
## 8 0.9777 nan 0.3000 0.0037
## 9 0.9639 nan 0.3000 0.0021
## 10 0.9447 nan 0.3000 0.0069
## 20 0.8665 nan 0.3000 0.0015
## 40 0.8027 nan 0.3000 -0.0010
## 60 0.7672 nan 0.3000 -0.0028
## 80 0.7436 nan 0.3000 -0.0024
## 100 0.7231 nan 0.3000 -0.0061
## 120 0.7065 nan 0.3000 -0.0019
## 140 0.6925 nan 0.3000 -0.0020
## 160 0.6836 nan 0.3000 0.0004
## 180 0.6737 nan 0.3000 -0.0020
## 200 0.6699 nan 0.3000 -0.0028
## 220 0.6612 nan 0.3000 -0.0026
## 240 0.6511 nan 0.3000 -0.0028
## 260 0.6397 nan 0.3000 -0.0017
## 280 0.6313 nan 0.3000 -0.0025
## 300 0.6242 nan 0.3000 -0.0020
## 320 0.6208 nan 0.3000 -0.0029
## 340 0.6134 nan 0.3000 -0.0034
## 360 0.6036 nan 0.3000 -0.0021
## 380 0.6006 nan 0.3000 -0.0034
## 400 0.5954 nan 0.3000 -0.0040
## 420 0.5848 nan 0.3000 -0.0043
## 440 0.5796 nan 0.3000 -0.0042
## 460 0.5732 nan 0.3000 -0.0026
## 480 0.5724 nan 0.3000 -0.0028
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1824 nan 0.3000 0.0475
## 2 1.1284 nan 0.3000 0.0249
## 3 1.0851 nan 0.3000 0.0151
## 4 1.0528 nan 0.3000 0.0100
## 5 1.0312 nan 0.3000 0.0042
## 6 1.0052 nan 0.3000 0.0101
## 7 0.9776 nan 0.3000 0.0132
## 8 0.9635 nan 0.3000 0.0001
## 9 0.9517 nan 0.3000 0.0009
## 10 0.9364 nan 0.3000 0.0024
## 20 0.8620 nan 0.3000 0.0024
## 40 0.8017 nan 0.3000 -0.0019
## 60 0.7710 nan 0.3000 -0.0051
## 80 0.7452 nan 0.3000 -0.0035
## 100 0.7303 nan 0.3000 -0.0003
## 120 0.7173 nan 0.3000 -0.0004
## 140 0.7033 nan 0.3000 -0.0041
## 160 0.6882 nan 0.3000 -0.0024
## 180 0.6742 nan 0.3000 -0.0035
## 200 0.6662 nan 0.3000 -0.0028
## 220 0.6549 nan 0.3000 -0.0016
## 240 0.6436 nan 0.3000 -0.0025
## 260 0.6379 nan 0.3000 -0.0013
## 280 0.6360 nan 0.3000 -0.0056
## 300 0.6239 nan 0.3000 -0.0015
## 320 0.6189 nan 0.3000 -0.0007
## 340 0.6080 nan 0.3000 -0.0047
## 360 0.5994 nan 0.3000 -0.0025
## 380 0.5939 nan 0.3000 -0.0015
## 400 0.5857 nan 0.3000 -0.0032
## 420 0.5814 nan 0.3000 -0.0029
## 440 0.5801 nan 0.3000 -0.0038
## 460 0.5691 nan 0.3000 -0.0027
## 480 0.5637 nan 0.3000 -0.0025
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1798 nan 0.3000 0.0486
## 2 1.1220 nan 0.3000 0.0258
## 3 1.0818 nan 0.3000 0.0165
## 4 1.0503 nan 0.3000 0.0142
## 5 1.0226 nan 0.3000 0.0082
## 6 1.0053 nan 0.3000 0.0034
## 7 0.9881 nan 0.3000 0.0079
## 8 0.9676 nan 0.3000 0.0079
## 9 0.9562 nan 0.3000 -0.0028
## 10 0.9444 nan 0.3000 0.0028
## 20 0.8704 nan 0.3000 -0.0014
## 40 0.8123 nan 0.3000 -0.0036
## 60 0.7698 nan 0.3000 -0.0005
## 80 0.7487 nan 0.3000 -0.0008
## 100 0.7308 nan 0.3000 -0.0007
## 120 0.7118 nan 0.3000 -0.0015
## 140 0.6996 nan 0.3000 -0.0015
## 160 0.6859 nan 0.3000 -0.0029
## 180 0.6693 nan 0.3000 -0.0024
## 200 0.6628 nan 0.3000 -0.0031
## 220 0.6529 nan 0.3000 -0.0009
## 240 0.6449 nan 0.3000 -0.0029
## 260 0.6336 nan 0.3000 -0.0009
## 280 0.6264 nan 0.3000 -0.0019
## 300 0.6245 nan 0.3000 -0.0081
## 320 0.6140 nan 0.3000 -0.0009
## 340 0.6088 nan 0.3000 -0.0044
## 360 0.6036 nan 0.3000 -0.0016
## 380 0.6008 nan 0.3000 -0.0021
## 400 0.5924 nan 0.3000 -0.0005
## 420 0.5906 nan 0.3000 -0.0046
## 440 0.5873 nan 0.3000 -0.0015
## 460 0.5825 nan 0.3000 -0.0037
## 480 0.5805 nan 0.3000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1659 nan 0.3000 0.0526
## 2 1.0888 nan 0.3000 0.0306
## 3 1.0276 nan 0.3000 0.0155
## 4 0.9945 nan 0.3000 0.0091
## 5 0.9704 nan 0.3000 0.0059
## 6 0.9412 nan 0.3000 0.0087
## 7 0.9230 nan 0.3000 0.0068
## 8 0.9083 nan 0.3000 -0.0010
## 9 0.8947 nan 0.3000 0.0030
## 10 0.8771 nan 0.3000 0.0017
## 20 0.8000 nan 0.3000 -0.0067
## 40 0.7083 nan 0.3000 -0.0014
## 60 0.6590 nan 0.3000 -0.0059
## 80 0.6082 nan 0.3000 -0.0045
## 100 0.5625 nan 0.3000 -0.0040
## 120 0.5231 nan 0.3000 -0.0044
## 140 0.4843 nan 0.3000 -0.0019
## 160 0.4483 nan 0.3000 -0.0036
## 180 0.4135 nan 0.3000 -0.0025
## 200 0.3862 nan 0.3000 -0.0033
## 220 0.3542 nan 0.3000 -0.0001
## 240 0.3329 nan 0.3000 -0.0044
## 260 0.3166 nan 0.3000 -0.0032
## 280 0.2962 nan 0.3000 -0.0012
## 300 0.2782 nan 0.3000 -0.0004
## 320 0.2662 nan 0.3000 -0.0030
## 340 0.2483 nan 0.3000 -0.0020
## 360 0.2369 nan 0.3000 -0.0009
## 380 0.2235 nan 0.3000 0.0003
## 400 0.2116 nan 0.3000 -0.0011
## 420 0.2016 nan 0.3000 -0.0033
## 440 0.1907 nan 0.3000 -0.0025
## 460 0.1834 nan 0.3000 -0.0021
## 480 0.1740 nan 0.3000 -0.0013
## 500 0.1639 nan 0.3000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1697 nan 0.3000 0.0586
## 2 1.1088 nan 0.3000 0.0233
## 3 1.0549 nan 0.3000 0.0256
## 4 1.0151 nan 0.3000 0.0133
## 5 0.9822 nan 0.3000 0.0068
## 6 0.9576 nan 0.3000 0.0059
## 7 0.9448 nan 0.3000 -0.0022
## 8 0.9271 nan 0.3000 0.0019
## 9 0.9102 nan 0.3000 0.0014
## 10 0.8917 nan 0.3000 0.0045
## 20 0.8127 nan 0.3000 -0.0019
## 40 0.7135 nan 0.3000 0.0002
## 60 0.6645 nan 0.3000 -0.0036
## 80 0.6051 nan 0.3000 -0.0013
## 100 0.5601 nan 0.3000 -0.0021
## 120 0.5053 nan 0.3000 -0.0040
## 140 0.4710 nan 0.3000 -0.0048
## 160 0.4433 nan 0.3000 -0.0022
## 180 0.4127 nan 0.3000 -0.0027
## 200 0.3877 nan 0.3000 -0.0036
## 220 0.3653 nan 0.3000 -0.0035
## 240 0.3417 nan 0.3000 -0.0018
## 260 0.3230 nan 0.3000 -0.0009
## 280 0.3089 nan 0.3000 -0.0020
## 300 0.2885 nan 0.3000 -0.0033
## 320 0.2699 nan 0.3000 -0.0026
## 340 0.2559 nan 0.3000 -0.0018
## 360 0.2454 nan 0.3000 -0.0024
## 380 0.2309 nan 0.3000 -0.0010
## 400 0.2210 nan 0.3000 -0.0024
## 420 0.2098 nan 0.3000 -0.0015
## 440 0.1979 nan 0.3000 -0.0007
## 460 0.1855 nan 0.3000 -0.0005
## 480 0.1771 nan 0.3000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1692 nan 0.3000 0.0560
## 2 1.0893 nan 0.3000 0.0318
## 3 1.0356 nan 0.3000 0.0223
## 4 1.0030 nan 0.3000 0.0105
## 5 0.9720 nan 0.3000 -0.0012
## 6 0.9483 nan 0.3000 0.0056
## 7 0.9230 nan 0.3000 0.0047
## 8 0.9056 nan 0.3000 0.0042
## 9 0.8893 nan 0.3000 0.0013
## 10 0.8791 nan 0.3000 0.0008
## 20 0.7965 nan 0.3000 -0.0027
## 40 0.7196 nan 0.3000 -0.0020
## 60 0.6528 nan 0.3000 -0.0006
## 80 0.6005 nan 0.3000 -0.0021
## 100 0.5595 nan 0.3000 -0.0033
## 120 0.5360 nan 0.3000 -0.0014
## 140 0.5089 nan 0.3000 -0.0021
## 160 0.4684 nan 0.3000 -0.0004
## 180 0.4293 nan 0.3000 -0.0011
## 200 0.4154 nan 0.3000 -0.0055
## 220 0.3900 nan 0.3000 -0.0064
## 240 0.3660 nan 0.3000 -0.0023
## 260 0.3447 nan 0.3000 -0.0018
## 280 0.3185 nan 0.3000 -0.0026
## 300 0.2993 nan 0.3000 -0.0039
## 320 0.2773 nan 0.3000 -0.0041
## 340 0.2603 nan 0.3000 -0.0030
## 360 0.2439 nan 0.3000 -0.0014
## 380 0.2327 nan 0.3000 -0.0021
## 400 0.2183 nan 0.3000 -0.0020
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## 440 0.1989 nan 0.3000 -0.0017
## 460 0.1862 nan 0.3000 -0.0010
## 480 0.1758 nan 0.3000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1512 nan 0.3000 0.0562
## 2 1.0646 nan 0.3000 0.0375
## 3 1.0040 nan 0.3000 0.0211
## 4 0.9555 nan 0.3000 0.0200
## 5 0.9329 nan 0.3000 -0.0027
## 6 0.9129 nan 0.3000 0.0014
## 7 0.8895 nan 0.3000 0.0051
## 8 0.8727 nan 0.3000 -0.0022
## 9 0.8536 nan 0.3000 -0.0021
## 10 0.8412 nan 0.3000 -0.0022
## 20 0.7440 nan 0.3000 -0.0045
## 40 0.6321 nan 0.3000 -0.0087
## 60 0.5490 nan 0.3000 -0.0027
## 80 0.4845 nan 0.3000 -0.0007
## 100 0.4319 nan 0.3000 -0.0005
## 120 0.3833 nan 0.3000 -0.0044
## 140 0.3386 nan 0.3000 -0.0031
## 160 0.3049 nan 0.3000 -0.0021
## 180 18440999.8231 nan 0.3000 -0.0008
## 200 inf nan 0.3000 nan
## 220 inf nan 0.3000 nan
## 240 inf nan 0.3000 nan
## 260 inf nan 0.3000 nan
## 280 inf nan 0.3000 nan
## 300 inf nan 0.3000 nan
## 320 inf nan 0.3000 nan
## 340 inf nan 0.3000 -0.0006
## 360 inf nan 0.3000 nan
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## 400 inf nan 0.3000 nan
## 420 inf nan 0.3000 nan
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## 460 inf nan 0.3000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1470 nan 0.3000 0.0628
## 2 1.0531 nan 0.3000 0.0436
## 3 0.9951 nan 0.3000 0.0223
## 4 0.9598 nan 0.3000 0.0117
## 5 0.9289 nan 0.3000 0.0099
## 6 0.9072 nan 0.3000 0.0026
## 7 0.8788 nan 0.3000 0.0051
## 8 0.8541 nan 0.3000 0.0045
## 9 0.8390 nan 0.3000 0.0021
## 10 0.8291 nan 0.3000 -0.0051
## 20 0.7302 nan 0.3000 -0.0034
## 40 0.6247 nan 0.3000 -0.0076
## 60 0.5590 nan 0.3000 -0.0058
## 80 0.4830 nan 0.3000 -0.0010
## 100 0.4310 nan 0.3000 -0.0044
## 120 0.3718 nan 0.3000 -0.0010
## 140 0.3243 nan 0.3000 -0.0027
## 160 0.2799 nan 0.3000 -0.0067
## 180 0.2588 nan 0.3000 -0.0034
## 200 0.2260 nan 0.3000 -0.0018
## 220 0.2020 nan 0.3000 -0.0020
## 240 0.1865 nan 0.3000 -0.0009
## 260 0.1689 nan 0.3000 -0.0020
## 280 0.1563 nan 0.3000 -0.0020
## 300 0.1450 nan 0.3000 -0.0020
## 320 0.1290 nan 0.3000 -0.0012
## 340 0.1223 nan 0.3000 -0.0015
## 360 0.1087 nan 0.3000 -0.0004
## 380 0.1006 nan 0.3000 -0.0003
## 400 0.0922 nan 0.3000 -0.0010
## 420 0.0846 nan 0.3000 -0.0007
## 440 0.0770 nan 0.3000 -0.0002
## 460 0.0703 nan 0.3000 -0.0003
## 480 0.0647 nan 0.3000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1443 nan 0.3000 0.0584
## 2 1.0642 nan 0.3000 0.0380
## 3 1.0077 nan 0.3000 0.0252
## 4 0.9774 nan 0.3000 0.0068
## 5 0.9385 nan 0.3000 0.0061
## 6 0.9138 nan 0.3000 0.0010
## 7 0.8843 nan 0.3000 0.0060
## 8 0.8669 nan 0.3000 0.0001
## 9 0.8516 nan 0.3000 -0.0016
## 10 0.8387 nan 0.3000 0.0020
## 20 0.7643 nan 0.3000 -0.0055
## 40 0.6463 nan 0.3000 -0.0021
## 60 0.5617 nan 0.3000 -0.0017
## 80 0.4965 nan 0.3000 -0.0039
## 100 0.4524 nan 0.3000 -0.0038
## 120 0.3964 nan 0.3000 -0.0046
## 140 0.3492 nan 0.3000 -0.0022
## 160 0.3129 nan 0.3000 -0.0023
## 180 0.2729 nan 0.3000 -0.0004
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## 220 0.2253 nan 0.3000 -0.0010
## 240 0.2043 nan 0.3000 -0.0018
## 260 0.1804 nan 0.3000 -0.0012
## 280 0.1658 nan 0.3000 -0.0014
## 300 0.1480 nan 0.3000 -0.0012
## 320 0.1343 nan 0.3000 -0.0003
## 340 0.1265 nan 0.3000 -0.0010
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## 380 0.1074 nan 0.3000 -0.0008
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## 460 0.0754 nan 0.3000 -0.0009
## 480 0.0690 nan 0.3000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1381 nan 0.5000 0.0557
## 2 1.0834 nan 0.5000 0.0241
## 3 1.0282 nan 0.5000 0.0249
## 4 0.9849 nan 0.5000 0.0196
## 5 0.9659 nan 0.5000 0.0055
## 6 0.9502 nan 0.5000 -0.0001
## 7 0.9398 nan 0.5000 -0.0060
## 8 0.9233 nan 0.5000 0.0007
## 9 0.9172 nan 0.5000 -0.0034
## 10 0.9115 nan 0.5000 -0.0011
## 20 0.8443 nan 0.5000 -0.0064
## 40 0.7825 nan 0.5000 -0.0049
## 60 0.7473 nan 0.5000 -0.0016
## 80 0.7257 nan 0.5000 -0.0077
## 100 0.7021 nan 0.5000 -0.0103
## 120 0.6795 nan 0.5000 -0.0029
## 140 0.6677 nan 0.5000 -0.0012
## 160 0.6577 nan 0.5000 -0.0052
## 180 0.6403 nan 0.5000 -0.0085
## 200 0.6340 nan 0.5000 -0.0063
## 220 0.6229 nan 0.5000 -0.0054
## 240 0.6093 nan 0.5000 -0.0065
## 260 0.5927 nan 0.5000 -0.0036
## 280 0.5812 nan 0.5000 -0.0087
## 300 0.5721 nan 0.5000 -0.0054
## 320 0.5724 nan 0.5000 -0.0060
## 340 0.5651 nan 0.5000 -0.0058
## 360 0.5753 nan 0.5000 -0.0071
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1432 nan 0.5000 0.0691
## 2 1.0747 nan 0.5000 0.0254
## 3 1.0234 nan 0.5000 0.0153
## 4 0.9910 nan 0.5000 0.0127
## 5 0.9783 nan 0.5000 -0.0011
## 6 0.9627 nan 0.5000 0.0047
## 7 0.9540 nan 0.5000 -0.0011
## 8 0.9370 nan 0.5000 0.0049
## 9 0.9122 nan 0.5000 0.0078
## 10 0.8982 nan 0.5000 -0.0016
## 20 0.8277 nan 0.5000 0.0009
## 40 0.7788 nan 0.5000 -0.0043
## 60 0.7508 nan 0.5000 0.0008
## 80 0.7368 nan 0.5000 -0.0086
## 100 0.7130 nan 0.5000 -0.0059
## 120 0.6822 nan 0.5000 -0.0065
## 140 0.6783 nan 0.5000 -0.0060
## 160 0.6529 nan 0.5000 -0.0001
## 180 0.6388 nan 0.5000 -0.0051
## 200 0.6205 nan 0.5000 -0.0034
## 220 0.6112 nan 0.5000 -0.0052
## 240 0.6013 nan 0.5000 -0.0064
## 260 0.5946 nan 0.5000 -0.0047
## 280 0.5808 nan 0.5000 -0.0068
## 300 0.5750 nan 0.5000 -0.0060
## 320 0.5702 nan 0.5000 -0.0048
## 340 0.5549 nan 0.5000 -0.0073
## 360 0.5431 nan 0.5000 -0.0082
## 380 0.5288 nan 0.5000 -0.0054
## 400 0.5228 nan 0.5000 -0.0028
## 420 35.0230 nan 0.5000 -0.0062
## 440 35.0203 nan 0.5000 -0.0017
## 460 35.0113 nan 0.5000 0.0000
## 480 35.0078 nan 0.5000 -0.0016
## 500 34.9963 nan 0.5000 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1454 nan 0.5000 0.0619
## 2 1.0708 nan 0.5000 0.0175
## 3 1.0190 nan 0.5000 0.0206
## 4 0.9994 nan 0.5000 0.0024
## 5 0.9594 nan 0.5000 0.0145
## 6 0.9452 nan 0.5000 0.0039
## 7 0.9345 nan 0.5000 -0.0007
## 8 0.9264 nan 0.5000 0.0017
## 9 0.9090 nan 0.5000 0.0056
## 10 0.8964 nan 0.5000 0.0029
## 20 0.8568 nan 0.5000 -0.0251
## 40 0.8005 nan 0.5000 -0.0044
## 60 0.7573 nan 0.5000 0.0020
## 80 0.7207 nan 0.5000 -0.0043
## 100 0.7094 nan 0.5000 -0.0067
## 120 0.6798 nan 0.5000 -0.0055
## 140 0.6578 nan 0.5000 -0.0074
## 160 0.6476 nan 0.5000 -0.0028
## 180 0.6381 nan 0.5000 -0.0045
## 200 0.6279 nan 0.5000 -0.0008
## 220 0.6093 nan 0.5000 -0.0041
## 240 0.6003 nan 0.5000 -0.0009
## 260 0.5876 nan 0.5000 -0.0038
## 280 0.5848 nan 0.5000 -0.0077
## 300 0.5772 nan 0.5000 -0.0071
## 320 0.5690 nan 0.5000 -0.0076
## 340 0.5577 nan 0.5000 -0.0042
## 360 0.5590 nan 0.5000 -0.0046
## 380 0.5417 nan 0.5000 -0.0033
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## 420 0.5310 nan 0.5000 -0.0027
## 440 0.5200 nan 0.5000 -0.0060
## 460 0.5118 nan 0.5000 -0.0012
## 480 0.5044 nan 0.5000 -0.0048
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1067 nan 0.5000 0.0826
## 2 1.0124 nan 0.5000 0.0456
## 3 0.9616 nan 0.5000 0.0143
## 4 0.9357 nan 0.5000 0.0006
## 5 0.9220 nan 0.5000 -0.0026
## 6 0.9052 nan 0.5000 -0.0112
## 7 0.8745 nan 0.5000 0.0082
## 8 0.8741 nan 0.5000 -0.0168
## 9 0.8595 nan 0.5000 -0.0005
## 10 0.8527 nan 0.5000 -0.0069
## 20 0.7604 nan 0.5000 -0.0019
## 40 0.6525 nan 0.5000 -0.0132
## 60 3258755157287.3828 nan 0.5000 -0.0117
## 80 3258755157287.2988 nan 0.5000 -0.0033
## 100 3258755172485.5679 nan 0.5000 -0.0040
## 120 3258755172485.5469 nan 0.5000 -0.0074
## 140 3258755172485.5361 nan 0.5000 0.0036
## 160 3258755172485.5327 nan 0.5000 -0.0000
## 180 3258755172485.5244 nan 0.5000 -0.0004
## 200 3258755172485.5225 nan 0.5000 -0.0017
## 220 3258755172485.5215 nan 0.5000 0.0006
## 240 3258755172485.5190 nan 0.5000 -0.0017
## 260 3258755172485.5059 nan 0.5000 0.0001
## 280 3258755172485.5049 nan 0.5000 -0.0021
## 300 3258755172485.4941 nan 0.5000 -0.0031
## 320 3258755172485.4951 nan 0.5000 -0.0002
## 340 3258755172485.5000 nan 0.5000 -0.0047
## 360 3258755172485.4980 nan 0.5000 -0.0033
## 380 3258755172485.4971 nan 0.5000 -0.0000
## 400 3258755172485.4775 nan 0.5000 -0.0017
## 420 3258755172485.4731 nan 0.5000 -0.0006
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## 480 3258755172485.4629 nan 0.5000 -0.0029
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1339 nan 0.5000 0.0780
## 2 1.0322 nan 0.5000 0.0349
## 3 0.9860 nan 0.5000 0.0166
## 4 0.9712 nan 0.5000 -0.0112
## 5 0.9422 nan 0.5000 0.0046
## 6 0.9087 nan 0.5000 0.0132
## 7 0.8959 nan 0.5000 -0.0057
## 8 0.8799 nan 0.5000 -0.0026
## 9 0.8535 nan 0.5000 0.0017
## 10 0.8455 nan 0.5000 -0.0090
## 20 0.7926 nan 0.5000 -0.0048
## 40 inf nan 0.5000 nan
## 60 inf nan 0.5000 nan
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 -0.0034
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
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## 220 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1167 nan 0.5000 0.0815
## 2 1.0270 nan 0.5000 0.0373
## 3 0.9668 nan 0.5000 0.0238
## 4 0.9292 nan 0.5000 0.0058
## 5 0.9081 nan 0.5000 0.0011
## 6 0.8933 nan 0.5000 -0.0031
## 7 0.8789 nan 0.5000 0.0009
## 8 0.8643 nan 0.5000 -0.0023
## 9 0.8543 nan 0.5000 -0.0011
## 10 0.8534 nan 0.5000 -0.0130
## 20 0.7635 nan 0.5000 -0.0035
## 40 0.6672 nan 0.5000 -0.0011
## 60 0.5930 nan 0.5000 -0.0119
## 80 0.5317 nan 0.5000 -0.0077
## 100 0.4676 nan 0.5000 -0.0011
## 120 0.4262 nan 0.5000 -0.0067
## 140 0.3700 nan 0.5000 -0.0023
## 160 0.3498 nan 0.5000 -0.0065
## 180 0.3115 nan 0.5000 -0.0036
## 200 0.2989 nan 0.5000 -0.0102
## 220 0.2715 nan 0.5000 -0.0052
## 240 0.2397 nan 0.5000 -0.0057
## 260 0.2159 nan 0.5000 -0.0024
## 280 0.1920 nan 0.5000 -0.0010
## 300 0.1679 nan 0.5000 -0.0014
## 320 0.1513 nan 0.5000 -0.0026
## 340 0.1362 nan 0.5000 -0.0023
## 360 0.1267 nan 0.5000 -0.0016
## 380 0.1190 nan 0.5000 -0.0007
## 400 0.1125 nan 0.5000 -0.0017
## 420 0.1018 nan 0.5000 -0.0011
## 440 0.0950 nan 0.5000 -0.0006
## 460 0.0859 nan 0.5000 0.0000
## 480 0.0788 nan 0.5000 -0.0003
## 500 0.0742 nan 0.5000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1008 nan 0.5000 0.0847
## 2 1.0128 nan 0.5000 0.0393
## 3 0.9589 nan 0.5000 0.0221
## 4 0.9412 nan 0.5000 -0.0094
## 5 0.9113 nan 0.5000 -0.0050
## 6 0.8902 nan 0.5000 -0.0088
## 7 0.8749 nan 0.5000 -0.0034
## 8 0.8463 nan 0.5000 0.0026
## 9 0.8293 nan 0.5000 -0.0067
## 10 0.8144 nan 0.5000 -0.0038
## 20 0.6973 nan 0.5000 0.0070
## 40 inf nan 0.5000 nan
## 60 inf nan 0.5000 nan
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
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## 180 inf nan 0.5000 nan
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## 220 inf nan 0.5000 nan
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## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0894 nan 0.5000 0.1006
## 2 0.9994 nan 0.5000 0.0402
## 3 0.9547 nan 0.5000 0.0108
## 4 0.9140 nan 0.5000 0.0026
## 5 0.8704 nan 0.5000 0.0136
## 6 0.8538 nan 0.5000 -0.0064
## 7 0.8408 nan 0.5000 -0.0031
## 8 0.8185 nan 0.5000 0.0020
## 9 0.8038 nan 0.5000 -0.0045
## 10 0.7876 nan 0.5000 0.0024
## 20 0.7093 nan 0.5000 -0.0156
## 40 0.6109 nan 0.5000 -0.0318
## 60 inf nan 0.5000 -inf
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0741 nan 0.5000 0.0830
## 2 0.9673 nan 0.5000 0.0309
## 3 0.9210 nan 0.5000 0.0094
## 4 0.8860 nan 0.5000 -0.0014
## 5 0.8597 nan 0.5000 -0.0061
## 6 0.8339 nan 0.5000 0.0016
## 7 0.8140 nan 0.5000 -0.0045
## 8 0.7953 nan 0.5000 -0.0069
## 9 0.7893 nan 0.5000 -0.0144
## 10 0.7762 nan 0.5000 -0.0097
## 20 0.6945 nan 0.5000 -0.0209
## 40 0.5814 nan 0.5000 -0.0084
## 60 0.4997 nan 0.5000 -0.0112
## 80 0.4048 nan 0.5000 -0.0039
## 100 0.3415 nan 0.5000 -0.0074
## 120 0.2707 nan 0.5000 -0.0022
## 140 0.2280 nan 0.5000 -0.0012
## 160 0.1891 nan 0.5000 -0.0018
## 180 0.1574 nan 0.5000 -0.0014
## 200 0.1301 nan 0.5000 0.0001
## 220 0.1125 nan 0.5000 -0.0016
## 240 0.0980 nan 0.5000 -0.0023
## 260 0.0851 nan 0.5000 -0.0012
## 280 0.0756 nan 0.5000 -0.0005
## 300 0.0645 nan 0.5000 -0.0008
## 320 0.0581 nan 0.5000 -0.0009
## 340 0.0523 nan 0.5000 -0.0003
## 360 0.0459 nan 0.5000 -0.0008
## 380 0.0394 nan 0.5000 -0.0005
## 400 0.0362 nan 0.5000 -0.0007
## 420 0.0316 nan 0.5000 -0.0002
## 440 0.0280 nan 0.5000 -0.0010
## 460 0.0242 nan 0.5000 -0.0005
## 480 0.0218 nan 0.5000 -0.0003
## 500 0.0203 nan 0.5000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1159 nan 1.0000 0.0756
## 2 1.0469 nan 1.0000 0.0225
## 3 0.9903 nan 1.0000 0.0237
## 4 0.9406 nan 1.0000 0.0242
## 5 0.9317 nan 1.0000 -0.0127
## 6 0.9155 nan 1.0000 0.0019
## 7 0.9086 nan 1.0000 -0.0049
## 8 0.8997 nan 1.0000 -0.0086
## 9 0.8917 nan 1.0000 -0.0034
## 10 0.8909 nan 1.0000 -0.0104
## 20 2101.9198 nan 1.0000 -2098.0314
## 40 2101.8575 nan 1.0000 -0.0076
## 60 2103.0130 nan 1.0000 -0.0005
## 80 2102.9515 nan 1.0000 -0.0255
## 100 2102.9348 nan 1.0000 -0.0018
## 120 7177454.2512 nan 1.0000 0.0000
## 140 7177454.2578 nan 1.0000 -0.0017
## 160 7177454.2440 nan 1.0000 0.0002
## 180 3356926643719341586884604862.0000 nan 1.0000 -3352075593656163168868428206.0000
## 200 3356926643719341586884604862.0000 nan 1.0000 -0.0003
## 220 3356926643719341586884604862.0000 nan 1.0000 -0.0001
## 240 3356926643719341586884604862.0000 nan 1.0000 0.0004
## 260 3356926643719341586884604862.0000 nan 1.0000 -0.0014
## 280 3356926643719341586884604862.0000 nan 1.0000 -0.0038
## 300 3356926643719341586884604862.0000 nan 1.0000 -0.0038
## 320 3356926643719341586884604862.0000 nan 1.0000 -0.0558
## 340 3356926643719341586884604862.0000 nan 1.0000 0.0003
## 360 3356926643719341586884604862.0000 nan 1.0000 -0.0035
## 380 3356926643719341586884604862.0000 nan 1.0000 -0.0029
## 400 3356926643719341586884604862.0000 nan 1.0000 -0.0017
## 420 3356926643719341586884604862.0000 nan 1.0000 -0.0041
## 440 3356926643719341586884604862.0000 nan 1.0000 0.0003
## 460 3356926643719341586884604862.0000 nan 1.0000 -0.0021
## 480 3356926643719341586884604862.0000 nan 1.0000 0.0037
## 500 3356926643719341586884604862.0000 nan 1.0000 -0.0021
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1167 nan 1.0000 0.0791
## 2 1.0536 nan 1.0000 0.0162
## 3 0.9981 nan 1.0000 0.0165
## 4 0.9559 nan 1.0000 0.0209
## 5 0.9492 nan 1.0000 -0.0194
## 6 0.9573 nan 1.0000 -0.0287
## 7 0.9560 nan 1.0000 -0.0194
## 8 0.9696 nan 1.0000 -0.0422
## 9 0.9507 nan 1.0000 -0.0014
## 10 0.9603 nan 1.0000 -0.0399
## 20 0.9112 nan 1.0000 -0.0346
## 40 1.4919 nan 1.0000 0.0074
## 60 1.4667 nan 1.0000 -0.0266
## 80 1.5041 nan 1.0000 -0.0179
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## 360 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1110 nan 1.0000 0.0726
## 2 1.0440 nan 1.0000 0.0194
## 3 1.0140 nan 1.0000 -0.0046
## 4 0.9556 nan 1.0000 0.0169
## 5 0.9491 nan 1.0000 -0.0146
## 6 0.9504 nan 1.0000 -0.0180
## 7 0.9172 nan 1.0000 0.0167
## 8 0.9167 nan 1.0000 -0.0150
## 9 0.9105 nan 1.0000 -0.0105
## 10 0.9030 nan 1.0000 -0.0025
## 20 0.9197 nan 1.0000 -0.0470
## 40 0.8185 nan 1.0000 -0.0306
## 60 0.8111 nan 1.0000 -0.0093
## 80 0.7839 nan 1.0000 -0.0183
## 100 0.8366 nan 1.0000 -0.0352
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0577 nan 1.0000 0.0828
## 2 0.9858 nan 1.0000 0.0178
## 3 0.9451 nan 1.0000 0.0080
## 4 0.9328 nan 1.0000 -0.0139
## 5 0.9435 nan 1.0000 -0.0393
## 6 0.9202 nan 1.0000 -0.0071
## 7 0.9047 nan 1.0000 -0.0063
## 8 0.8843 nan 1.0000 -0.0171
## 9 0.8883 nan 1.0000 -0.0331
## 10 0.9291 nan 1.0000 -0.0857
## 20 0.9084 nan 1.0000 -0.0358
## 40 0.8070 nan 1.0000 -0.0162
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 0.0000
## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0512 nan 1.0000 0.0883
## 2 0.9844 nan 1.0000 0.0128
## 3 0.9731 nan 1.0000 -0.0327
## 4 0.9483 nan 1.0000 -0.0140
## 5 0.9356 nan 1.0000 -0.0272
## 6 0.9155 nan 1.0000 -0.0168
## 7 0.8799 nan 1.0000 0.0083
## 8 0.8878 nan 1.0000 -0.0423
## 9 0.8766 nan 1.0000 -0.0066
## 10 0.8629 nan 1.0000 -0.0143
## 20 0.9221 nan 1.0000 -0.0393
## 40 0.9332 nan 1.0000 -0.0781
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0579 nan 1.0000 0.1052
## 2 0.9751 nan 1.0000 0.0312
## 3 0.9490 nan 1.0000 -0.0102
## 4 0.9466 nan 1.0000 -0.0368
## 5 0.9360 nan 1.0000 -0.0340
## 6 0.9540 nan 1.0000 -0.0557
## 7 0.9075 nan 1.0000 0.0047
## 8 0.9053 nan 1.0000 -0.0460
## 9 0.8918 nan 1.0000 -0.0096
## 10 0.9113 nan 1.0000 -0.0539
## 20 2.2372 nan 1.0000 -0.0153
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 -0.0555
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
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## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0481 nan 1.0000 0.0802
## 2 0.9871 nan 1.0000 -0.0170
## 3 0.9653 nan 1.0000 -0.0241
## 4 0.9266 nan 1.0000 -0.0060
## 5 0.8726 nan 1.0000 -0.0164
## 6 0.9266 nan 1.0000 -0.1141
## 7 0.8383 nan 1.0000 0.0204
## 8 0.8226 nan 1.0000 -0.0228
## 9 0.9830 nan 1.0000 -0.1941
## 10 1.0042 nan 1.0000 -0.0585
## 20 1.3105 nan 1.0000 -0.1265
## 40 2.2336 nan 1.0000 -0.0402
## 60 156595565351557801078280404822288682422642208844686806222468262440282804600666042282846042062824446480848648242602422800820468000244608440064408428288488246646026246080080426626282022402868020880866628060684282466866248260404628.0000 nan 1.0000 inf
## 80 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0344 nan 1.0000 0.1226
## 2 0.9879 nan 1.0000 -0.0199
## 3 0.9722 nan 1.0000 -0.0209
## 4 0.9815 nan 1.0000 -0.0550
## 5 0.9826 nan 1.0000 -0.0360
## 6 0.9605 nan 1.0000 -0.0138
## 7 0.9535 nan 1.0000 -0.0442
## 8 0.9027 nan 1.0000 -0.0082
## 9 0.8845 nan 1.0000 -0.0149
## 10 0.8866 nan 1.0000 -0.0445
## 20 0.7629 nan 1.0000 -0.0667
## 40 0.5829 nan 1.0000 -0.0488
## 60 12624308.1751 nan 1.0000 -0.0228
## 80 12624316.1764 nan 1.0000 -0.0339
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0423 nan 1.0000 0.1088
## 2 0.9598 nan 1.0000 0.0227
## 3 0.9244 nan 1.0000 -0.0180
## 4 0.9104 nan 1.0000 -0.0226
## 5 0.9100 nan 1.0000 -0.0322
## 6 0.8944 nan 1.0000 -0.0258
## 7 0.8875 nan 1.0000 -0.0420
## 8 0.8936 nan 1.0000 -0.0539
## 9 0.8811 nan 1.0000 -0.0255
## 10 0.8777 nan 1.0000 -0.0422
## 20 16.4256 nan 1.0000 -0.0707
## 40 94453736.8072 nan 1.0000 -73357843.1509
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
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## 280 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
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## 240 1.2934 nan 0.0000 0.0000
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## 280 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
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## 3 1.2923 nan 0.0010 0.0001
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## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2909 nan 0.0010 0.0001
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## 10 1.2897 nan 0.0010 0.0001
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2793 nan 0.0010 0.0002
## 60 1.2727 nan 0.0010 0.0002
## 80 1.2661 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
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## 3 1.2924 nan 0.0010 0.0002
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## 5 1.2917 nan 0.0010 0.0002
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## 10 1.2899 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
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## 3 1.2922 nan 0.0010 0.0002
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## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
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## 40 1.2790 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
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## 6 1.2906 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## 3 1.2919 nan 0.0010 0.0002
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## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## 3 1.2918 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
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## 3 1.2918 nan 0.0010 0.0002
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## 60 1.2628 nan 0.0010 0.0002
## 80 1.2531 nan 0.0010 0.0002
## 100 1.2441 nan 0.0010 0.0002
## 120 1.2350 nan 0.0010 0.0002
## 140 1.2265 nan 0.0010 0.0002
## 160 1.2181 nan 0.0010 0.0002
## 180 1.2100 nan 0.0010 0.0002
## 200 1.2021 nan 0.0010 0.0001
## 220 1.1944 nan 0.0010 0.0001
## 240 1.1868 nan 0.0010 0.0001
## 260 1.1794 nan 0.0010 0.0002
## 280 1.1725 nan 0.0010 0.0002
## 300 1.1654 nan 0.0010 0.0001
## 320 1.1586 nan 0.0010 0.0002
## 340 1.1522 nan 0.0010 0.0001
## 360 1.1460 nan 0.0010 0.0001
## 380 1.1397 nan 0.0010 0.0001
## 400 1.1336 nan 0.0010 0.0001
## 420 1.1277 nan 0.0010 0.0001
## 440 1.1221 nan 0.0010 0.0001
## 460 1.1163 nan 0.0010 0.0001
## 480 1.1108 nan 0.0010 0.0001
## 500 1.1057 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2564 nan 0.1000 0.0154
## 2 1.2284 nan 0.1000 0.0129
## 3 1.2055 nan 0.1000 0.0110
## 4 1.1851 nan 0.1000 0.0086
## 5 1.1700 nan 0.1000 0.0058
## 6 1.1527 nan 0.1000 0.0071
## 7 1.1368 nan 0.1000 0.0066
## 8 1.1220 nan 0.1000 0.0063
## 9 1.1080 nan 0.1000 0.0050
## 10 1.0961 nan 0.1000 0.0037
## 20 1.0020 nan 0.1000 0.0016
## 40 0.9276 nan 0.1000 -0.0006
## 60 0.8885 nan 0.1000 0.0001
## 80 0.8580 nan 0.1000 -0.0002
## 100 0.8413 nan 0.1000 -0.0010
## 120 0.8265 nan 0.1000 -0.0015
## 140 0.8137 nan 0.1000 -0.0014
## 160 0.8040 nan 0.1000 -0.0004
## 180 0.7981 nan 0.1000 -0.0009
## 200 0.7914 nan 0.1000 -0.0008
## 220 0.7830 nan 0.1000 -0.0007
## 240 0.7754 nan 0.1000 -0.0009
## 260 0.7693 nan 0.1000 -0.0016
## 280 0.7616 nan 0.1000 -0.0008
## 300 0.7536 nan 0.1000 -0.0003
## 320 0.7477 nan 0.1000 -0.0008
## 340 0.7419 nan 0.1000 -0.0005
## 360 0.7369 nan 0.1000 -0.0004
## 380 0.7313 nan 0.1000 -0.0007
## 400 0.7267 nan 0.1000 -0.0006
## 420 0.7218 nan 0.1000 -0.0009
## 440 0.7192 nan 0.1000 -0.0009
## 460 0.7144 nan 0.1000 -0.0009
## 480 0.7101 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2571 nan 0.1000 0.0170
## 2 1.2309 nan 0.1000 0.0135
## 3 1.2018 nan 0.1000 0.0113
## 4 1.1816 nan 0.1000 0.0094
## 5 1.1652 nan 0.1000 0.0070
## 6 1.1507 nan 0.1000 0.0060
## 7 1.1328 nan 0.1000 0.0073
## 8 1.1200 nan 0.1000 0.0057
## 9 1.1065 nan 0.1000 0.0055
## 10 1.0947 nan 0.1000 0.0046
## 20 1.0080 nan 0.1000 0.0026
## 40 0.9268 nan 0.1000 0.0003
## 60 0.8862 nan 0.1000 -0.0007
## 80 0.8645 nan 0.1000 -0.0004
## 100 0.8448 nan 0.1000 -0.0005
## 120 0.8301 nan 0.1000 -0.0005
## 140 0.8204 nan 0.1000 -0.0014
## 160 0.8103 nan 0.1000 -0.0005
## 180 0.8005 nan 0.1000 -0.0025
## 200 0.7937 nan 0.1000 -0.0013
## 220 0.7846 nan 0.1000 -0.0015
## 240 0.7782 nan 0.1000 -0.0016
## 260 0.7714 nan 0.1000 -0.0009
## 280 0.7625 nan 0.1000 -0.0007
## 300 0.7570 nan 0.1000 -0.0009
## 320 0.7511 nan 0.1000 -0.0012
## 340 0.7461 nan 0.1000 -0.0010
## 360 0.7409 nan 0.1000 -0.0006
## 380 0.7364 nan 0.1000 -0.0004
## 400 0.7326 nan 0.1000 -0.0007
## 420 0.7258 nan 0.1000 -0.0006
## 440 0.7218 nan 0.1000 -0.0008
## 460 0.7197 nan 0.1000 -0.0006
## 480 0.7155 nan 0.1000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2580 nan 0.1000 0.0155
## 2 1.2276 nan 0.1000 0.0139
## 3 1.2006 nan 0.1000 0.0120
## 4 1.1796 nan 0.1000 0.0087
## 5 1.1664 nan 0.1000 0.0057
## 6 1.1510 nan 0.1000 0.0066
## 7 1.1358 nan 0.1000 0.0057
## 8 1.1182 nan 0.1000 0.0065
## 9 1.1037 nan 0.1000 0.0064
## 10 1.0899 nan 0.1000 0.0052
## 20 1.0087 nan 0.1000 0.0000
## 40 0.9251 nan 0.1000 0.0008
## 60 0.8862 nan 0.1000 -0.0005
## 80 0.8578 nan 0.1000 -0.0000
## 100 0.8389 nan 0.1000 -0.0000
## 120 0.8233 nan 0.1000 -0.0002
## 140 0.8130 nan 0.1000 -0.0016
## 160 0.8021 nan 0.1000 0.0001
## 180 0.7923 nan 0.1000 -0.0005
## 200 0.7827 nan 0.1000 -0.0014
## 220 0.7781 nan 0.1000 -0.0014
## 240 0.7714 nan 0.1000 -0.0012
## 260 0.7653 nan 0.1000 -0.0010
## 280 0.7596 nan 0.1000 -0.0006
## 300 0.7544 nan 0.1000 -0.0008
## 320 0.7479 nan 0.1000 -0.0005
## 340 0.7424 nan 0.1000 -0.0014
## 360 0.7383 nan 0.1000 -0.0014
## 380 0.7350 nan 0.1000 -0.0014
## 400 0.7283 nan 0.1000 -0.0005
## 420 0.7233 nan 0.1000 -0.0011
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## 460 0.7149 nan 0.1000 -0.0009
## 480 0.7103 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2516 nan 0.1000 0.0206
## 2 1.2084 nan 0.1000 0.0171
## 3 1.1741 nan 0.1000 0.0142
## 4 1.1445 nan 0.1000 0.0140
## 5 1.1188 nan 0.1000 0.0112
## 6 1.0966 nan 0.1000 0.0088
## 7 1.0786 nan 0.1000 0.0058
## 8 1.0620 nan 0.1000 0.0063
## 9 1.0493 nan 0.1000 0.0053
## 10 1.0375 nan 0.1000 0.0049
## 20 0.9395 nan 0.1000 0.0016
## 40 0.8595 nan 0.1000 -0.0006
## 60 0.8117 nan 0.1000 -0.0007
## 80 0.7807 nan 0.1000 -0.0020
## 100 0.7546 nan 0.1000 -0.0007
## 120 0.7299 nan 0.1000 -0.0026
## 140 0.7054 nan 0.1000 -0.0006
## 160 0.6836 nan 0.1000 -0.0019
## 180 0.6631 nan 0.1000 -0.0014
## 200 0.6389 nan 0.1000 -0.0007
## 220 0.6245 nan 0.1000 -0.0003
## 240 0.6055 nan 0.1000 -0.0009
## 260 0.5897 nan 0.1000 -0.0008
## 280 0.5745 nan 0.1000 -0.0015
## 300 0.5596 nan 0.1000 -0.0016
## 320 0.5459 nan 0.1000 -0.0009
## 340 0.5314 nan 0.1000 -0.0015
## 360 0.5193 nan 0.1000 -0.0015
## 380 0.5077 nan 0.1000 -0.0013
## 400 0.4951 nan 0.1000 -0.0001
## 420 0.4870 nan 0.1000 -0.0011
## 440 0.4756 nan 0.1000 -0.0010
## 460 0.4657 nan 0.1000 -0.0010
## 480 0.4568 nan 0.1000 -0.0002
## 500 0.4454 nan 0.1000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2446 nan 0.1000 0.0199
## 2 1.2109 nan 0.1000 0.0173
## 3 1.1748 nan 0.1000 0.0118
## 4 1.1483 nan 0.1000 0.0113
## 5 1.1219 nan 0.1000 0.0117
## 6 1.1052 nan 0.1000 0.0068
## 7 1.0865 nan 0.1000 0.0075
## 8 1.0685 nan 0.1000 0.0085
## 9 1.0522 nan 0.1000 0.0033
## 10 1.0372 nan 0.1000 0.0077
## 20 0.9408 nan 0.1000 0.0014
## 40 0.8600 nan 0.1000 -0.0000
## 60 0.8066 nan 0.1000 -0.0001
## 80 0.7728 nan 0.1000 -0.0009
## 100 0.7403 nan 0.1000 -0.0011
## 120 0.7164 nan 0.1000 -0.0016
## 140 0.6927 nan 0.1000 -0.0005
## 160 0.6753 nan 0.1000 -0.0004
## 180 0.6584 nan 0.1000 -0.0008
## 200 0.6405 nan 0.1000 -0.0010
## 220 0.6240 nan 0.1000 -0.0014
## 240 0.6076 nan 0.1000 -0.0009
## 260 0.5918 nan 0.1000 -0.0006
## 280 0.5787 nan 0.1000 0.0001
## 300 0.5634 nan 0.1000 -0.0008
## 320 0.5533 nan 0.1000 -0.0015
## 340 0.5365 nan 0.1000 -0.0008
## 360 0.5236 nan 0.1000 -0.0010
## 380 0.5110 nan 0.1000 -0.0011
## 400 0.4985 nan 0.1000 -0.0014
## 420 0.4867 nan 0.1000 -0.0011
## 440 0.4752 nan 0.1000 -0.0009
## 460 0.4612 nan 0.1000 -0.0011
## 480 0.4506 nan 0.1000 -0.0002
## 500 0.4416 nan 0.1000 -0.0010
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2517 nan 0.1000 0.0191
## 2 1.2115 nan 0.1000 0.0153
## 3 1.1793 nan 0.1000 0.0107
## 4 1.1500 nan 0.1000 0.0108
## 5 1.1282 nan 0.1000 0.0103
## 6 1.1070 nan 0.1000 0.0095
## 7 1.0831 nan 0.1000 0.0070
## 8 1.0632 nan 0.1000 0.0080
## 9 1.0483 nan 0.1000 0.0063
## 10 1.0348 nan 0.1000 0.0038
## 20 0.9343 nan 0.1000 0.0021
## 40 0.8594 nan 0.1000 0.0009
## 60 0.8098 nan 0.1000 -0.0008
## 80 0.7704 nan 0.1000 -0.0013
## 100 0.7483 nan 0.1000 -0.0011
## 120 0.7245 nan 0.1000 -0.0009
## 140 0.7034 nan 0.1000 -0.0015
## 160 0.6852 nan 0.1000 -0.0010
## 180 0.6657 nan 0.1000 -0.0000
## 200 0.6467 nan 0.1000 -0.0006
## 220 0.6300 nan 0.1000 -0.0010
## 240 0.6127 nan 0.1000 -0.0019
## 260 0.5962 nan 0.1000 -0.0006
## 280 0.5854 nan 0.1000 -0.0013
## 300 0.5739 nan 0.1000 -0.0010
## 320 0.5613 nan 0.1000 -0.0001
## 340 0.5510 nan 0.1000 -0.0013
## 360 0.5384 nan 0.1000 -0.0006
## 380 0.5256 nan 0.1000 -0.0006
## 400 0.5133 nan 0.1000 -0.0008
## 420 0.5030 nan 0.1000 -0.0001
## 440 0.4925 nan 0.1000 -0.0010
## 460 0.4802 nan 0.1000 -0.0004
## 480 0.4700 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2382 nan 0.1000 0.0216
## 2 1.1986 nan 0.1000 0.0145
## 3 1.1690 nan 0.1000 0.0122
## 4 1.1344 nan 0.1000 0.0130
## 5 1.1008 nan 0.1000 0.0145
## 6 1.0768 nan 0.1000 0.0077
## 7 1.0546 nan 0.1000 0.0081
## 8 1.0346 nan 0.1000 0.0049
## 9 1.0153 nan 0.1000 0.0062
## 10 0.9993 nan 0.1000 0.0051
## 20 0.8986 nan 0.1000 0.0012
## 40 0.8063 nan 0.1000 0.0000
## 60 0.7508 nan 0.1000 -0.0023
## 80 0.7052 nan 0.1000 -0.0019
## 100 0.6682 nan 0.1000 -0.0019
## 120 0.6307 nan 0.1000 -0.0012
## 140 0.6007 nan 0.1000 -0.0014
## 160 0.5742 nan 0.1000 -0.0005
## 180 0.5506 nan 0.1000 -0.0018
## 200 0.5283 nan 0.1000 -0.0011
## 220 0.5086 nan 0.1000 -0.0013
## 240 0.4876 nan 0.1000 -0.0017
## 260 0.4629 nan 0.1000 -0.0015
## 280 0.4477 nan 0.1000 -0.0011
## 300 0.4271 nan 0.1000 -0.0010
## 320 0.4113 nan 0.1000 -0.0013
## 340 0.3960 nan 0.1000 -0.0008
## 360 0.3835 nan 0.1000 -0.0019
## 380 0.3715 nan 0.1000 -0.0006
## 400 0.3585 nan 0.1000 -0.0009
## 420 0.3461 nan 0.1000 -0.0011
## 440 0.3310 nan 0.1000 -0.0008
## 460 0.3187 nan 0.1000 -0.0005
## 480 0.3088 nan 0.1000 -0.0002
## 500 0.2990 nan 0.1000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2504 nan 0.1000 0.0192
## 2 1.2045 nan 0.1000 0.0186
## 3 1.1602 nan 0.1000 0.0197
## 4 1.1324 nan 0.1000 0.0098
## 5 1.1055 nan 0.1000 0.0078
## 6 1.0796 nan 0.1000 0.0094
## 7 1.0555 nan 0.1000 0.0091
## 8 1.0409 nan 0.1000 0.0044
## 9 1.0227 nan 0.1000 0.0065
## 10 1.0049 nan 0.1000 0.0068
## 20 0.9040 nan 0.1000 0.0009
## 40 0.8107 nan 0.1000 -0.0021
## 60 0.7542 nan 0.1000 -0.0008
## 80 0.7104 nan 0.1000 -0.0016
## 100 0.6682 nan 0.1000 0.0004
## 120 0.6346 nan 0.1000 -0.0003
## 140 0.6053 nan 0.1000 -0.0020
## 160 0.5761 nan 0.1000 -0.0005
## 180 0.5468 nan 0.1000 -0.0023
## 200 0.5254 nan 0.1000 -0.0013
## 220 0.4977 nan 0.1000 -0.0010
## 240 0.4781 nan 0.1000 -0.0007
## 260 0.4577 nan 0.1000 -0.0008
## 280 0.4403 nan 0.1000 -0.0015
## 300 0.4219 nan 0.1000 -0.0013
## 320 0.4052 nan 0.1000 -0.0011
## 340 0.3887 nan 0.1000 -0.0011
## 360 0.3736 nan 0.1000 -0.0006
## 380 0.3609 nan 0.1000 -0.0010
## 400 0.3445 nan 0.1000 -0.0016
## 420 0.3304 nan 0.1000 -0.0012
## 440 0.3186 nan 0.1000 -0.0014
## 460 0.3052 nan 0.1000 -0.0003
## 480 0.2932 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2455 nan 0.1000 0.0229
## 2 1.1976 nan 0.1000 0.0205
## 3 1.1619 nan 0.1000 0.0148
## 4 1.1297 nan 0.1000 0.0123
## 5 1.0998 nan 0.1000 0.0103
## 6 1.0775 nan 0.1000 0.0066
## 7 1.0591 nan 0.1000 0.0076
## 8 1.0350 nan 0.1000 0.0070
## 9 1.0189 nan 0.1000 0.0046
## 10 1.0033 nan 0.1000 0.0056
## 20 0.9026 nan 0.1000 0.0007
## 40 0.8093 nan 0.1000 -0.0016
## 60 0.7516 nan 0.1000 0.0011
## 80 0.7037 nan 0.1000 -0.0009
## 100 0.6703 nan 0.1000 -0.0012
## 120 0.6417 nan 0.1000 -0.0013
## 140 0.6111 nan 0.1000 -0.0009
## 160 0.5830 nan 0.1000 -0.0010
## 180 0.5559 nan 0.1000 -0.0013
## 200 0.5321 nan 0.1000 -0.0007
## 220 0.5063 nan 0.1000 -0.0019
## 240 0.4852 nan 0.1000 -0.0009
## 260 0.4680 nan 0.1000 -0.0019
## 280 0.4481 nan 0.1000 -0.0004
## 300 0.4289 nan 0.1000 -0.0004
## 320 0.4118 nan 0.1000 -0.0017
## 340 0.3941 nan 0.1000 -0.0002
## 360 0.3794 nan 0.1000 -0.0003
## 380 0.3666 nan 0.1000 -0.0010
## 400 0.3530 nan 0.1000 -0.0009
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## 460 0.3169 nan 0.1000 -0.0009
## 480 0.3031 nan 0.1000 -0.0004
## 500 0.2915 nan 0.1000 -0.0018
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2270 nan 0.2000 0.0319
## 2 1.1848 nan 0.2000 0.0209
## 3 1.1528 nan 0.2000 0.0131
## 4 1.1274 nan 0.2000 0.0101
## 5 1.1056 nan 0.2000 0.0103
## 6 1.0839 nan 0.2000 0.0060
## 7 1.0594 nan 0.2000 0.0112
## 8 1.0449 nan 0.2000 0.0046
## 9 1.0286 nan 0.2000 0.0068
## 10 1.0189 nan 0.2000 0.0030
## 20 0.9301 nan 0.2000 0.0011
## 40 0.8658 nan 0.2000 -0.0039
## 60 0.8345 nan 0.2000 -0.0031
## 80 0.8094 nan 0.2000 -0.0032
## 100 0.7934 nan 0.2000 -0.0010
## 120 0.7805 nan 0.2000 -0.0003
## 140 0.7659 nan 0.2000 -0.0021
## 160 0.7542 nan 0.2000 -0.0023
## 180 0.7456 nan 0.2000 -0.0027
## 200 0.7315 nan 0.2000 -0.0014
## 220 0.7242 nan 0.2000 -0.0012
## 240 0.7128 nan 0.2000 -0.0018
## 260 0.7091 nan 0.2000 -0.0024
## 280 0.6992 nan 0.2000 0.0005
## 300 0.6906 nan 0.2000 -0.0020
## 320 0.6861 nan 0.2000 -0.0048
## 340 0.6798 nan 0.2000 -0.0009
## 360 0.6719 nan 0.2000 -0.0016
## 380 0.6665 nan 0.2000 -0.0007
## 400 0.6634 nan 0.2000 -0.0011
## 420 0.6553 nan 0.2000 -0.0027
## 440 0.6508 nan 0.2000 -0.0041
## 460 0.6455 nan 0.2000 -0.0036
## 480 0.6407 nan 0.2000 -0.0019
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2231 nan 0.2000 0.0262
## 2 1.1769 nan 0.2000 0.0193
## 3 1.1477 nan 0.2000 0.0119
## 4 1.1171 nan 0.2000 0.0119
## 5 1.0984 nan 0.2000 0.0076
## 6 1.0759 nan 0.2000 0.0091
## 7 1.0521 nan 0.2000 0.0074
## 8 1.0370 nan 0.2000 0.0037
## 9 1.0212 nan 0.2000 0.0033
## 10 1.0089 nan 0.2000 0.0040
## 20 0.9371 nan 0.2000 -0.0007
## 40 0.8697 nan 0.2000 0.0002
## 60 0.8263 nan 0.2000 -0.0024
## 80 0.8056 nan 0.2000 0.0001
## 100 0.7951 nan 0.2000 -0.0025
## 120 0.7795 nan 0.2000 -0.0013
## 140 0.7674 nan 0.2000 -0.0012
## 160 0.7532 nan 0.2000 -0.0021
## 180 0.7443 nan 0.2000 -0.0015
## 200 0.7329 nan 0.2000 -0.0013
## 220 0.7228 nan 0.2000 -0.0001
## 240 0.7123 nan 0.2000 -0.0007
## 260 0.7056 nan 0.2000 -0.0016
## 280 0.6964 nan 0.2000 -0.0006
## 300 0.6877 nan 0.2000 -0.0001
## 320 0.6843 nan 0.2000 -0.0019
## 340 0.6739 nan 0.2000 -0.0035
## 360 0.6722 nan 0.2000 -0.0020
## 380 0.6646 nan 0.2000 -0.0025
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## 460 0.6405 nan 0.2000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2208 nan 0.2000 0.0295
## 2 1.1797 nan 0.2000 0.0196
## 3 1.1475 nan 0.2000 0.0149
## 4 1.1182 nan 0.2000 0.0125
## 5 1.0966 nan 0.2000 0.0076
## 6 1.0672 nan 0.2000 0.0100
## 7 1.0485 nan 0.2000 0.0087
## 8 1.0314 nan 0.2000 0.0071
## 9 1.0199 nan 0.2000 0.0049
## 10 1.0103 nan 0.2000 0.0014
## 20 0.9308 nan 0.2000 -0.0003
## 40 0.8650 nan 0.2000 -0.0018
## 60 0.8326 nan 0.2000 -0.0034
## 80 0.8125 nan 0.2000 -0.0006
## 100 0.7908 nan 0.2000 -0.0011
## 120 0.7752 nan 0.2000 -0.0017
## 140 0.7629 nan 0.2000 -0.0015
## 160 0.7525 nan 0.2000 -0.0020
## 180 0.7439 nan 0.2000 -0.0017
## 200 0.7371 nan 0.2000 -0.0005
## 220 0.7293 nan 0.2000 -0.0036
## 240 0.7184 nan 0.2000 -0.0030
## 260 0.7103 nan 0.2000 -0.0011
## 280 0.7027 nan 0.2000 -0.0019
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## 460 0.6432 nan 0.2000 -0.0027
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2076 nan 0.2000 0.0293
## 2 1.1513 nan 0.2000 0.0236
## 3 1.1056 nan 0.2000 0.0183
## 4 1.0655 nan 0.2000 0.0149
## 5 1.0379 nan 0.2000 0.0086
## 6 1.0167 nan 0.2000 0.0013
## 7 0.9920 nan 0.2000 0.0078
## 8 0.9782 nan 0.2000 0.0049
## 9 0.9625 nan 0.2000 0.0022
## 10 0.9461 nan 0.2000 0.0010
## 20 0.8654 nan 0.2000 -0.0041
## 40 0.7866 nan 0.2000 -0.0022
## 60 0.7298 nan 0.2000 -0.0011
## 80 0.6971 nan 0.2000 -0.0015
## 100 0.6626 nan 0.2000 -0.0012
## 120 0.6367 nan 0.2000 -0.0020
## 140 0.6238 nan 0.2000 -0.0032
## 160 0.5907 nan 0.2000 -0.0027
## 180 0.5683 nan 0.2000 -0.0026
## 200 0.5395 nan 0.2000 -0.0027
## 220 0.5147 nan 0.2000 -0.0013
## 240 0.4914 nan 0.2000 -0.0036
## 260 0.4721 nan 0.2000 -0.0020
## 280 0.4515 nan 0.2000 -0.0009
## 300 0.4236 nan 0.2000 -0.0013
## 320 0.4080 nan 0.2000 -0.0019
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## 460 0.3080 nan 0.2000 -0.0004
## 480 0.2958 nan 0.2000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2091 nan 0.2000 0.0318
## 2 1.1443 nan 0.2000 0.0275
## 3 1.0927 nan 0.2000 0.0245
## 4 1.0601 nan 0.2000 0.0124
## 5 1.0354 nan 0.2000 0.0073
## 6 1.0151 nan 0.2000 0.0062
## 7 0.9964 nan 0.2000 0.0050
## 8 0.9739 nan 0.2000 0.0033
## 9 0.9586 nan 0.2000 0.0050
## 10 0.9423 nan 0.2000 0.0059
## 20 0.8632 nan 0.2000 -0.0049
## 40 0.7839 nan 0.2000 -0.0008
## 60 0.7279 nan 0.2000 -0.0017
## 80 0.6898 nan 0.2000 -0.0005
## 100 0.6624 nan 0.2000 -0.0035
## 120 0.6276 nan 0.2000 -0.0020
## 140 0.5875 nan 0.2000 -0.0023
## 160 0.5606 nan 0.2000 -0.0012
## 180 0.5366 nan 0.2000 -0.0036
## 200 0.5131 nan 0.2000 -0.0025
## 220 0.4945 nan 0.2000 -0.0027
## 240 0.4717 nan 0.2000 -0.0033
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## 280 0.4414 nan 0.2000 -0.0015
## 300 0.4289 nan 0.2000 -0.0026
## 320 0.4131 nan 0.2000 -0.0014
## 340 0.3923 nan 0.2000 -0.0021
## 360 0.3787 nan 0.2000 -0.0012
## 380 0.3646 nan 0.2000 -0.0026
## 400 0.3489 nan 0.2000 0.0001
## 420 0.3370 nan 0.2000 -0.0034
## 440 0.3263 nan 0.2000 -0.0012
## 460 0.3141 nan 0.2000 -0.0022
## 480 0.3005 nan 0.2000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2046 nan 0.2000 0.0373
## 2 1.1351 nan 0.2000 0.0254
## 3 1.0896 nan 0.2000 0.0194
## 4 1.0580 nan 0.2000 0.0131
## 5 1.0370 nan 0.2000 0.0059
## 6 1.0182 nan 0.2000 0.0042
## 7 0.9919 nan 0.2000 0.0108
## 8 0.9710 nan 0.2000 0.0015
## 9 0.9613 nan 0.2000 0.0020
## 10 0.9447 nan 0.2000 0.0032
## 20 0.8709 nan 0.2000 -0.0024
## 40 0.7885 nan 0.2000 0.0007
## 60 0.7370 nan 0.2000 -0.0047
## 80 0.6912 nan 0.2000 -0.0011
## 100 0.6508 nan 0.2000 -0.0020
## 120 0.6223 nan 0.2000 -0.0019
## 140 0.5920 nan 0.2000 -0.0015
## 160 0.5666 nan 0.2000 -0.0013
## 180 0.5396 nan 0.2000 -0.0026
## 200 0.5166 nan 0.2000 -0.0032
## 220 0.4949 nan 0.2000 -0.0012
## 240 0.4728 nan 0.2000 -0.0034
## 260 0.4532 nan 0.2000 -0.0021
## 280 0.4366 nan 0.2000 -0.0017
## 300 0.4228 nan 0.2000 -0.0012
## 320 0.4009 nan 0.2000 -0.0022
## 340 0.3882 nan 0.2000 -0.0010
## 360 0.3714 nan 0.2000 -0.0012
## 380 0.3614 nan 0.2000 -0.0029
## 400 0.3476 nan 0.2000 -0.0018
## 420 0.3357 nan 0.2000 -0.0024
## 440 0.3210 nan 0.2000 -0.0009
## 460 0.3080 nan 0.2000 -0.0011
## 480 0.2963 nan 0.2000 -0.0026
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2037 nan 0.2000 0.0453
## 2 1.1350 nan 0.2000 0.0238
## 3 1.0809 nan 0.2000 0.0206
## 4 1.0422 nan 0.2000 0.0118
## 5 1.0071 nan 0.2000 0.0089
## 6 0.9907 nan 0.2000 0.0016
## 7 0.9643 nan 0.2000 0.0051
## 8 0.9435 nan 0.2000 0.0043
## 9 0.9259 nan 0.2000 0.0006
## 10 0.9045 nan 0.2000 0.0047
## 20 0.8188 nan 0.2000 -0.0028
## 40 0.7329 nan 0.2000 -0.0002
## 60 0.6667 nan 0.2000 -0.0033
## 80 0.6110 nan 0.2000 -0.0013
## 100 0.5537 nan 0.2000 -0.0026
## 120 0.5108 nan 0.2000 -0.0043
## 140 0.4627 nan 0.2000 -0.0012
## 160 0.4286 nan 0.2000 -0.0022
## 180 0.3919 nan 0.2000 -0.0011
## 200 0.3591 nan 0.2000 0.0000
## 220 0.3357 nan 0.2000 -0.0022
## 240 0.3108 nan 0.2000 -0.0013
## 260 0.2909 nan 0.2000 -0.0017
## 280 0.2698 nan 0.2000 -0.0015
## 300 0.2530 nan 0.2000 -0.0015
## 320 0.2388 nan 0.2000 -0.0019
## 340 0.2247 nan 0.2000 -0.0008
## 360 0.2073 nan 0.2000 -0.0017
## 380 0.1941 nan 0.2000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2000 nan 0.2000 0.0428
## 2 1.1344 nan 0.2000 0.0254
## 3 1.0890 nan 0.2000 0.0206
## 4 1.0488 nan 0.2000 0.0136
## 5 1.0138 nan 0.2000 0.0144
## 6 0.9831 nan 0.2000 0.0111
## 7 0.9557 nan 0.2000 0.0067
## 8 0.9396 nan 0.2000 0.0049
## 9 0.9174 nan 0.2000 0.0046
## 10 0.9025 nan 0.2000 0.0017
## 20 0.8076 nan 0.2000 -0.0026
## 40 0.7198 nan 0.2000 -0.0064
## 60 0.6412 nan 0.2000 -0.0067
## 80 0.5883 nan 0.2000 -0.0032
## 100 0.5323 nan 0.2000 -0.0012
## 120 0.4830 nan 0.2000 -0.0024
## 140 0.4448 nan 0.2000 -0.0027
## 160 0.4099 nan 0.2000 -0.0017
## 180 0.3776 nan 0.2000 -0.0013
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## 220 0.3213 nan 0.2000 -0.0022
## 240 0.2992 nan 0.2000 -0.0015
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## 280 0.2557 nan 0.2000 -0.0004
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## 320 0.2284 nan 0.2000 -0.0007
## 340 0.2142 nan 0.2000 -0.0018
## 360 0.1986 nan 0.2000 -0.0001
## 380 0.1877 nan 0.2000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2030 nan 0.2000 0.0431
## 2 1.1297 nan 0.2000 0.0291
## 3 1.0848 nan 0.2000 0.0183
## 4 1.0393 nan 0.2000 0.0144
## 5 1.0060 nan 0.2000 0.0099
## 6 0.9760 nan 0.2000 0.0123
## 7 0.9473 nan 0.2000 0.0071
## 8 0.9309 nan 0.2000 0.0015
## 9 0.9168 nan 0.2000 0.0000
## 10 0.9006 nan 0.2000 0.0014
## 20 0.8175 nan 0.2000 -0.0020
## 40 0.7205 nan 0.2000 -0.0037
## 60 0.6472 nan 0.2000 -0.0019
## 80 0.5932 nan 0.2000 -0.0051
## 100 0.5437 nan 0.2000 -0.0017
## 120 0.4950 nan 0.2000 -0.0047
## 140 0.4589 nan 0.2000 -0.0007
## 160 0.4165 nan 0.2000 -0.0001
## 180 0.3801 nan 0.2000 -0.0024
## 200 0.3494 nan 0.2000 -0.0023
## 220 0.3249 nan 0.2000 -0.0006
## 240 0.3022 nan 0.2000 -0.0018
## 260 0.2820 nan 0.2000 -0.0014
## 280 0.2623 nan 0.2000 -0.0015
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## 320 0.2301 nan 0.2000 -0.0021
## 340 0.2160 nan 0.2000 -0.0010
## 360 0.2041 nan 0.2000 -0.0012
## 380 0.1900 nan 0.2000 -0.0006
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## 480 0.1390 nan 0.2000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1895 nan 0.3000 0.0425
## 2 1.1422 nan 0.3000 0.0211
## 3 1.0902 nan 0.3000 0.0170
## 4 1.0498 nan 0.3000 0.0143
## 5 1.0294 nan 0.3000 0.0069
## 6 1.0046 nan 0.3000 0.0112
## 7 0.9909 nan 0.3000 0.0040
## 8 0.9728 nan 0.3000 0.0053
## 9 0.9610 nan 0.3000 0.0019
## 10 0.9496 nan 0.3000 0.0035
## 20 0.8831 nan 0.3000 0.0015
## 40 0.8305 nan 0.3000 -0.0063
## 60 0.7948 nan 0.3000 -0.0016
## 80 0.7653 nan 0.3000 -0.0020
## 100 0.7459 nan 0.3000 -0.0012
## 120 0.7261 nan 0.3000 -0.0039
## 140 0.7158 nan 0.3000 -0.0036
## 160 0.7047 nan 0.3000 -0.0037
## 180 0.6924 nan 0.3000 -0.0021
## 200 0.6830 nan 0.3000 -0.0055
## 220 0.6736 nan 0.3000 -0.0029
## 240 0.6628 nan 0.3000 -0.0022
## 260 0.6549 nan 0.3000 -0.0073
## 280 0.6453 nan 0.3000 -0.0017
## 300 0.6411 nan 0.3000 -0.0055
## 320 0.6327 nan 0.3000 -0.0040
## 340 0.6258 nan 0.3000 -0.0029
## 360 0.6184 nan 0.3000 -0.0031
## 380 0.6140 nan 0.3000 -0.0004
## 400 0.6028 nan 0.3000 -0.0031
## 420 0.5981 nan 0.3000 -0.0040
## 440 0.5893 nan 0.3000 -0.0023
## 460 0.5865 nan 0.3000 -0.0041
## 480 0.5841 nan 0.3000 -0.0019
## 500 0.5741 nan 0.3000 -0.0039
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2092 nan 0.3000 0.0453
## 2 1.1587 nan 0.3000 0.0208
## 3 1.1122 nan 0.3000 0.0181
## 4 1.0833 nan 0.3000 0.0125
## 5 1.0544 nan 0.3000 0.0146
## 6 1.0311 nan 0.3000 0.0092
## 7 1.0121 nan 0.3000 0.0070
## 8 1.0024 nan 0.3000 -0.0012
## 9 0.9900 nan 0.3000 0.0015
## 10 0.9826 nan 0.3000 -0.0028
## 20 0.9031 nan 0.3000 -0.0014
## 40 0.8391 nan 0.3000 -0.0012
## 60 0.8107 nan 0.3000 -0.0022
## 80 0.7871 nan 0.3000 -0.0069
## 100 0.7695 nan 0.3000 -0.0043
## 120 0.7497 nan 0.3000 -0.0019
## 140 0.7353 nan 0.3000 -0.0030
## 160 0.7238 nan 0.3000 -0.0044
## 180 0.7075 nan 0.3000 -0.0011
## 200 0.6910 nan 0.3000 -0.0032
## 220 0.6812 nan 0.3000 -0.0030
## 240 0.6691 nan 0.3000 -0.0027
## 260 0.6664 nan 0.3000 -0.0038
## 280 0.6561 nan 0.3000 -0.0040
## 300 0.6474 nan 0.3000 -0.0017
## 320 0.6401 nan 0.3000 -0.0028
## 340 0.6322 nan 0.3000 -0.0009
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## 380 0.6188 nan 0.3000 -0.0035
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1974 nan 0.3000 0.0455
## 2 1.1455 nan 0.3000 0.0227
## 3 1.1032 nan 0.3000 0.0165
## 4 1.0747 nan 0.3000 0.0135
## 5 1.0388 nan 0.3000 0.0154
## 6 1.0186 nan 0.3000 0.0091
## 7 1.0037 nan 0.3000 0.0037
## 8 0.9849 nan 0.3000 0.0071
## 9 0.9715 nan 0.3000 0.0059
## 10 0.9600 nan 0.3000 0.0030
## 20 0.8950 nan 0.3000 -0.0035
## 40 0.8489 nan 0.3000 -0.0013
## 60 0.8089 nan 0.3000 -0.0037
## 80 0.7894 nan 0.3000 -0.0031
## 100 0.7690 nan 0.3000 -0.0042
## 120 0.7577 nan 0.3000 -0.0028
## 140 0.7404 nan 0.3000 -0.0035
## 160 0.7286 nan 0.3000 -0.0009
## 180 0.7241 nan 0.3000 -0.0055
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## 240 0.6751 nan 0.3000 -0.0031
## 260 0.6670 nan 0.3000 -0.0040
## 280 0.6594 nan 0.3000 -0.0044
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## 380 0.6169 nan 0.3000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2004 nan 0.3000 0.0323
## 2 1.1106 nan 0.3000 0.0448
## 3 1.0646 nan 0.3000 0.0148
## 4 1.0166 nan 0.3000 0.0075
## 5 0.9859 nan 0.3000 0.0056
## 6 0.9643 nan 0.3000 0.0041
## 7 0.9475 nan 0.3000 0.0026
## 8 0.9344 nan 0.3000 0.0021
## 9 0.9112 nan 0.3000 0.0056
## 10 0.8974 nan 0.3000 0.0029
## 20 0.8216 nan 0.3000 0.0086
## 40 0.7406 nan 0.3000 -0.0001
## 60 0.6755 nan 0.3000 -0.0020
## 80 0.6258 nan 0.3000 -0.0039
## 100 0.5879 nan 0.3000 -0.0008
## 120 0.5581 nan 0.3000 -0.0062
## 140 0.5225 nan 0.3000 -0.0026
## 160 0.4923 nan 0.3000 -0.0025
## 180 0.4553 nan 0.3000 -0.0044
## 200 0.4300 nan 0.3000 -0.0020
## 220 0.4029 nan 0.3000 -0.0018
## 240 0.3835 nan 0.3000 -0.0029
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## 280 0.3466 nan 0.3000 -0.0019
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## 320 0.3159 nan 0.3000 -0.0025
## 340 0.2948 nan 0.3000 -0.0006
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## 380 0.2662 nan 0.3000 -0.0024
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1695 nan 0.3000 0.0510
## 2 1.0950 nan 0.3000 0.0180
## 3 1.0390 nan 0.3000 0.0178
## 4 1.0111 nan 0.3000 0.0074
## 5 0.9763 nan 0.3000 0.0114
## 6 0.9571 nan 0.3000 0.0010
## 7 0.9394 nan 0.3000 -0.0010
## 8 0.9187 nan 0.3000 0.0078
## 9 0.9030 nan 0.3000 0.0008
## 10 0.9000 nan 0.3000 -0.0037
## 20 0.8409 nan 0.3000 -0.0050
## 40 0.7495 nan 0.3000 -0.0009
## 60 0.6956 nan 0.3000 -0.0082
## 80 0.6505 nan 0.3000 -0.0026
## 100 0.5986 nan 0.3000 -0.0012
## 120 0.5616 nan 0.3000 -0.0043
## 140 0.5234 nan 0.3000 -0.0044
## 160 0.4924 nan 0.3000 -0.0021
## 180 0.4711 nan 0.3000 -0.0090
## 200 0.4424 nan 0.3000 -0.0041
## 220 0.4113 nan 0.3000 -0.0032
## 240 0.3876 nan 0.3000 -0.0045
## 260 0.3758 nan 0.3000 -0.0011
## 280 0.3504 nan 0.3000 -0.0029
## 300 0.3373 nan 0.3000 -0.0015
## 320 0.3229 nan 0.3000 -0.0019
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## 360 0.2868 nan 0.3000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1804 nan 0.3000 0.0503
## 2 1.1007 nan 0.3000 0.0337
## 3 1.0459 nan 0.3000 0.0237
## 4 0.9973 nan 0.3000 0.0177
## 5 0.9693 nan 0.3000 0.0068
## 6 0.9488 nan 0.3000 0.0037
## 7 0.9330 nan 0.3000 0.0013
## 8 0.9178 nan 0.3000 0.0036
## 9 0.9096 nan 0.3000 -0.0011
## 10 0.8937 nan 0.3000 0.0055
## 20 0.8280 nan 0.3000 -0.0016
## 40 0.7435 nan 0.3000 -0.0026
## 60 0.6931 nan 0.3000 -0.0047
## 80 0.6478 nan 0.3000 -0.0037
## 100 0.6003 nan 0.3000 -0.0034
## 120 0.5682 nan 0.3000 -0.0039
## 140 0.5339 nan 0.3000 -0.0022
## 160 0.4882 nan 0.3000 -0.0025
## 180 0.4671 nan 0.3000 -0.0026
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## 220 0.4168 nan 0.3000 -0.0023
## 240 0.3870 nan 0.3000 -0.0040
## 260 0.3627 nan 0.3000 -0.0020
## 280 0.3414 nan 0.3000 -0.0022
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## 340 0.2847 nan 0.3000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1683 nan 0.3000 0.0688
## 2 1.0768 nan 0.3000 0.0345
## 3 1.0220 nan 0.3000 0.0161
## 4 0.9739 nan 0.3000 0.0152
## 5 0.9463 nan 0.3000 0.0040
## 6 0.9150 nan 0.3000 0.0055
## 7 0.8972 nan 0.3000 -0.0030
## 8 0.8821 nan 0.3000 -0.0054
## 9 0.8705 nan 0.3000 -0.0003
## 10 0.8649 nan 0.3000 -0.0056
## 20 0.7804 nan 0.3000 -0.0087
## 40 0.6498 nan 0.3000 -0.0041
## 60 0.5780 nan 0.3000 -0.0036
## 80 0.5179 nan 0.3000 -0.0030
## 100 0.4573 nan 0.3000 -0.0047
## 120 0.4052 nan 0.3000 -0.0036
## 140 0.3592 nan 0.3000 -0.0029
## 160 0.3237 nan 0.3000 -0.0013
## 180 0.2847 nan 0.3000 -0.0006
## 200 0.2611 nan 0.3000 -0.0010
## 220 0.2318 nan 0.3000 -0.0033
## 240 0.2081 nan 0.3000 -0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1668 nan 0.3000 0.0617
## 2 1.0629 nan 0.3000 0.0443
## 3 1.0027 nan 0.3000 0.0244
## 4 0.9592 nan 0.3000 0.0142
## 5 0.9377 nan 0.3000 0.0013
## 6 0.9164 nan 0.3000 0.0052
## 7 0.8947 nan 0.3000 0.0023
## 8 0.8745 nan 0.3000 0.0077
## 9 0.8590 nan 0.3000 -0.0030
## 10 0.8481 nan 0.3000 -0.0023
## 20 0.7800 nan 0.3000 -0.0022
## 40 0.6680 nan 0.3000 -0.0058
## 60 0.5844 nan 0.3000 -0.0040
## 80 0.5200 nan 0.3000 -0.0070
## 100 0.4590 nan 0.3000 -0.0028
## 120 0.4045 nan 0.3000 -0.0039
## 140 0.3490 nan 0.3000 -0.0015
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## 180 0.2799 nan 0.3000 -0.0020
## 200 0.2613 nan 0.3000 -0.0014
## 220 0.2389 nan 0.3000 -0.0009
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## 280 0.1748 nan 0.3000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1683 nan 0.3000 0.0536
## 2 1.0892 nan 0.3000 0.0280
## 3 1.0274 nan 0.3000 0.0211
## 4 0.9902 nan 0.3000 0.0145
## 5 0.9508 nan 0.3000 0.0140
## 6 0.9275 nan 0.3000 0.0039
## 7 0.9065 nan 0.3000 -0.0023
## 8 0.8936 nan 0.3000 -0.0067
## 9 0.8808 nan 0.3000 -0.0012
## 10 0.8689 nan 0.3000 0.0004
## 20 0.7803 nan 0.3000 -0.0053
## 40 0.6779 nan 0.3000 -0.0108
## 60 0.5959 nan 0.3000 0.0015
## 80 0.5400 nan 0.3000 -0.0071
## 100 0.4770 nan 0.3000 -0.0057
## 120 0.4124 nan 0.3000 -0.0032
## 140 0.3616 nan 0.3000 -0.0025
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## 180 0.2928 nan 0.3000 -0.0044
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1557 nan 0.5000 0.0673
## 2 1.0933 nan 0.5000 0.0190
## 3 1.0732 nan 0.5000 -0.0057
## 4 1.0299 nan 0.5000 0.0227
## 5 0.9952 nan 0.5000 0.0121
## 6 0.9598 nan 0.5000 0.0045
## 7 0.9486 nan 0.5000 -0.0059
## 8 0.9392 nan 0.5000 0.0002
## 9 0.9294 nan 0.5000 -0.0009
## 10 0.9238 nan 0.5000 -0.0059
## 20 0.8731 nan 0.5000 0.0005
## 40 0.8129 nan 0.5000 0.0024
## 60 inf nan 0.5000 nan
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 -0.0002
## 140 inf nan 0.5000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1553 nan 0.5000 0.0597
## 2 1.1032 nan 0.5000 0.0240
## 3 1.0552 nan 0.5000 0.0211
## 4 1.0164 nan 0.5000 0.0144
## 5 0.9962 nan 0.5000 0.0019
## 6 0.9580 nan 0.5000 0.0145
## 7 0.9465 nan 0.5000 0.0010
## 8 0.9307 nan 0.5000 0.0030
## 9 0.9154 nan 0.5000 0.0001
## 10 0.9047 nan 0.5000 -0.0085
## 20 0.8502 nan 0.5000 -0.0003
## 40 0.7946 nan 0.5000 -0.0047
## 60 0.7704 nan 0.5000 -0.0013
## 80 0.7437 nan 0.5000 -0.0045
## 100 0.7158 nan 0.5000 -0.0027
## 120 0.7051 nan 0.5000 -0.0085
## 140 0.6809 nan 0.5000 -0.0059
## 160 0.6626 nan 0.5000 -0.0049
## 180 0.6553 nan 0.5000 -0.0060
## 200 0.6423 nan 0.5000 -0.0044
## 220 0.6273 nan 0.5000 -0.0030
## 240 0.6183 nan 0.5000 -0.0079
## 260 0.6097 nan 0.5000 -0.0052
## 280 0.5930 nan 0.5000 -0.0048
## 300 0.5805 nan 0.5000 -0.0023
## 320 0.5711 nan 0.5000 -0.0021
## 340 0.5676 nan 0.5000 -0.0051
## 360 0.5600 nan 0.5000 -0.0046
## 380 0.5618 nan 0.5000 -0.0068
## 400 0.5401 nan 0.5000 -0.0035
## 420 0.5343 nan 0.5000 -0.0036
## 440 0.5282 nan 0.5000 -0.0049
## 460 0.5227 nan 0.5000 -0.0040
## 480 0.5065 nan 0.5000 -0.0032
## 500 0.5008 nan 0.5000 -0.0051
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1442 nan 0.5000 0.0527
## 2 1.0786 nan 0.5000 0.0139
## 3 1.0369 nan 0.5000 0.0203
## 4 1.0004 nan 0.5000 0.0165
## 5 0.9658 nan 0.5000 0.0135
## 6 0.9516 nan 0.5000 0.0003
## 7 0.9360 nan 0.5000 0.0004
## 8 0.9248 nan 0.5000 -0.0011
## 9 0.9225 nan 0.5000 -0.0064
## 10 0.9157 nan 0.5000 0.0004
## 20 0.8457 nan 0.5000 -0.0065
## 40 0.8059 nan 0.5000 -0.0074
## 60 0.7879 nan 0.5000 -0.0049
## 80 0.7552 nan 0.5000 -0.0032
## 100 0.7289 nan 0.5000 -0.0074
## 120 0.7165 nan 0.5000 -0.0062
## 140 0.7071 nan 0.5000 -0.0078
## 160 0.6998 nan 0.5000 -0.0075
## 180 0.6706 nan 0.5000 -0.0013
## 200 0.6597 nan 0.5000 -0.0133
## 220 0.6486 nan 0.5000 -0.0025
## 240 0.6409 nan 0.5000 -0.0074
## 260 0.6270 nan 0.5000 -0.0013
## 280 0.6123 nan 0.5000 -0.0082
## 300 0.5940 nan 0.5000 -0.0038
## 320 0.5876 nan 0.5000 -0.0027
## 340 0.5774 nan 0.5000 -0.0058
## 360 0.5706 nan 0.5000 -0.0041
## 380 0.5647 nan 0.5000 -0.0035
## 400 0.5576 nan 0.5000 -0.0029
## 420 0.5478 nan 0.5000 -0.0041
## 440 0.5314 nan 0.5000 -0.0012
## 460 0.5225 nan 0.5000 -0.0070
## 480 0.5190 nan 0.5000 -0.0032
## 500 0.5019 nan 0.5000 -0.0043
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1306 nan 0.5000 0.0841
## 2 1.0494 nan 0.5000 0.0320
## 3 1.0176 nan 0.5000 0.0026
## 4 0.9675 nan 0.5000 0.0150
## 5 0.9429 nan 0.5000 0.0036
## 6 0.9242 nan 0.5000 0.0036
## 7 0.9115 nan 0.5000 -0.0047
## 8 0.8910 nan 0.5000 -0.0057
## 9 0.8719 nan 0.5000 -0.0029
## 10 0.8688 nan 0.5000 -0.0115
## 20 0.7969 nan 0.5000 -0.0046
## 40 0.9901 nan 0.5000 -0.0125
## 60 0.8761 nan 0.5000 -0.0009
## 80 inf nan 0.5000 -0.1296
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
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## 220 inf nan 0.5000 nan
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## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1248 nan 0.5000 0.0700
## 2 1.0281 nan 0.5000 0.0435
## 3 0.9841 nan 0.5000 0.0092
## 4 0.9432 nan 0.5000 0.0070
## 5 0.9349 nan 0.5000 -0.0085
## 6 0.9145 nan 0.5000 -0.0066
## 7 0.8885 nan 0.5000 0.0046
## 8 0.8662 nan 0.5000 0.0041
## 9 0.8544 nan 0.5000 -0.0003
## 10 0.8439 nan 0.5000 0.0002
## 20 0.7804 nan 0.5000 -0.0042
## 40 0.6877 nan 0.5000 -0.0017
## 60 0.6104 nan 0.5000 -0.0045
## 80 0.5556 nan 0.5000 -0.0087
## 100 0.5086 nan 0.5000 -0.0041
## 120 0.4594 nan 0.5000 -0.0115
## 140 0.4226 nan 0.5000 -0.0084
## 160 0.3852 nan 0.5000 -0.0070
## 180 0.3395 nan 0.5000 -0.0047
## 200 0.3040 nan 0.5000 -0.0023
## 220 0.2774 nan 0.5000 -0.0057
## 240 0.2458 nan 0.5000 -0.0015
## 260 0.2154 nan 0.5000 -0.0017
## 280 0.1922 nan 0.5000 -0.0002
## 300 0.1781 nan 0.5000 -0.0012
## 320 0.1651 nan 0.5000 -0.0008
## 340 0.1469 nan 0.5000 -0.0038
## 360 0.1354 nan 0.5000 -0.0011
## 380 0.1235 nan 0.5000 -0.0020
## 400 0.1153 nan 0.5000 -0.0002
## 420 0.1058 nan 0.5000 -0.0006
## 440 0.0970 nan 0.5000 -0.0022
## 460 0.0889 nan 0.5000 -0.0018
## 480 0.0825 nan 0.5000 -0.0008
## 500 0.0764 nan 0.5000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1146 nan 0.5000 0.0776
## 2 1.0287 nan 0.5000 0.0304
## 3 0.9681 nan 0.5000 0.0139
## 4 0.9413 nan 0.5000 0.0069
## 5 0.9300 nan 0.5000 -0.0091
## 6 0.9205 nan 0.5000 -0.0155
## 7 0.8946 nan 0.5000 0.0020
## 8 0.8758 nan 0.5000 0.0007
## 9 0.8681 nan 0.5000 -0.0111
## 10 0.8595 nan 0.5000 -0.0082
## 20 0.8548 nan 0.5000 -0.0156
## 40 0.7634 nan 0.5000 -0.0065
## 60 0.6608 nan 0.5000 -0.0029
## 80 0.5694 nan 0.5000 -0.0039
## 100 0.5021 nan 0.5000 -0.0062
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
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## 220 inf nan 0.5000 nan
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## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1055 nan 0.5000 0.0699
## 2 1.0284 nan 0.5000 0.0202
## 3 0.9820 nan 0.5000 0.0101
## 4 0.9338 nan 0.5000 0.0143
## 5 0.9131 nan 0.5000 -0.0032
## 6 0.8937 nan 0.5000 -0.0071
## 7 0.8873 nan 0.5000 -0.0176
## 8 0.8706 nan 0.5000 -0.0082
## 9 0.8681 nan 0.5000 -0.0143
## 10 0.8581 nan 0.5000 -0.0049
## 20 0.7503 nan 0.5000 -0.0088
## 40 0.5897 nan 0.5000 -0.0155
## 60 0.5431 nan 0.5000 -0.0079
## 80 0.4930 nan 0.5000 -0.0014
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0821 nan 0.5000 0.0868
## 2 0.9915 nan 0.5000 0.0335
## 3 0.9423 nan 0.5000 0.0122
## 4 0.9147 nan 0.5000 0.0023
## 5 0.8823 nan 0.5000 0.0033
## 6 0.8633 nan 0.5000 -0.0071
## 7 0.8446 nan 0.5000 -0.0086
## 8 0.8291 nan 0.5000 -0.0048
## 9 0.8221 nan 0.5000 -0.0123
## 10 0.8061 nan 0.5000 -0.0081
## 20 0.7246 nan 0.5000 -0.0098
## 40 0.6040 nan 0.5000 -0.0078
## 60 0.4750 nan 0.5000 -0.0050
## 80 0.4091 nan 0.5000 -0.0099
## 100 0.3297 nan 0.5000 -0.0037
## 120 0.9646 nan 0.5000 -0.0030
## 140 0.8291 nan 0.5000 -0.0013
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 -0.0015
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 -0.0015
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 -0.0007
## 340 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0945 nan 0.5000 0.0863
## 2 0.9933 nan 0.5000 0.0369
## 3 0.9526 nan 0.5000 0.0037
## 4 0.9145 nan 0.5000 0.0081
## 5 0.8902 nan 0.5000 0.0001
## 6 0.8647 nan 0.5000 -0.0009
## 7 0.8448 nan 0.5000 0.0025
## 8 0.8371 nan 0.5000 -0.0088
## 9 0.8258 nan 0.5000 -0.0090
## 10 0.8093 nan 0.5000 -0.0031
## 20 0.7091 nan 0.5000 -0.0053
## 40 0.5853 nan 0.5000 -0.0103
## 60 0.4806 nan 0.5000 -0.0034
## 80 0.3814 nan 0.5000 -0.0085
## 100 0.3095 nan 0.5000 -0.0074
## 120 0.2530 nan 0.5000 -0.0007
## 140 0.2102 nan 0.5000 -0.0029
## 160 0.1794 nan 0.5000 -0.0009
## 180 0.1539 nan 0.5000 -0.0004
## 200 0.1341 nan 0.5000 -0.0032
## 220 0.1129 nan 0.5000 -0.0016
## 240 0.0989 nan 0.5000 -0.0031
## 260 0.0884 nan 0.5000 -0.0010
## 280 0.0765 nan 0.5000 -0.0012
## 300 0.0662 nan 0.5000 -0.0015
## 320 0.0592 nan 0.5000 -0.0010
## 340 0.0511 nan 0.5000 -0.0011
## 360 0.0445 nan 0.5000 -0.0009
## 380 0.0392 nan 0.5000 -0.0000
## 400 0.0337 nan 0.5000 -0.0004
## 420 0.0310 nan 0.5000 -0.0005
## 440 0.0276 nan 0.5000 -0.0004
## 460 0.0237 nan 0.5000 -0.0003
## 480 0.0211 nan 0.5000 -0.0003
## 500 0.0189 nan 0.5000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1263 nan 1.0000 0.0821
## 2 1.0685 nan 1.0000 0.0063
## 3 1.0150 nan 1.0000 0.0139
## 4 0.9791 nan 1.0000 0.0106
## 5 1.0292 nan 1.0000 -0.0619
## 6 1.0154 nan 1.0000 -0.0046
## 7 1.0102 nan 1.0000 -0.0103
## 8 1.0092 nan 1.0000 -0.0170
## 9 1.0389 nan 1.0000 -0.0212
## 10 1.0349 nan 1.0000 -0.0115
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 200 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1161 nan 1.0000 0.0782
## 2 1.0603 nan 1.0000 -0.0070
## 3 1.0098 nan 1.0000 0.0242
## 4 1.0343 nan 1.0000 -0.0455
## 5 1.0140 nan 1.0000 -0.0025
## 6 0.9956 nan 1.0000 0.0011
## 7 0.9915 nan 1.0000 -0.0264
## 8 0.9977 nan 1.0000 -0.0206
## 9 1.0166 nan 1.0000 -0.0389
## 10 1.0087 nan 1.0000 -0.0290
## 20 937493056.1750 nan 1.0000 0.0077
## 40 937493058.6785 nan 1.0000 -0.0312
## 60 937493058.6082 nan 1.0000 -0.0013
## 80 937493058.5094 nan 1.0000 -0.0072
## 100 937493078.2023 nan 1.0000 -0.0448
## 120 937493078.2081 nan 1.0000 -0.0171
## 140 373204708981721595924.0000 nan 1.0000 0.0033
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 1825765563213561993860248620806022228600224226004466826428802284066484048262040488600288206248226066822084026862024040884062260206866442228820404862446202266400684246466002442802824260480462040604868488664420884600626062.0000 nan 1.0000 0.0007
## 380 1825765563213561993860248620806022228600224226004466826428802284066484048262040488600288206248226066822084026862024040884062260206866442228820404862446202266400684246466002442802824260480462040604868488664420884600626062.0000 nan 1.0000 -38.0565
## 400 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1245 nan 1.0000 0.0501
## 2 1.0548 nan 1.0000 0.0316
## 3 1.0425 nan 1.0000 -0.0129
## 4 1.0162 nan 1.0000 0.0091
## 5 0.9983 nan 1.0000 -0.0046
## 6 0.9936 nan 1.0000 -0.0161
## 7 0.9751 nan 1.0000 0.0002
## 8 0.9632 nan 1.0000 0.0001
## 9 0.9603 nan 1.0000 -0.0141
## 10 0.9538 nan 1.0000 -0.0137
## 20 0.9024 nan 1.0000 -0.0132
## 40 0.8625 nan 1.0000 -0.0274
## 60 0.7845 nan 1.0000 -0.0264
## 80 0.7300 nan 1.0000 0.0000
## 100 0.7396 nan 1.0000 -0.0225
## 120 0.7152 nan 1.0000 -0.0361
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000 nan 1.0000 -0.0361
## 240 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000 nan 1.0000 -0.0000
## 260 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000 nan 1.0000 0.0020
## 280 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000 nan 1.0000 -0.0830
## 300 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000 nan 1.0000 0.0119
## 320 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000 nan 1.0000 -0.0268
## 340 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000 nan 1.0000 -0.0220
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0747 nan 1.0000 0.1075
## 2 1.0179 nan 1.0000 0.0039
## 3 0.9734 nan 1.0000 0.0091
## 4 0.9783 nan 1.0000 -0.0329
## 5 0.9971 nan 1.0000 -0.0625
## 6 3.9797 nan 1.0000 -3.0321
## 7 3.9466 nan 1.0000 -0.0090
## 8 3.9457 nan 1.0000 -0.0193
## 9 3.9275 nan 1.0000 -0.0167
## 10 3.9205 nan 1.0000 -0.0032
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0744 nan 1.0000 0.0851
## 2 0.9750 nan 1.0000 0.0350
## 3 0.9513 nan 1.0000 -0.0026
## 4 0.9451 nan 1.0000 -0.0117
## 5 0.9262 nan 1.0000 -0.0056
## 6 0.9423 nan 1.0000 -0.0377
## 7 0.9091 nan 1.0000 0.0038
## 8 0.9226 nan 1.0000 -0.0361
## 9 0.9146 nan 1.0000 -0.0208
## 10 0.8934 nan 1.0000 -0.0123
## 20 2.0729 nan 1.0000 -0.0522
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 200 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0801 nan 1.0000 0.1057
## 2 1.0156 nan 1.0000 0.0185
## 3 0.9622 nan 1.0000 0.0042
## 4 0.9342 nan 1.0000 -0.0214
## 5 0.9181 nan 1.0000 -0.0239
## 6 0.9295 nan 1.0000 -0.0536
## 7 0.9127 nan 1.0000 -0.0249
## 8 0.9131 nan 1.0000 -0.0358
## 9 0.9638 nan 1.0000 -0.0796
## 10 0.9257 nan 1.0000 0.0131
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0551 nan 1.0000 0.0744
## 2 0.9964 nan 1.0000 -0.0098
## 3 0.9518 nan 1.0000 -0.0190
## 4 0.9897 nan 1.0000 -0.0793
## 5 0.9569 nan 1.0000 -0.0056
## 6 1.0045 nan 1.0000 -0.0817
## 7 1.0051 nan 1.0000 -0.0547
## 8 0.9842 nan 1.0000 -0.0330
## 9 0.9843 nan 1.0000 -0.0490
## 10 0.9591 nan 1.0000 -0.0180
## 20 1.5144 nan 1.0000 -0.0437
## 40 1284.4598 nan 1.0000 -0.0295
## 60 38681437849941152112000468044028060646208684220488828048642048886428204.0000 nan 1.0000 -0.0216
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 -250.3232
## 120 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
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## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0586 nan 1.0000 0.0904
## 2 0.9605 nan 1.0000 0.0344
## 3 1.0113 nan 1.0000 -0.0978
## 4 0.9842 nan 1.0000 -0.0016
## 5 0.9495 nan 1.0000 -0.0077
## 6 0.9606 nan 1.0000 -0.0401
## 7 1.0022 nan 1.0000 -0.0782
## 8 0.9570 nan 1.0000 -0.0058
## 9 0.9690 nan 1.0000 -0.0588
## 10 1.1244 nan 1.0000 -0.2113
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0652 nan 1.0000 0.0907
## 2 1.0248 nan 1.0000 -0.0400
## 3 0.9759 nan 1.0000 -0.0401
## 4 0.9214 nan 1.0000 0.0114
## 5 0.9494 nan 1.0000 -0.0483
## 6 0.9560 nan 1.0000 -0.0198
## 7 0.9827 nan 1.0000 -0.0910
## 8 1.1044 nan 1.0000 -0.1859
## 9 1.1145 nan 1.0000 -0.0336
## 10 1.1529 nan 1.0000 -0.0815
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## 3 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2900 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0001
## 20 1.2859 nan 0.0010 0.0002
## 40 1.2787 nan 0.0010 0.0002
## 60 1.2719 nan 0.0010 0.0002
## 80 1.2653 nan 0.0010 0.0001
## 100 1.2587 nan 0.0010 0.0001
## 120 1.2525 nan 0.0010 0.0001
## 140 1.2466 nan 0.0010 0.0001
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## 180 1.2349 nan 0.0010 0.0001
## 200 1.2293 nan 0.0010 0.0001
## 220 1.2243 nan 0.0010 0.0001
## 240 1.2192 nan 0.0010 0.0001
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## 280 1.2091 nan 0.0010 0.0001
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## 320 1.1997 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2910 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2903 nan 0.0010 0.0002
## 9 1.2899 nan 0.0010 0.0002
## 10 1.2896 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2790 nan 0.0010 0.0002
## 60 1.2720 nan 0.0010 0.0002
## 80 1.2655 nan 0.0010 0.0001
## 100 1.2592 nan 0.0010 0.0001
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## 140 1.2468 nan 0.0010 0.0001
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## 180 1.2352 nan 0.0010 0.0001
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## 240 1.2191 nan 0.0010 0.0001
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## 280 1.2090 nan 0.0010 0.0001
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## 320 1.1997 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2903 nan 0.0010 0.0002
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## 10 1.2896 nan 0.0010 0.0002
## 20 1.2860 nan 0.0010 0.0002
## 40 1.2785 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0003
## 7 1.2899 nan 0.0010 0.0002
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## 10 1.2883 nan 0.0010 0.0002
## 20 1.2837 nan 0.0010 0.0002
## 40 1.2743 nan 0.0010 0.0002
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## 160 1.2241 nan 0.0010 0.0002
## 180 1.2164 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2899 nan 0.0010 0.0002
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## 40 1.2741 nan 0.0010 0.0002
## 60 1.2652 nan 0.0010 0.0002
## 80 1.2564 nan 0.0010 0.0002
## 100 1.2478 nan 0.0010 0.0002
## 120 1.2398 nan 0.0010 0.0002
## 140 1.2317 nan 0.0010 0.0002
## 160 1.2241 nan 0.0010 0.0002
## 180 1.2168 nan 0.0010 0.0002
## 200 1.2092 nan 0.0010 0.0001
## 220 1.2020 nan 0.0010 0.0002
## 240 1.1951 nan 0.0010 0.0001
## 260 1.1884 nan 0.0010 0.0001
## 280 1.1820 nan 0.0010 0.0002
## 300 1.1756 nan 0.0010 0.0001
## 320 1.1697 nan 0.0010 0.0001
## 340 1.1637 nan 0.0010 0.0001
## 360 1.1579 nan 0.0010 0.0001
## 380 1.1521 nan 0.0010 0.0001
## 400 1.1465 nan 0.0010 0.0001
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## 480 1.1253 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
## 9 1.2891 nan 0.0010 0.0002
## 10 1.2886 nan 0.0010 0.0002
## 20 1.2839 nan 0.0010 0.0002
## 40 1.2745 nan 0.0010 0.0002
## 60 1.2653 nan 0.0010 0.0002
## 80 1.2564 nan 0.0010 0.0002
## 100 1.2481 nan 0.0010 0.0002
## 120 1.2398 nan 0.0010 0.0002
## 140 1.2317 nan 0.0010 0.0002
## 160 1.2241 nan 0.0010 0.0002
## 180 1.2166 nan 0.0010 0.0001
## 200 1.2093 nan 0.0010 0.0002
## 220 1.2023 nan 0.0010 0.0001
## 240 1.1954 nan 0.0010 0.0002
## 260 1.1888 nan 0.0010 0.0002
## 280 1.1825 nan 0.0010 0.0001
## 300 1.1761 nan 0.0010 0.0001
## 320 1.1699 nan 0.0010 0.0001
## 340 1.1640 nan 0.0010 0.0001
## 360 1.1582 nan 0.0010 0.0001
## 380 1.1526 nan 0.0010 0.0001
## 400 1.1469 nan 0.0010 0.0001
## 420 1.1415 nan 0.0010 0.0001
## 440 1.1362 nan 0.0010 0.0001
## 460 1.1310 nan 0.0010 0.0001
## 480 1.1259 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0003
## 5 1.2906 nan 0.0010 0.0003
## 6 1.2899 nan 0.0010 0.0003
## 7 1.2894 nan 0.0010 0.0003
## 8 1.2888 nan 0.0010 0.0003
## 9 1.2883 nan 0.0010 0.0002
## 10 1.2878 nan 0.0010 0.0002
## 20 1.2823 nan 0.0010 0.0003
## 40 1.2717 nan 0.0010 0.0002
## 60 1.2612 nan 0.0010 0.0002
## 80 1.2512 nan 0.0010 0.0002
## 100 1.2414 nan 0.0010 0.0002
## 120 1.2318 nan 0.0010 0.0002
## 140 1.2228 nan 0.0010 0.0002
## 160 1.2142 nan 0.0010 0.0002
## 180 1.2055 nan 0.0010 0.0002
## 200 1.1969 nan 0.0010 0.0002
## 220 1.1888 nan 0.0010 0.0001
## 240 1.1810 nan 0.0010 0.0002
## 260 1.1735 nan 0.0010 0.0002
## 280 1.1659 nan 0.0010 0.0001
## 300 1.1585 nan 0.0010 0.0002
## 320 1.1514 nan 0.0010 0.0001
## 340 1.1447 nan 0.0010 0.0001
## 360 1.1382 nan 0.0010 0.0002
## 380 1.1318 nan 0.0010 0.0001
## 400 1.1253 nan 0.0010 0.0001
## 420 1.1192 nan 0.0010 0.0001
## 440 1.1133 nan 0.0010 0.0001
## 460 1.1072 nan 0.0010 0.0001
## 480 1.1018 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0003
## 5 1.2905 nan 0.0010 0.0002
## 6 1.2899 nan 0.0010 0.0003
## 7 1.2894 nan 0.0010 0.0002
## 8 1.2889 nan 0.0010 0.0002
## 9 1.2883 nan 0.0010 0.0002
## 10 1.2878 nan 0.0010 0.0003
## 20 1.2823 nan 0.0010 0.0003
## 40 1.2716 nan 0.0010 0.0002
## 60 1.2611 nan 0.0010 0.0002
## 80 1.2507 nan 0.0010 0.0002
## 100 1.2409 nan 0.0010 0.0002
## 120 1.2315 nan 0.0010 0.0002
## 140 1.2226 nan 0.0010 0.0002
## 160 1.2136 nan 0.0010 0.0002
## 180 1.2050 nan 0.0010 0.0002
## 200 1.1966 nan 0.0010 0.0002
## 220 1.1884 nan 0.0010 0.0002
## 240 1.1805 nan 0.0010 0.0002
## 260 1.1732 nan 0.0010 0.0002
## 280 1.1658 nan 0.0010 0.0001
## 300 1.1588 nan 0.0010 0.0002
## 320 1.1515 nan 0.0010 0.0002
## 340 1.1447 nan 0.0010 0.0002
## 360 1.1382 nan 0.0010 0.0001
## 380 1.1319 nan 0.0010 0.0001
## 400 1.1255 nan 0.0010 0.0001
## 420 1.1193 nan 0.0010 0.0001
## 440 1.1132 nan 0.0010 0.0001
## 460 1.1076 nan 0.0010 0.0001
## 480 1.1021 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2916 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0003
## 5 1.2905 nan 0.0010 0.0003
## 6 1.2899 nan 0.0010 0.0002
## 7 1.2894 nan 0.0010 0.0003
## 8 1.2888 nan 0.0010 0.0002
## 9 1.2883 nan 0.0010 0.0002
## 10 1.2876 nan 0.0010 0.0003
## 20 1.2821 nan 0.0010 0.0003
## 40 1.2715 nan 0.0010 0.0002
## 60 1.2612 nan 0.0010 0.0002
## 80 1.2508 nan 0.0010 0.0003
## 100 1.2409 nan 0.0010 0.0002
## 120 1.2316 nan 0.0010 0.0002
## 140 1.2224 nan 0.0010 0.0002
## 160 1.2134 nan 0.0010 0.0002
## 180 1.2048 nan 0.0010 0.0002
## 200 1.1965 nan 0.0010 0.0002
## 220 1.1883 nan 0.0010 0.0002
## 240 1.1805 nan 0.0010 0.0002
## 260 1.1727 nan 0.0010 0.0002
## 280 1.1654 nan 0.0010 0.0001
## 300 1.1582 nan 0.0010 0.0002
## 320 1.1509 nan 0.0010 0.0001
## 340 1.1440 nan 0.0010 0.0001
## 360 1.1374 nan 0.0010 0.0002
## 380 1.1309 nan 0.0010 0.0001
## 400 1.1247 nan 0.0010 0.0001
## 420 1.1186 nan 0.0010 0.0001
## 440 1.1125 nan 0.0010 0.0001
## 460 1.1069 nan 0.0010 0.0001
## 480 1.1011 nan 0.0010 0.0001
## 500 1.0956 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2590 nan 0.1000 0.0166
## 2 1.2316 nan 0.1000 0.0111
## 3 1.2054 nan 0.1000 0.0099
## 4 1.1805 nan 0.1000 0.0104
## 5 1.1639 nan 0.1000 0.0080
## 6 1.1444 nan 0.1000 0.0075
## 7 1.1299 nan 0.1000 0.0070
## 8 1.1133 nan 0.1000 0.0056
## 9 1.0992 nan 0.1000 0.0053
## 10 1.0882 nan 0.1000 0.0056
## 20 0.9982 nan 0.1000 0.0029
## 40 0.9185 nan 0.1000 -0.0003
## 60 0.8754 nan 0.1000 -0.0007
## 80 0.8446 nan 0.1000 -0.0008
## 100 0.8232 nan 0.1000 0.0002
## 120 0.8133 nan 0.1000 -0.0003
## 140 0.7992 nan 0.1000 -0.0004
## 160 0.7912 nan 0.1000 -0.0022
## 180 0.7800 nan 0.1000 -0.0021
## 200 0.7712 nan 0.1000 -0.0020
## 220 0.7656 nan 0.1000 -0.0026
## 240 0.7599 nan 0.1000 -0.0006
## 260 0.7509 nan 0.1000 0.0003
## 280 0.7439 nan 0.1000 -0.0012
## 300 0.7382 nan 0.1000 -0.0004
## 320 0.7328 nan 0.1000 -0.0005
## 340 0.7253 nan 0.1000 -0.0007
## 360 0.7202 nan 0.1000 -0.0008
## 380 0.7135 nan 0.1000 -0.0006
## 400 0.7065 nan 0.1000 -0.0007
## 420 0.7028 nan 0.1000 -0.0010
## 440 0.6965 nan 0.1000 -0.0007
## 460 0.6912 nan 0.1000 -0.0002
## 480 0.6877 nan 0.1000 -0.0018
## 500 0.6830 nan 0.1000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2618 nan 0.1000 0.0160
## 2 1.2290 nan 0.1000 0.0147
## 3 1.2035 nan 0.1000 0.0110
## 4 1.1818 nan 0.1000 0.0076
## 5 1.1604 nan 0.1000 0.0079
## 6 1.1428 nan 0.1000 0.0064
## 7 1.1275 nan 0.1000 0.0062
## 8 1.1123 nan 0.1000 0.0071
## 9 1.0988 nan 0.1000 0.0056
## 10 1.0846 nan 0.1000 0.0049
## 20 0.9896 nan 0.1000 0.0025
## 40 0.9057 nan 0.1000 0.0007
## 60 0.8616 nan 0.1000 -0.0007
## 80 0.8389 nan 0.1000 -0.0002
## 100 0.8189 nan 0.1000 -0.0017
## 120 0.8013 nan 0.1000 -0.0004
## 140 0.7870 nan 0.1000 -0.0015
## 160 0.7774 nan 0.1000 -0.0010
## 180 0.7670 nan 0.1000 -0.0006
## 200 0.7585 nan 0.1000 -0.0006
## 220 0.7512 nan 0.1000 -0.0012
## 240 0.7426 nan 0.1000 -0.0008
## 260 0.7365 nan 0.1000 -0.0007
## 280 0.7292 nan 0.1000 -0.0010
## 300 0.7241 nan 0.1000 -0.0006
## 320 0.7204 nan 0.1000 -0.0014
## 340 0.7159 nan 0.1000 -0.0019
## 360 0.7093 nan 0.1000 -0.0000
## 380 0.7048 nan 0.1000 -0.0008
## 400 0.7002 nan 0.1000 -0.0009
## 420 0.6960 nan 0.1000 -0.0004
## 440 0.6919 nan 0.1000 -0.0011
## 460 0.6877 nan 0.1000 -0.0002
## 480 0.6830 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2564 nan 0.1000 0.0171
## 2 1.2244 nan 0.1000 0.0149
## 3 1.1952 nan 0.1000 0.0125
## 4 1.1721 nan 0.1000 0.0089
## 5 1.1514 nan 0.1000 0.0070
## 6 1.1322 nan 0.1000 0.0074
## 7 1.1175 nan 0.1000 0.0043
## 8 1.1047 nan 0.1000 0.0037
## 9 1.0902 nan 0.1000 0.0070
## 10 1.0791 nan 0.1000 0.0033
## 20 0.9885 nan 0.1000 0.0004
## 40 0.9134 nan 0.1000 0.0012
## 60 0.8702 nan 0.1000 0.0008
## 80 0.8419 nan 0.1000 -0.0003
## 100 0.8221 nan 0.1000 -0.0007
## 120 0.8067 nan 0.1000 -0.0009
## 140 0.7974 nan 0.1000 -0.0006
## 160 0.7861 nan 0.1000 -0.0003
## 180 0.7755 nan 0.1000 -0.0011
## 200 0.7688 nan 0.1000 -0.0012
## 220 0.7612 nan 0.1000 -0.0010
## 240 0.7555 nan 0.1000 -0.0016
## 260 0.7506 nan 0.1000 -0.0009
## 280 0.7461 nan 0.1000 -0.0015
## 300 0.7396 nan 0.1000 -0.0006
## 320 0.7337 nan 0.1000 -0.0008
## 340 0.7278 nan 0.1000 -0.0011
## 360 0.7198 nan 0.1000 -0.0009
## 380 0.7132 nan 0.1000 -0.0009
## 400 0.7092 nan 0.1000 -0.0007
## 420 0.7052 nan 0.1000 -0.0017
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## 460 0.6950 nan 0.1000 -0.0008
## 480 0.6899 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2459 nan 0.1000 0.0222
## 2 1.2073 nan 0.1000 0.0201
## 3 1.1742 nan 0.1000 0.0097
## 4 1.1448 nan 0.1000 0.0139
## 5 1.1197 nan 0.1000 0.0125
## 6 1.0963 nan 0.1000 0.0090
## 7 1.0777 nan 0.1000 0.0055
## 8 1.0578 nan 0.1000 0.0080
## 9 1.0433 nan 0.1000 0.0050
## 10 1.0268 nan 0.1000 0.0074
## 20 0.9320 nan 0.1000 -0.0007
## 40 0.8362 nan 0.1000 0.0006
## 60 0.7838 nan 0.1000 -0.0007
## 80 0.7476 nan 0.1000 -0.0004
## 100 0.7232 nan 0.1000 -0.0007
## 120 0.7009 nan 0.1000 -0.0011
## 140 0.6732 nan 0.1000 -0.0002
## 160 0.6510 nan 0.1000 -0.0006
## 180 0.6315 nan 0.1000 -0.0014
## 200 0.6132 nan 0.1000 -0.0012
## 220 0.5958 nan 0.1000 -0.0014
## 240 0.5758 nan 0.1000 -0.0014
## 260 0.5616 nan 0.1000 -0.0012
## 280 0.5424 nan 0.1000 -0.0019
## 300 0.5297 nan 0.1000 -0.0007
## 320 0.5133 nan 0.1000 -0.0007
## 340 0.4974 nan 0.1000 -0.0012
## 360 0.4850 nan 0.1000 -0.0010
## 380 0.4733 nan 0.1000 -0.0004
## 400 0.4639 nan 0.1000 -0.0005
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## 440 0.4401 nan 0.1000 -0.0011
## 460 0.4282 nan 0.1000 -0.0020
## 480 0.4181 nan 0.1000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2482 nan 0.1000 0.0234
## 2 1.2084 nan 0.1000 0.0170
## 3 1.1795 nan 0.1000 0.0148
## 4 1.1464 nan 0.1000 0.0135
## 5 1.1220 nan 0.1000 0.0125
## 6 1.0964 nan 0.1000 0.0123
## 7 1.0781 nan 0.1000 0.0066
## 8 1.0607 nan 0.1000 0.0068
## 9 1.0415 nan 0.1000 0.0079
## 10 1.0257 nan 0.1000 0.0070
## 20 0.9278 nan 0.1000 0.0007
## 40 0.8428 nan 0.1000 -0.0008
## 60 0.7878 nan 0.1000 -0.0008
## 80 0.7535 nan 0.1000 -0.0012
## 100 0.7249 nan 0.1000 -0.0019
## 120 0.7045 nan 0.1000 -0.0017
## 140 0.6838 nan 0.1000 -0.0010
## 160 0.6604 nan 0.1000 -0.0012
## 180 0.6398 nan 0.1000 -0.0005
## 200 0.6197 nan 0.1000 -0.0004
## 220 0.6035 nan 0.1000 -0.0013
## 240 0.5884 nan 0.1000 -0.0007
## 260 0.5731 nan 0.1000 -0.0009
## 280 0.5567 nan 0.1000 -0.0004
## 300 0.5425 nan 0.1000 -0.0003
## 320 0.5266 nan 0.1000 -0.0007
## 340 0.5138 nan 0.1000 -0.0025
## 360 0.5029 nan 0.1000 -0.0014
## 380 0.4922 nan 0.1000 -0.0010
## 400 0.4781 nan 0.1000 -0.0007
## 420 0.4639 nan 0.1000 -0.0016
## 440 0.4526 nan 0.1000 -0.0002
## 460 0.4432 nan 0.1000 -0.0010
## 480 0.4315 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2500 nan 0.1000 0.0205
## 2 1.2089 nan 0.1000 0.0181
## 3 1.1763 nan 0.1000 0.0163
## 4 1.1436 nan 0.1000 0.0127
## 5 1.1170 nan 0.1000 0.0114
## 6 1.0943 nan 0.1000 0.0105
## 7 1.0712 nan 0.1000 0.0084
## 8 1.0530 nan 0.1000 0.0030
## 9 1.0383 nan 0.1000 0.0034
## 10 1.0234 nan 0.1000 0.0065
## 20 0.9276 nan 0.1000 0.0032
## 40 0.8283 nan 0.1000 -0.0003
## 60 0.7783 nan 0.1000 -0.0012
## 80 0.7385 nan 0.1000 -0.0009
## 100 0.7109 nan 0.1000 -0.0020
## 120 0.6896 nan 0.1000 -0.0009
## 140 0.6683 nan 0.1000 -0.0010
## 160 0.6440 nan 0.1000 -0.0008
## 180 0.6255 nan 0.1000 -0.0006
## 200 0.6041 nan 0.1000 -0.0014
## 220 0.5927 nan 0.1000 -0.0019
## 240 0.5788 nan 0.1000 -0.0008
## 260 0.5605 nan 0.1000 -0.0011
## 280 0.5471 nan 0.1000 -0.0006
## 300 0.5332 nan 0.1000 -0.0005
## 320 0.5190 nan 0.1000 -0.0014
## 340 0.5040 nan 0.1000 -0.0003
## 360 0.4925 nan 0.1000 -0.0007
## 380 0.4803 nan 0.1000 -0.0013
## 400 0.4669 nan 0.1000 -0.0013
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## 460 0.4328 nan 0.1000 -0.0009
## 480 0.4219 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2407 nan 0.1000 0.0260
## 2 1.2018 nan 0.1000 0.0131
## 3 1.1617 nan 0.1000 0.0181
## 4 1.1281 nan 0.1000 0.0136
## 5 1.0947 nan 0.1000 0.0123
## 6 1.0710 nan 0.1000 0.0117
## 7 1.0438 nan 0.1000 0.0113
## 8 1.0222 nan 0.1000 0.0076
## 9 1.0012 nan 0.1000 0.0077
## 10 0.9880 nan 0.1000 0.0035
## 20 0.8827 nan 0.1000 0.0016
## 40 0.7743 nan 0.1000 -0.0025
## 60 0.7141 nan 0.1000 0.0001
## 80 0.6739 nan 0.1000 -0.0008
## 100 0.6362 nan 0.1000 -0.0016
## 120 0.5956 nan 0.1000 -0.0010
## 140 0.5698 nan 0.1000 -0.0013
## 160 0.5418 nan 0.1000 -0.0011
## 180 0.5154 nan 0.1000 -0.0010
## 200 0.4937 nan 0.1000 -0.0023
## 220 0.4702 nan 0.1000 -0.0003
## 240 0.4447 nan 0.1000 -0.0014
## 260 0.4232 nan 0.1000 -0.0009
## 280 0.4008 nan 0.1000 -0.0008
## 300 0.3794 nan 0.1000 -0.0008
## 320 0.3637 nan 0.1000 -0.0006
## 340 0.3500 nan 0.1000 -0.0017
## 360 0.3331 nan 0.1000 -0.0006
## 380 0.3207 nan 0.1000 -0.0010
## 400 0.3072 nan 0.1000 -0.0008
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## 460 0.2720 nan 0.1000 -0.0005
## 480 0.2628 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2359 nan 0.1000 0.0226
## 2 1.1909 nan 0.1000 0.0223
## 3 1.1599 nan 0.1000 0.0144
## 4 1.1267 nan 0.1000 0.0134
## 5 1.0982 nan 0.1000 0.0122
## 6 1.0759 nan 0.1000 0.0093
## 7 1.0495 nan 0.1000 0.0133
## 8 1.0325 nan 0.1000 0.0071
## 9 1.0135 nan 0.1000 0.0057
## 10 0.9918 nan 0.1000 0.0074
## 20 0.8869 nan 0.1000 0.0016
## 40 0.7836 nan 0.1000 0.0001
## 60 0.7320 nan 0.1000 -0.0007
## 80 0.6767 nan 0.1000 -0.0022
## 100 0.6314 nan 0.1000 -0.0006
## 120 0.5941 nan 0.1000 -0.0005
## 140 0.5699 nan 0.1000 -0.0010
## 160 0.5411 nan 0.1000 -0.0014
## 180 0.5140 nan 0.1000 -0.0006
## 200 0.4909 nan 0.1000 0.0003
## 220 0.4650 nan 0.1000 -0.0003
## 240 0.4423 nan 0.1000 -0.0015
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## 280 0.4050 nan 0.1000 -0.0013
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## 380 0.3212 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2423 nan 0.1000 0.0238
## 2 1.1884 nan 0.1000 0.0242
## 3 1.1460 nan 0.1000 0.0165
## 4 1.1198 nan 0.1000 0.0083
## 5 1.0947 nan 0.1000 0.0110
## 6 1.0713 nan 0.1000 0.0101
## 7 1.0505 nan 0.1000 0.0077
## 8 1.0291 nan 0.1000 0.0062
## 9 1.0111 nan 0.1000 0.0059
## 10 0.9947 nan 0.1000 0.0052
## 20 0.8912 nan 0.1000 0.0025
## 40 0.7845 nan 0.1000 -0.0022
## 60 0.7307 nan 0.1000 -0.0023
## 80 0.6831 nan 0.1000 -0.0000
## 100 0.6517 nan 0.1000 -0.0006
## 120 0.6152 nan 0.1000 -0.0011
## 140 0.5829 nan 0.1000 -0.0017
## 160 0.5554 nan 0.1000 -0.0025
## 180 0.5291 nan 0.1000 -0.0008
## 200 0.5067 nan 0.1000 -0.0014
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## 240 0.4606 nan 0.1000 -0.0014
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## 280 0.4225 nan 0.1000 -0.0015
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## 340 0.3741 nan 0.1000 -0.0014
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## 480 0.2828 nan 0.1000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2328 nan 0.2000 0.0322
## 2 1.1820 nan 0.2000 0.0226
## 3 1.1486 nan 0.2000 0.0126
## 4 1.1160 nan 0.2000 0.0150
## 5 1.0895 nan 0.2000 0.0130
## 6 1.0654 nan 0.2000 0.0098
## 7 1.0442 nan 0.2000 0.0064
## 8 1.0275 nan 0.2000 0.0037
## 9 1.0148 nan 0.2000 0.0049
## 10 0.9939 nan 0.2000 0.0076
## 20 0.9055 nan 0.2000 0.0008
## 40 0.8415 nan 0.2000 -0.0009
## 60 0.8060 nan 0.2000 -0.0020
## 80 0.7840 nan 0.2000 -0.0033
## 100 0.7691 nan 0.2000 -0.0011
## 120 0.7482 nan 0.2000 -0.0009
## 140 0.7341 nan 0.2000 -0.0017
## 160 0.7232 nan 0.2000 -0.0009
## 180 0.7138 nan 0.2000 -0.0003
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## 240 0.6892 nan 0.2000 -0.0002
## 260 0.6804 nan 0.2000 -0.0011
## 280 0.6725 nan 0.2000 -0.0035
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## 320 0.6523 nan 0.2000 -0.0013
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## 420 0.6267 nan 0.2000 -0.0027
## 440 0.6183 nan 0.2000 -0.0005
## 460 0.6118 nan 0.2000 -0.0020
## 480 0.6068 nan 0.2000 -0.0025
## 500 0.6006 nan 0.2000 -0.0018
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2276 nan 0.2000 0.0305
## 2 1.1775 nan 0.2000 0.0169
## 3 1.1504 nan 0.2000 0.0133
## 4 1.1131 nan 0.2000 0.0133
## 5 1.0854 nan 0.2000 0.0119
## 6 1.0631 nan 0.2000 0.0083
## 7 1.0390 nan 0.2000 0.0085
## 8 1.0211 nan 0.2000 0.0080
## 9 1.0109 nan 0.2000 0.0027
## 10 0.9986 nan 0.2000 0.0030
## 20 0.9104 nan 0.2000 0.0020
## 40 0.8485 nan 0.2000 -0.0009
## 60 0.8176 nan 0.2000 -0.0007
## 80 0.7899 nan 0.2000 -0.0008
## 100 0.7743 nan 0.2000 -0.0017
## 120 0.7546 nan 0.2000 -0.0022
## 140 0.7416 nan 0.2000 -0.0035
## 160 0.7292 nan 0.2000 -0.0019
## 180 0.7190 nan 0.2000 -0.0020
## 200 0.7055 nan 0.2000 -0.0021
## 220 0.6994 nan 0.2000 -0.0011
## 240 0.6900 nan 0.2000 -0.0007
## 260 0.6787 nan 0.2000 -0.0002
## 280 0.6723 nan 0.2000 -0.0023
## 300 0.6663 nan 0.2000 -0.0016
## 320 0.6612 nan 0.2000 -0.0025
## 340 0.6537 nan 0.2000 -0.0014
## 360 0.6465 nan 0.2000 -0.0016
## 380 0.6392 nan 0.2000 -0.0015
## 400 0.6338 nan 0.2000 -0.0029
## 420 0.6277 nan 0.2000 -0.0016
## 440 0.6208 nan 0.2000 -0.0022
## 460 0.6142 nan 0.2000 -0.0020
## 480 0.6087 nan 0.2000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2216 nan 0.2000 0.0335
## 2 1.1764 nan 0.2000 0.0214
## 3 1.1395 nan 0.2000 0.0129
## 4 1.1101 nan 0.2000 0.0140
## 5 1.0850 nan 0.2000 0.0092
## 6 1.0636 nan 0.2000 0.0085
## 7 1.0377 nan 0.2000 0.0109
## 8 1.0231 nan 0.2000 0.0035
## 9 1.0050 nan 0.2000 0.0066
## 10 0.9961 nan 0.2000 0.0013
## 20 0.9105 nan 0.2000 0.0038
## 40 0.8449 nan 0.2000 -0.0017
## 60 0.8114 nan 0.2000 -0.0037
## 80 0.7880 nan 0.2000 -0.0042
## 100 0.7716 nan 0.2000 -0.0011
## 120 0.7591 nan 0.2000 -0.0022
## 140 0.7476 nan 0.2000 -0.0010
## 160 0.7334 nan 0.2000 -0.0015
## 180 0.7261 nan 0.2000 -0.0020
## 200 0.7167 nan 0.2000 -0.0001
## 220 0.7091 nan 0.2000 -0.0016
## 240 0.7000 nan 0.2000 -0.0022
## 260 0.6934 nan 0.2000 -0.0017
## 280 0.6876 nan 0.2000 -0.0014
## 300 0.6773 nan 0.2000 -0.0026
## 320 0.6684 nan 0.2000 -0.0015
## 340 0.6586 nan 0.2000 -0.0021
## 360 0.6526 nan 0.2000 -0.0038
## 380 0.6467 nan 0.2000 -0.0025
## 400 0.6376 nan 0.2000 -0.0013
## 420 0.6337 nan 0.2000 -0.0015
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## 480 0.6175 nan 0.2000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2140 nan 0.2000 0.0398
## 2 1.1406 nan 0.2000 0.0288
## 3 1.0914 nan 0.2000 0.0212
## 4 1.0596 nan 0.2000 0.0097
## 5 1.0254 nan 0.2000 0.0139
## 6 1.0042 nan 0.2000 0.0057
## 7 0.9798 nan 0.2000 0.0059
## 8 0.9650 nan 0.2000 0.0030
## 9 0.9545 nan 0.2000 -0.0006
## 10 0.9415 nan 0.2000 0.0002
## 20 0.8396 nan 0.2000 0.0003
## 40 0.7548 nan 0.2000 -0.0001
## 60 0.7095 nan 0.2000 -0.0058
## 80 0.6610 nan 0.2000 -0.0036
## 100 0.6228 nan 0.2000 -0.0039
## 120 0.5881 nan 0.2000 -0.0010
## 140 0.5577 nan 0.2000 -0.0029
## 160 0.5236 nan 0.2000 -0.0013
## 180 0.4983 nan 0.2000 -0.0021
## 200 0.4743 nan 0.2000 -0.0023
## 220 0.4499 nan 0.2000 -0.0009
## 240 0.4252 nan 0.2000 -0.0005
## 260 0.4037 nan 0.2000 -0.0010
## 280 0.3894 nan 0.2000 -0.0020
## 300 0.3760 nan 0.2000 -0.0012
## 320 0.3592 nan 0.2000 -0.0004
## 340 0.3429 nan 0.2000 -0.0011
## 360 0.3289 nan 0.2000 -0.0015
## 380 0.3098 nan 0.2000 -0.0004
## 400 0.2982 nan 0.2000 -0.0022
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## 460 0.2645 nan 0.2000 0.0002
## 480 0.2530 nan 0.2000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2017 nan 0.2000 0.0365
## 2 1.1352 nan 0.2000 0.0280
## 3 1.0792 nan 0.2000 0.0225
## 4 1.0467 nan 0.2000 0.0117
## 5 1.0168 nan 0.2000 0.0070
## 6 0.9964 nan 0.2000 0.0065
## 7 0.9748 nan 0.2000 0.0052
## 8 0.9580 nan 0.2000 0.0035
## 9 0.9390 nan 0.2000 0.0036
## 10 0.9265 nan 0.2000 0.0039
## 20 0.8365 nan 0.2000 -0.0025
## 40 0.7710 nan 0.2000 -0.0046
## 60 0.7146 nan 0.2000 -0.0037
## 80 0.6629 nan 0.2000 -0.0010
## 100 0.6208 nan 0.2000 -0.0016
## 120 0.5867 nan 0.2000 -0.0031
## 140 0.5542 nan 0.2000 -0.0021
## 160 0.5253 nan 0.2000 -0.0003
## 180 0.5020 nan 0.2000 -0.0035
## 200 0.4751 nan 0.2000 -0.0007
## 220 0.4486 nan 0.2000 -0.0015
## 240 0.4301 nan 0.2000 -0.0027
## 260 0.4116 nan 0.2000 -0.0004
## 280 0.3914 nan 0.2000 -0.0003
## 300 0.3720 nan 0.2000 -0.0009
## 320 0.3538 nan 0.2000 -0.0026
## 340 0.3329 nan 0.2000 -0.0021
## 360 0.3212 nan 0.2000 -0.0016
## 380 0.3058 nan 0.2000 -0.0019
## 400 0.2967 nan 0.2000 -0.0010
## 420 0.2860 nan 0.2000 -0.0007
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## 460 0.2584 nan 0.2000 -0.0010
## 480 0.2497 nan 0.2000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2119 nan 0.2000 0.0358
## 2 1.1490 nan 0.2000 0.0277
## 3 1.0875 nan 0.2000 0.0212
## 4 1.0589 nan 0.2000 0.0100
## 5 1.0237 nan 0.2000 0.0130
## 6 1.0011 nan 0.2000 0.0073
## 7 0.9777 nan 0.2000 0.0082
## 8 0.9601 nan 0.2000 0.0044
## 9 0.9458 nan 0.2000 0.0019
## 10 0.9355 nan 0.2000 0.0008
## 20 0.8485 nan 0.2000 -0.0004
## 40 0.7628 nan 0.2000 -0.0033
## 60 0.7031 nan 0.2000 -0.0032
## 80 0.6585 nan 0.2000 -0.0028
## 100 0.6306 nan 0.2000 -0.0020
## 120 0.6015 nan 0.2000 -0.0034
## 140 0.5578 nan 0.2000 -0.0027
## 160 0.5227 nan 0.2000 -0.0007
## 180 0.4949 nan 0.2000 -0.0011
## 200 0.4681 nan 0.2000 -0.0012
## 220 0.4448 nan 0.2000 -0.0010
## 240 0.4265 nan 0.2000 -0.0013
## 260 0.4108 nan 0.2000 -0.0016
## 280 0.3960 nan 0.2000 -0.0007
## 300 0.3819 nan 0.2000 -0.0013
## 320 0.3625 nan 0.2000 -0.0016
## 340 0.3463 nan 0.2000 -0.0024
## 360 0.3393 nan 0.2000 -0.0022
## 380 0.3258 nan 0.2000 -0.0023
## 400 0.3129 nan 0.2000 -0.0026
## 420 0.3027 nan 0.2000 -0.0008
## 440 0.2898 nan 0.2000 -0.0008
## 460 0.2768 nan 0.2000 -0.0007
## 480 0.2649 nan 0.2000 -0.0019
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1965 nan 0.2000 0.0443
## 2 1.1149 nan 0.2000 0.0352
## 3 1.0625 nan 0.2000 0.0234
## 4 1.0270 nan 0.2000 0.0103
## 5 0.9949 nan 0.2000 0.0081
## 6 0.9712 nan 0.2000 0.0043
## 7 0.9454 nan 0.2000 0.0043
## 8 0.9224 nan 0.2000 0.0033
## 9 0.9038 nan 0.2000 0.0037
## 10 0.8835 nan 0.2000 0.0045
## 20 0.7866 nan 0.2000 -0.0004
## 40 0.6776 nan 0.2000 -0.0015
## 60 0.6258 nan 0.2000 -0.0129
## 80 0.5652 nan 0.2000 -0.0016
## 100 0.5092 nan 0.2000 -0.0047
## 120 0.4501 nan 0.2000 -0.0017
## 140 0.4145 nan 0.2000 -0.0034
## 160 0.3751 nan 0.2000 -0.0012
## 180 0.3460 nan 0.2000 -0.0011
## 200 0.3192 nan 0.2000 -0.0009
## 220 0.2991 nan 0.2000 -0.0022
## 240 0.2726 nan 0.2000 -0.0005
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## 280 0.2379 nan 0.2000 -0.0014
## 300 0.2209 nan 0.2000 -0.0021
## 320 0.2036 nan 0.2000 -0.0009
## 340 0.1911 nan 0.2000 -0.0015
## 360 0.1785 nan 0.2000 -0.0009
## 380 0.1688 nan 0.2000 -0.0005
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## 480 0.1197 nan 0.2000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1858 nan 0.2000 0.0440
## 2 1.1254 nan 0.2000 0.0222
## 3 1.0689 nan 0.2000 0.0216
## 4 1.0167 nan 0.2000 0.0225
## 5 0.9818 nan 0.2000 0.0131
## 6 0.9587 nan 0.2000 0.0056
## 7 0.9351 nan 0.2000 0.0056
## 8 0.9122 nan 0.2000 0.0057
## 9 0.8957 nan 0.2000 -0.0000
## 10 0.8815 nan 0.2000 0.0003
## 20 0.7925 nan 0.2000 -0.0028
## 40 0.7004 nan 0.2000 -0.0010
## 60 0.6306 nan 0.2000 -0.0042
## 80 0.5754 nan 0.2000 -0.0024
## 100 0.5265 nan 0.2000 -0.0017
## 120 0.4842 nan 0.2000 -0.0049
## 140 0.4371 nan 0.2000 -0.0014
## 160 0.3956 nan 0.2000 -0.0018
## 180 0.3671 nan 0.2000 -0.0035
## 200 0.3362 nan 0.2000 -0.0047
## 220 0.3086 nan 0.2000 -0.0026
## 240 0.2860 nan 0.2000 -0.0014
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## 280 0.2449 nan 0.2000 -0.0006
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## 320 0.2100 nan 0.2000 -0.0014
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## 380 0.1726 nan 0.2000 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1916 nan 0.2000 0.0477
## 2 1.1086 nan 0.2000 0.0367
## 3 1.0632 nan 0.2000 0.0174
## 4 1.0147 nan 0.2000 0.0213
## 5 0.9889 nan 0.2000 0.0044
## 6 0.9571 nan 0.2000 0.0085
## 7 0.9300 nan 0.2000 0.0064
## 8 0.9099 nan 0.2000 0.0039
## 9 0.8933 nan 0.2000 0.0027
## 10 0.8799 nan 0.2000 0.0020
## 20 0.7874 nan 0.2000 -0.0030
## 40 0.6911 nan 0.2000 -0.0025
## 60 0.6141 nan 0.2000 -0.0043
## 80 0.5606 nan 0.2000 -0.0027
## 100 0.5110 nan 0.2000 -0.0020
## 120 0.4665 nan 0.2000 -0.0013
## 140 0.4282 nan 0.2000 -0.0033
## 160 0.3899 nan 0.2000 -0.0036
## 180 0.3622 nan 0.2000 -0.0020
## 200 0.3220 nan 0.2000 -0.0015
## 220 0.2947 nan 0.2000 -0.0006
## 240 0.2724 nan 0.2000 -0.0007
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## 280 0.2367 nan 0.2000 -0.0023
## 300 0.2188 nan 0.2000 -0.0008
## 320 0.2051 nan 0.2000 -0.0009
## 340 0.1907 nan 0.2000 -0.0009
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## 380 0.1674 nan 0.2000 -0.0019
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2064 nan 0.3000 0.0455
## 2 1.1425 nan 0.3000 0.0188
## 3 1.0973 nan 0.3000 0.0187
## 4 1.0530 nan 0.3000 0.0157
## 5 1.0217 nan 0.3000 0.0140
## 6 1.0028 nan 0.3000 0.0044
## 7 0.9855 nan 0.3000 0.0043
## 8 0.9626 nan 0.3000 0.0078
## 9 0.9512 nan 0.3000 0.0026
## 10 0.9402 nan 0.3000 0.0012
## 20 0.8655 nan 0.3000 -0.0047
## 40 0.7982 nan 0.3000 -0.0009
## 60 0.7697 nan 0.3000 -0.0047
## 80 0.7388 nan 0.3000 -0.0014
## 100 0.7236 nan 0.3000 -0.0027
## 120 0.7015 nan 0.3000 -0.0036
## 140 0.6866 nan 0.3000 -0.0027
## 160 0.6746 nan 0.3000 -0.0016
## 180 0.6649 nan 0.3000 -0.0072
## 200 0.6523 nan 0.3000 -0.0024
## 220 0.6382 nan 0.3000 -0.0005
## 240 0.6307 nan 0.3000 -0.0045
## 260 0.6226 nan 0.3000 -0.0038
## 280 0.6172 nan 0.3000 -0.0039
## 300 0.6097 nan 0.3000 -0.0029
## 320 0.6014 nan 0.3000 -0.0038
## 340 0.5902 nan 0.3000 -0.0045
## 360 0.5845 nan 0.3000 -0.0062
## 380 0.5770 nan 0.3000 -0.0015
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## 420 0.5654 nan 0.3000 -0.0029
## 440 0.5585 nan 0.3000 -0.0021
## 460 0.5520 nan 0.3000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1987 nan 0.3000 0.0409
## 2 1.1286 nan 0.3000 0.0241
## 3 1.0880 nan 0.3000 0.0179
## 4 1.0539 nan 0.3000 0.0069
## 5 1.0187 nan 0.3000 0.0152
## 6 1.0030 nan 0.3000 0.0062
## 7 0.9793 nan 0.3000 0.0091
## 8 0.9666 nan 0.3000 0.0030
## 9 0.9551 nan 0.3000 0.0014
## 10 0.9396 nan 0.3000 0.0030
## 20 0.8713 nan 0.3000 -0.0000
## 40 0.8172 nan 0.3000 -0.0058
## 60 0.7865 nan 0.3000 -0.0036
## 80 0.7667 nan 0.3000 -0.0045
## 100 0.7444 nan 0.3000 -0.0072
## 120 0.7216 nan 0.3000 -0.0024
## 140 0.7056 nan 0.3000 -0.0039
## 160 0.6936 nan 0.3000 -0.0052
## 180 0.6748 nan 0.3000 -0.0020
## 200 0.6645 nan 0.3000 -0.0012
## 220 0.6645 nan 0.3000 -0.0029
## 240 0.6508 nan 0.3000 -0.0056
## 260 0.6400 nan 0.3000 -0.0027
## 280 0.6309 nan 0.3000 -0.0015
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## 320 0.6104 nan 0.3000 -0.0030
## 340 0.6032 nan 0.3000 -0.0012
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## 380 0.5932 nan 0.3000 -0.0041
## 400 0.5822 nan 0.3000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2097 nan 0.3000 0.0404
## 2 1.1461 nan 0.3000 0.0222
## 3 1.1006 nan 0.3000 0.0172
## 4 1.0622 nan 0.3000 0.0180
## 5 1.0277 nan 0.3000 0.0127
## 6 1.0089 nan 0.3000 0.0023
## 7 0.9871 nan 0.3000 0.0079
## 8 0.9651 nan 0.3000 0.0097
## 9 0.9490 nan 0.3000 0.0053
## 10 0.9334 nan 0.3000 0.0053
## 20 0.8628 nan 0.3000 -0.0007
## 40 0.8070 nan 0.3000 -0.0025
## 60 0.7791 nan 0.3000 -0.0081
## 80 0.7575 nan 0.3000 -0.0033
## 100 0.7331 nan 0.3000 -0.0017
## 120 0.7207 nan 0.3000 -0.0021
## 140 0.7050 nan 0.3000 -0.0028
## 160 0.6979 nan 0.3000 -0.0006
## 180 0.6843 nan 0.3000 -0.0002
## 200 0.6691 nan 0.3000 -0.0002
## 220 0.6590 nan 0.3000 -0.0024
## 240 0.6472 nan 0.3000 -0.0029
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## 320 0.6100 nan 0.3000 -0.0043
## 340 0.6010 nan 0.3000 -0.0023
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## 420 0.5739 nan 0.3000 -0.0009
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## 460 0.5609 nan 0.3000 -0.0032
## 480 0.5522 nan 0.3000 -0.0018
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1618 nan 0.3000 0.0570
## 2 1.0812 nan 0.3000 0.0415
## 3 1.0274 nan 0.3000 0.0258
## 4 0.9821 nan 0.3000 0.0150
## 5 0.9561 nan 0.3000 0.0069
## 6 0.9352 nan 0.3000 0.0019
## 7 0.9176 nan 0.3000 0.0033
## 8 0.8980 nan 0.3000 0.0035
## 9 0.8813 nan 0.3000 -0.0003
## 10 0.8681 nan 0.3000 0.0006
## 20 0.7888 nan 0.3000 -0.0007
## 40 0.7282 nan 0.3000 -0.0062
## 60 0.6635 nan 0.3000 -0.0036
## 80 0.6154 nan 0.3000 -0.0040
## 100 0.5653 nan 0.3000 -0.0010
## 120 0.5361 nan 0.3000 -0.0036
## 140 0.5021 nan 0.3000 -0.0089
## 160 0.4578 nan 0.3000 -0.0041
## 180 0.4175 nan 0.3000 -0.0034
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## 240 0.3422 nan 0.3000 -0.0034
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## 280 0.3063 nan 0.3000 -0.0012
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## 320 0.2816 nan 0.3000 -0.0029
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1675 nan 0.3000 0.0529
## 2 1.0900 nan 0.3000 0.0390
## 3 1.0356 nan 0.3000 0.0235
## 4 0.9977 nan 0.3000 0.0118
## 5 0.9806 nan 0.3000 0.0002
## 6 0.9501 nan 0.3000 0.0069
## 7 0.9308 nan 0.3000 0.0016
## 8 0.9124 nan 0.3000 0.0055
## 9 0.9030 nan 0.3000 -0.0041
## 10 0.8902 nan 0.3000 -0.0007
## 20 0.8104 nan 0.3000 -0.0038
## 40 0.7183 nan 0.3000 -0.0013
## 60 0.6583 nan 0.3000 -0.0058
## 80 0.6146 nan 0.3000 -0.0037
## 100 0.5605 nan 0.3000 -0.0042
## 120 0.5111 nan 0.3000 -0.0048
## 140 0.4769 nan 0.3000 -0.0005
## 160 0.4441 nan 0.3000 -0.0040
## 180 0.4073 nan 0.3000 -0.0039
## 200 0.3742 nan 0.3000 -0.0037
## 220 0.3470 nan 0.3000 -0.0029
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## 280 0.2846 nan 0.3000 -0.0024
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1605 nan 0.3000 0.0508
## 2 1.0959 nan 0.3000 0.0283
## 3 1.0350 nan 0.3000 0.0218
## 4 0.9972 nan 0.3000 0.0111
## 5 0.9721 nan 0.3000 0.0070
## 6 0.9519 nan 0.3000 0.0036
## 7 0.9271 nan 0.3000 0.0084
## 8 0.9082 nan 0.3000 0.0037
## 9 0.8941 nan 0.3000 -0.0001
## 10 0.8789 nan 0.3000 -0.0003
## 20 0.8039 nan 0.3000 -0.0041
## 40 0.7208 nan 0.3000 -0.0016
## 60 0.6485 nan 0.3000 -0.0017
## 80 0.5941 nan 0.3000 -0.0045
## 100 0.5610 nan 0.3000 -0.0065
## 120 0.5240 nan 0.3000 -0.0017
## 140 0.4929 nan 0.3000 -0.0077
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## 180 0.4308 nan 0.3000 -0.0032
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1549 nan 0.3000 0.0478
## 2 1.0744 nan 0.3000 0.0363
## 3 1.0121 nan 0.3000 0.0239
## 4 0.9755 nan 0.3000 0.0043
## 5 0.9289 nan 0.3000 0.0091
## 6 0.9072 nan 0.3000 -0.0011
## 7 0.8941 nan 0.3000 -0.0098
## 8 0.8757 nan 0.3000 -0.0014
## 9 0.8551 nan 0.3000 0.0024
## 10 0.8385 nan 0.3000 -0.0029
## 20 0.7426 nan 0.3000 -0.0085
## 40 0.6317 nan 0.3000 -0.0003
## 60 0.5423 nan 0.3000 -0.0008
## 80 0.4808 nan 0.3000 -0.0054
## 100 0.4165 nan 0.3000 0.0001
## 120 0.3673 nan 0.3000 -0.0031
## 140 0.3264 nan 0.3000 -0.0004
## 160 0.2823 nan 0.3000 -0.0035
## 180 0.2515 nan 0.3000 -0.0011
## 200 0.2283 nan 0.3000 -0.0011
## 220 0.2035 nan 0.3000 -0.0018
## 240 0.1833 nan 0.3000 -0.0009
## 260 0.1660 nan 0.3000 -0.0020
## 280 0.1516 nan 0.3000 -0.0002
## 300 0.1381 nan 0.3000 -0.0004
## 320 0.1243 nan 0.3000 -0.0010
## 340 0.1141 nan 0.3000 -0.0010
## 360 0.1063 nan 0.3000 -0.0006
## 380 0.0947 nan 0.3000 -0.0009
## 400 0.0877 nan 0.3000 -0.0005
## 420 0.0809 nan 0.3000 -0.0013
## 440 0.0747 nan 0.3000 -0.0007
## 460 0.0702 nan 0.3000 -0.0011
## 480 0.0643 nan 0.3000 -0.0003
## 500 0.0589 nan 0.3000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1524 nan 0.3000 0.0626
## 2 1.0677 nan 0.3000 0.0343
## 3 1.0018 nan 0.3000 0.0244
## 4 0.9584 nan 0.3000 0.0144
## 5 0.9309 nan 0.3000 0.0031
## 6 0.9105 nan 0.3000 0.0016
## 7 0.8868 nan 0.3000 0.0056
## 8 0.8696 nan 0.3000 -0.0003
## 9 0.8517 nan 0.3000 0.0014
## 10 0.8377 nan 0.3000 -0.0018
## 20 0.7487 nan 0.3000 -0.0069
## 40 0.6154 nan 0.3000 -0.0051
## 60 0.5268 nan 0.3000 -0.0023
## 80 0.4567 nan 0.3000 -0.0032
## 100 0.4051 nan 0.3000 -0.0053
## 120 0.3599 nan 0.3000 -0.0021
## 140 0.3184 nan 0.3000 0.0005
## 160 0.2808 nan 0.3000 -0.0029
## 180 0.2491 nan 0.3000 -0.0012
## 200 0.2252 nan 0.3000 -0.0029
## 220 0.2021 nan 0.3000 -0.0018
## 240 0.1787 nan 0.3000 -0.0003
## 260 0.1621 nan 0.3000 -0.0011
## 280 0.1446 nan 0.3000 -0.0017
## 300 0.1312 nan 0.3000 -0.0019
## 320 0.1201 nan 0.3000 -0.0013
## 340 0.1102 nan 0.3000 -0.0009
## 360 0.0985 nan 0.3000 -0.0005
## 380 0.0922 nan 0.3000 -0.0014
## 400 0.0845 nan 0.3000 -0.0012
## 420 0.0777 nan 0.3000 -0.0012
## 440 0.0719 nan 0.3000 -0.0008
## 460 0.0661 nan 0.3000 -0.0004
## 480 0.0616 nan 0.3000 -0.0002
## 500 0.0571 nan 0.3000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1539 nan 0.3000 0.0614
## 2 1.0709 nan 0.3000 0.0286
## 3 1.0019 nan 0.3000 0.0297
## 4 0.9596 nan 0.3000 0.0148
## 5 0.9279 nan 0.3000 0.0082
## 6 0.9008 nan 0.3000 0.0036
## 7 0.8787 nan 0.3000 0.0023
## 8 0.8608 nan 0.3000 0.0020
## 9 0.8423 nan 0.3000 0.0009
## 10 0.8330 nan 0.3000 -0.0007
## 20 0.7427 nan 0.3000 -0.0039
## 40 0.6267 nan 0.3000 -0.0054
## 60 0.5444 nan 0.3000 -0.0023
## 80 0.4736 nan 0.3000 -0.0056
## 100 0.4109 nan 0.3000 -0.0063
## 120 0.3597 nan 0.3000 -0.0047
## 140 0.3253 nan 0.3000 -0.0040
## 160 0.2832 nan 0.3000 -0.0016
## 180 0.2534 nan 0.3000 -0.0031
## 200 0.2259 nan 0.3000 -0.0022
## 220 0.2051 nan 0.3000 -0.0018
## 240 0.1791 nan 0.3000 -0.0024
## 260 0.1581 nan 0.3000 -0.0010
## 280 0.1442 nan 0.3000 -0.0012
## 300 0.1307 nan 0.3000 -0.0009
## 320 0.1183 nan 0.3000 -0.0002
## 340 0.1068 nan 0.3000 -0.0008
## 360 0.0977 nan 0.3000 -0.0015
## 380 0.0900 nan 0.3000 -0.0003
## 400 0.0824 nan 0.3000 -0.0002
## 420 0.0746 nan 0.3000 -0.0009
## 440 0.0690 nan 0.3000 -0.0003
## 460 0.0624 nan 0.3000 -0.0002
## 480 0.0570 nan 0.3000 -0.0001
## 500 0.0530 nan 0.3000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1573 nan 0.5000 0.0644
## 2 1.0905 nan 0.5000 0.0262
## 3 1.0387 nan 0.5000 0.0049
## 4 1.0085 nan 0.5000 0.0035
## 5 0.9883 nan 0.5000 0.0006
## 6 0.9547 nan 0.5000 0.0132
## 7 0.9438 nan 0.5000 0.0009
## 8 0.9299 nan 0.5000 -0.0003
## 9 0.9191 nan 0.5000 0.0003
## 10 0.9079 nan 0.5000 0.0001
## 20 0.8560 nan 0.5000 -0.0038
## 40 0.8026 nan 0.5000 -0.0029
## 60 0.7637 nan 0.5000 0.0001
## 80 0.7358 nan 0.5000 -0.0022
## 100 0.7057 nan 0.5000 0.0005
## 120 0.6823 nan 0.5000 -0.0107
## 140 0.6660 nan 0.5000 -0.0010
## 160 0.6461 nan 0.5000 -0.0043
## 180 0.6303 nan 0.5000 -0.0101
## 200 0.6118 nan 0.5000 -0.0119
## 220 0.6030 nan 0.5000 -0.0064
## 240 0.5845 nan 0.5000 -0.0067
## 260 0.5765 nan 0.5000 -0.0086
## 280 0.5568 nan 0.5000 -0.0040
## 300 0.5449 nan 0.5000 -0.0017
## 320 0.5343 nan 0.5000 -0.0049
## 340 0.5235 nan 0.5000 -0.0034
## 360 0.5159 nan 0.5000 -0.0028
## 380 0.4963 nan 0.5000 -0.0048
## 400 0.4908 nan 0.5000 -0.0024
## 420 0.4820 nan 0.5000 -0.0023
## 440 0.4784 nan 0.5000 -0.0028
## 460 0.4647 nan 0.5000 -0.0015
## 480 0.4599 nan 0.5000 -0.0057
## 500 0.4577 nan 0.5000 -0.0077
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1582 nan 0.5000 0.0561
## 2 1.0825 nan 0.5000 0.0352
## 3 1.0240 nan 0.5000 0.0205
## 4 0.9615 nan 0.5000 0.0179
## 5 0.9499 nan 0.5000 -0.0024
## 6 0.9366 nan 0.5000 0.0015
## 7 0.9250 nan 0.5000 -0.0035
## 8 0.9128 nan 0.5000 0.0023
## 9 0.8990 nan 0.5000 -0.0027
## 10 0.8939 nan 0.5000 -0.0046
## 20 0.8423 nan 0.5000 0.0068
## 40 0.7838 nan 0.5000 -0.0030
## 60 0.7433 nan 0.5000 -0.0089
## 80 0.7148 nan 0.5000 -0.0037
## 100 0.7046 nan 0.5000 -0.0164
## 120 0.6764 nan 0.5000 -0.0053
## 140 0.6573 nan 0.5000 -0.0075
## 160 0.6444 nan 0.5000 -0.0041
## 180 0.6305 nan 0.5000 -0.0055
## 200 0.6205 nan 0.5000 -0.0143
## 220 0.6056 nan 0.5000 -0.0061
## 240 0.5935 nan 0.5000 -0.0036
## 260 0.5837 nan 0.5000 -0.0020
## 280 0.5928 nan 0.5000 -0.0015
## 300 4.5850 nan 0.5000 -0.0004
## 320 4.4201 nan 0.5000 -0.0000
## 340 3.2309 nan 0.5000 0.0035
## 360 3.2192 nan 0.5000 -0.0022
## 380 3.2073 nan 0.5000 -0.0009
## 400 3.2288 nan 0.5000 -0.0049
## 420 3.2298 nan 0.5000 -0.0117
## 440 3.2256 nan 0.5000 -0.0026
## 460 3.2124 nan 0.5000 -0.0006
## 480 3.0565 nan 0.5000 -0.0056
## 500 3.0597 nan 0.5000 -0.0034
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1571 nan 0.5000 0.0656
## 2 1.0917 nan 0.5000 0.0236
## 3 1.0316 nan 0.5000 0.0248
## 4 0.9849 nan 0.5000 0.0185
## 5 0.9699 nan 0.5000 0.0004
## 6 0.9504 nan 0.5000 -0.0007
## 7 0.9411 nan 0.5000 -0.0022
## 8 0.9173 nan 0.5000 0.0048
## 9 0.9180 nan 0.5000 -0.0073
## 10 0.9125 nan 0.5000 -0.0071
## 20 0.8430 nan 0.5000 -0.0026
## 40 0.7887 nan 0.5000 -0.0020
## 60 0.7642 nan 0.5000 0.0005
## 80 0.7499 nan 0.5000 -0.0113
## 100 0.7126 nan 0.5000 -0.0047
## 120 0.6929 nan 0.5000 -0.0034
## 140 0.6796 nan 0.5000 -0.0029
## 160 0.6627 nan 0.5000 -0.0094
## 180 0.6366 nan 0.5000 -0.0007
## 200 0.6281 nan 0.5000 -0.0033
## 220 0.6046 nan 0.5000 0.0006
## 240 0.6017 nan 0.5000 -0.0057
## 260 0.5800 nan 0.5000 -0.0034
## 280 0.5777 nan 0.5000 -0.0071
## 300 0.5616 nan 0.5000 -0.0075
## 320 0.5497 nan 0.5000 -0.0033
## 340 0.5438 nan 0.5000 -0.0079
## 360 0.5307 nan 0.5000 -0.0083
## 380 0.5183 nan 0.5000 -0.0029
## 400 0.5085 nan 0.5000 -0.0018
## 420 0.5022 nan 0.5000 -0.0019
## 440 0.5028 nan 0.5000 -0.0067
## 460 0.5108 nan 0.5000 -0.0048
## 480 0.4987 nan 0.5000 -0.0041
## 500 0.4975 nan 0.5000 -0.0073
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1269 nan 0.5000 0.0629
## 2 1.0298 nan 0.5000 0.0316
## 3 0.9771 nan 0.5000 0.0092
## 4 0.9309 nan 0.5000 0.0096
## 5 0.9054 nan 0.5000 0.0081
## 6 0.8939 nan 0.5000 -0.0130
## 7 0.8749 nan 0.5000 -0.0020
## 8 0.8589 nan 0.5000 -0.0011
## 9 0.8462 nan 0.5000 -0.0083
## 10 0.8405 nan 0.5000 -0.0087
## 20 0.8078 nan 0.5000 -0.0125
## 40 inf nan 0.5000 nan
## 60 inf nan 0.5000 -0.0002
## 80 inf nan 0.5000 -0.0146
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 -0.0066
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 -0.0008
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1248 nan 0.5000 0.0629
## 2 1.0353 nan 0.5000 0.0378
## 3 0.9813 nan 0.5000 0.0262
## 4 0.9417 nan 0.5000 0.0077
## 5 0.9228 nan 0.5000 -0.0095
## 6 0.9040 nan 0.5000 -0.0069
## 7 0.8857 nan 0.5000 0.0005
## 8 0.8719 nan 0.5000 -0.0040
## 9 0.8497 nan 0.5000 -0.0047
## 10 0.8379 nan 0.5000 0.0003
## 20 0.7621 nan 0.5000 -0.0186
## 40 0.6666 nan 0.5000 -0.0149
## 60 0.5674 nan 0.5000 -0.0035
## 80 0.5019 nan 0.5000 -0.0097
## 100 0.4435 nan 0.5000 -0.0038
## 120 0.4013 nan 0.5000 -0.0013
## 140 0.3580 nan 0.5000 -0.0026
## 160 0.3130 nan 0.5000 -0.0061
## 180 0.2672 nan 0.5000 -0.0001
## 200 0.2373 nan 0.5000 -0.0009
## 220 0.2193 nan 0.5000 -0.0042
## 240 0.1938 nan 0.5000 -0.0031
## 260 0.1805 nan 0.5000 -0.0028
## 280 0.1611 nan 0.5000 -0.0002
## 300 0.1497 nan 0.5000 -0.0016
## 320 0.1336 nan 0.5000 -0.0010
## 340 0.1196 nan 0.5000 -0.0012
## 360 0.1121 nan 0.5000 -0.0027
## 380 0.1004 nan 0.5000 -0.0017
## 400 0.0899 nan 0.5000 -0.0005
## 420 0.0846 nan 0.5000 -0.0016
## 440 0.0797 nan 0.5000 -0.0004
## 460 0.0743 nan 0.5000 -0.0011
## 480 0.0689 nan 0.5000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1189 nan 0.5000 0.0628
## 2 1.0290 nan 0.5000 0.0325
## 3 0.9552 nan 0.5000 0.0187
## 4 0.9360 nan 0.5000 -0.0108
## 5 0.9202 nan 0.5000 -0.0000
## 6 0.8888 nan 0.5000 0.0059
## 7 0.8765 nan 0.5000 -0.0012
## 8 0.8709 nan 0.5000 -0.0077
## 9 0.8613 nan 0.5000 -0.0039
## 10 0.8509 nan 0.5000 -0.0034
## 20 0.7690 nan 0.5000 -0.0108
## 40 0.6755 nan 0.5000 -0.0006
## 60 0.6028 nan 0.5000 -0.0041
## 80 0.5383 nan 0.5000 -0.0057
## 100 0.4842 nan 0.5000 -0.0070
## 120 0.4377 nan 0.5000 -0.0041
## 140 0.3753 nan 0.5000 -0.0050
## 160 0.3429 nan 0.5000 -0.0023
## 180 0.3148 nan 0.5000 -0.0101
## 200 0.2705 nan 0.5000 -0.0046
## 220 0.2512 nan 0.5000 -0.0073
## 240 0.2245 nan 0.5000 -0.0053
## 260 0.1997 nan 0.5000 -0.0026
## 280 0.1834 nan 0.5000 -0.0011
## 300 0.1715 nan 0.5000 -0.0021
## 320 0.1576 nan 0.5000 -0.0029
## 340 0.1507 nan 0.5000 -0.0005
## 360 0.1354 nan 0.5000 -0.0021
## 380 0.1273 nan 0.5000 -0.0008
## 400 0.1148 nan 0.5000 -0.0014
## 420 0.1067 nan 0.5000 -0.0017
## 440 0.0966 nan 0.5000 -0.0017
## 460 0.0897 nan 0.5000 -0.0011
## 480 0.0834 nan 0.5000 -0.0003
## 500 0.0771 nan 0.5000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0963 nan 0.5000 0.0837
## 2 1.0021 nan 0.5000 0.0344
## 3 0.9407 nan 0.5000 0.0253
## 4 0.9107 nan 0.5000 -0.0076
## 5 0.8786 nan 0.5000 0.0078
## 6 0.8486 nan 0.5000 0.0041
## 7 0.8420 nan 0.5000 -0.0252
## 8 0.8498 nan 0.5000 -0.0377
## 9 0.8061 nan 0.5000 0.0042
## 10 0.8046 nan 0.5000 -0.0165
## 20 4.2210 nan 0.5000 -0.0190
## 40 inf nan 0.5000 nan
## 60 inf nan 0.5000 nan
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
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## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0865 nan 0.5000 0.0719
## 2 0.9784 nan 0.5000 0.0440
## 3 0.9350 nan 0.5000 0.0043
## 4 0.9016 nan 0.5000 0.0026
## 5 0.8729 nan 0.5000 0.0010
## 6 0.8461 nan 0.5000 0.0023
## 7 0.8296 nan 0.5000 -0.0020
## 8 0.8145 nan 0.5000 -0.0075
## 9 0.8094 nan 0.5000 -0.0162
## 10 0.7949 nan 0.5000 -0.0071
## 20 0.6954 nan 0.5000 -0.0138
## 40 0.5565 nan 0.5000 -0.0077
## 60 0.4544 nan 0.5000 -0.0036
## 80 0.3758 nan 0.5000 -0.0068
## 100 0.3066 nan 0.5000 -0.0084
## 120 0.2506 nan 0.5000 -0.0053
## 140 0.2056 nan 0.5000 -0.0018
## 160 0.1805 nan 0.5000 -0.0033
## 180 0.1578 nan 0.5000 -0.0004
## 200 0.1391 nan 0.5000 -0.0051
## 220 0.1170 nan 0.5000 -0.0005
## 240 0.1030 nan 0.5000 -0.0001
## 260 0.0850 nan 0.5000 -0.0004
## 280 0.0717 nan 0.5000 -0.0005
## 300 0.0635 nan 0.5000 -0.0012
## 320 0.0553 nan 0.5000 -0.0012
## 340 0.0474 nan 0.5000 -0.0003
## 360 0.0411 nan 0.5000 -0.0006
## 380 0.0351 nan 0.5000 -0.0004
## 400 0.0316 nan 0.5000 -0.0004
## 420 0.0277 nan 0.5000 -0.0001
## 440 0.0238 nan 0.5000 -0.0000
## 460 0.0209 nan 0.5000 -0.0003
## 480 0.0185 nan 0.5000 -0.0003
## 500 0.0160 nan 0.5000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0889 nan 0.5000 0.0896
## 2 0.9933 nan 0.5000 0.0281
## 3 0.9341 nan 0.5000 0.0243
## 4 0.8908 nan 0.5000 0.0117
## 5 0.8537 nan 0.5000 0.0079
## 6 0.8351 nan 0.5000 -0.0093
## 7 0.8196 nan 0.5000 -0.0041
## 8 0.8015 nan 0.5000 -0.0029
## 9 0.8001 nan 0.5000 -0.0172
## 10 0.7872 nan 0.5000 -0.0098
## 20 0.7132 nan 0.5000 -0.0143
## 40 0.5931 nan 0.5000 -0.0193
## 60 0.4690 nan 0.5000 -0.0120
## 80 0.3797 nan 0.5000 -0.0015
## 100 0.3143 nan 0.5000 -0.0043
## 120 0.2666 nan 0.5000 -0.0077
## 140 0.2350 nan 0.5000 -0.0095
## 160 0.1756 nan 0.5000 -0.0011
## 180 0.1448 nan 0.5000 -0.0005
## 200 0.1225 nan 0.5000 -0.0016
## 220 0.1054 nan 0.5000 -0.0016
## 240 0.0889 nan 0.5000 -0.0004
## 260 0.0760 nan 0.5000 -0.0005
## 280 0.0647 nan 0.5000 -0.0005
## 300 0.0576 nan 0.5000 -0.0008
## 320 0.0492 nan 0.5000 -0.0022
## 340 0.0416 nan 0.5000 -0.0018
## 360 0.0363 nan 0.5000 -0.0011
## 380 0.0316 nan 0.5000 -0.0001
## 400 0.0272 nan 0.5000 -0.0003
## 420 0.0231 nan 0.5000 -0.0002
## 440 0.0211 nan 0.5000 -0.0001
## 460 0.0186 nan 0.5000 -0.0001
## 480 0.0162 nan 0.5000 -0.0001
## 500 0.0145 nan 0.5000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1207 nan 1.0000 0.0569
## 2 1.0805 nan 1.0000 -0.0164
## 3 1.0173 nan 1.0000 0.0313
## 4 1.2356 nan 1.0000 -0.2446
## 5 1.2268 nan 1.0000 -0.0308
## 6 179180.4410 nan 1.0000 -119280.2570
## 7 179180.4340 nan 1.0000 -0.0279
## 8 179180.4321 nan 1.0000 -0.0270
## 9 179180.4124 nan 1.0000 -0.0038
## 10 179180.4034 nan 1.0000 -0.0103
## 20 179181.9246 nan 1.0000 -0.0324
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1243 nan 1.0000 0.0765
## 2 1.0619 nan 1.0000 0.0149
## 3 0.9989 nan 1.0000 0.0121
## 4 1.0297 nan 1.0000 -0.0496
## 5 0.9959 nan 1.0000 0.0128
## 6 0.9916 nan 1.0000 -0.0196
## 7 0.9714 nan 1.0000 0.0034
## 8 0.9544 nan 1.0000 -0.0042
## 9 0.9504 nan 1.0000 -0.0248
## 10 0.9248 nan 1.0000 -0.0009
## 20 0.8813 nan 1.0000 -0.0240
## 40 38240.1096 nan 1.0000 -0.0043
## 60 38240.0572 nan 1.0000 -0.0092
## 80 38240.0558 nan 1.0000 -0.0078
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## 320 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1225 nan 1.0000 0.0605
## 2 1.0468 nan 1.0000 0.0277
## 3 0.9953 nan 1.0000 0.0261
## 4 0.9714 nan 1.0000 0.0020
## 5 0.9612 nan 1.0000 0.0008
## 6 0.9510 nan 1.0000 -0.0112
## 7 0.9640 nan 1.0000 -0.0310
## 8 0.9599 nan 1.0000 -0.0342
## 9 0.9431 nan 1.0000 0.0061
## 10 0.9562 nan 1.0000 -0.0480
## 20 0.8570 nan 1.0000 -0.0117
## 40 0.8162 nan 1.0000 -0.0353
## 60 0.8376 nan 1.0000 -0.0307
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 96295.9951 nan 1.0000 -0.0061
## 160 96296.0082 nan 1.0000 -0.0399
## 180 96295.7469 nan 1.0000 -0.0153
## 200 96295.7446 nan 1.0000 -0.0144
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0480 nan 1.0000 0.0919
## 2 0.9294 nan 1.0000 0.0405
## 3 0.9028 nan 1.0000 -0.0113
## 4 0.8962 nan 1.0000 -0.0337
## 5 0.8764 nan 1.0000 0.0016
## 6 0.8690 nan 1.0000 -0.0271
## 7 0.9010 nan 1.0000 -0.0553
## 8 1.3792 nan 1.0000 -0.5590
## 9 1.3751 nan 1.0000 -0.0055
## 10 inf nan 1.0000 -0.0210
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 -0.0005
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0706 nan 1.0000 0.0802
## 2 1.0014 nan 1.0000 -0.0144
## 3 0.9670 nan 1.0000 -0.0027
## 4 0.9649 nan 1.0000 -0.0368
## 5 0.9514 nan 1.0000 -0.0211
## 6 0.9263 nan 1.0000 -0.0055
## 7 0.9259 nan 1.0000 -0.0213
## 8 0.9091 nan 1.0000 -0.0122
## 9 0.9157 nan 1.0000 -0.0324
## 10 0.8885 nan 1.0000 -0.0131
## 20 0.8847 nan 1.0000 -0.0498
## 40 0.8151 nan 1.0000 -0.0830
## 60 0.6858 nan 1.0000 -0.0414
## 80 0.7143 nan 1.0000 -0.0081
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0715 nan 1.0000 0.0771
## 2 0.9910 nan 1.0000 0.0196
## 3 0.9958 nan 1.0000 -0.0212
## 4 0.9909 nan 1.0000 -0.0134
## 5 1.0280 nan 1.0000 -0.0765
## 6 1.0700 nan 1.0000 -0.0880
## 7 1.1550 nan 1.0000 -0.1293
## 8 1.0896 nan 1.0000 0.0233
## 9 1.0933 nan 1.0000 -0.0358
## 10 1.0651 nan 1.0000 -0.0239
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0321 nan 1.0000 0.0854
## 2 0.9941 nan 1.0000 -0.0432
## 3 0.9880 nan 1.0000 -0.0320
## 4 0.9327 nan 1.0000 -0.0048
## 5 0.8900 nan 1.0000 -0.0159
## 6 0.8561 nan 1.0000 -0.0066
## 7 0.9030 nan 1.0000 -0.0808
## 8 0.9485 nan 1.0000 -0.0888
## 9 0.9278 nan 1.0000 -0.0117
## 10 0.9544 nan 1.0000 -0.0597
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0661 nan 1.0000 0.0452
## 2 1.0255 nan 1.0000 -0.0243
## 3 0.9304 nan 1.0000 0.0408
## 4 0.8814 nan 1.0000 -0.0097
## 5 0.8512 nan 1.0000 -0.0067
## 6 0.8787 nan 1.0000 -0.0644
## 7 0.8999 nan 1.0000 -0.0632
## 8 0.8778 nan 1.0000 -0.0090
## 9 0.9325 nan 1.0000 -0.1167
## 10 0.9023 nan 1.0000 -0.0079
## 20 inf nan 1.0000 -inf
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1077 nan 1.0000 0.0518
## 2 1.0641 nan 1.0000 -0.0350
## 3 1.0998 nan 1.0000 -0.0680
## 4 1.0450 nan 1.0000 -0.0134
## 5 0.9829 nan 1.0000 -0.0041
## 6 1.0006 nan 1.0000 -0.0671
## 7 0.9956 nan 1.0000 -0.0557
## 8 0.9968 nan 1.0000 -0.0686
## 9 0.9328 nan 1.0000 0.0124
## 10 0.9480 nan 1.0000 -0.0537
## 20 1.5518 nan 1.0000 -0.1314
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0001
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2903 nan 0.0010 0.0002
## 9 1.2900 nan 0.0010 0.0002
## 10 1.2896 nan 0.0010 0.0002
## 20 1.2856 nan 0.0010 0.0002
## 40 1.2783 nan 0.0010 0.0002
## 60 1.2715 nan 0.0010 0.0002
## 80 1.2647 nan 0.0010 0.0002
## 100 1.2579 nan 0.0010 0.0001
## 120 1.2513 nan 0.0010 0.0001
## 140 1.2451 nan 0.0010 0.0001
## 160 1.2390 nan 0.0010 0.0001
## 180 1.2333 nan 0.0010 0.0001
## 200 1.2275 nan 0.0010 0.0001
## 220 1.2218 nan 0.0010 0.0001
## 240 1.2167 nan 0.0010 0.0001
## 260 1.2114 nan 0.0010 0.0001
## 280 1.2063 nan 0.0010 0.0001
## 300 1.2015 nan 0.0010 0.0001
## 320 1.1969 nan 0.0010 0.0001
## 340 1.1922 nan 0.0010 0.0001
## 360 1.1877 nan 0.0010 0.0001
## 380 1.1833 nan 0.0010 0.0001
## 400 1.1792 nan 0.0010 0.0001
## 420 1.1751 nan 0.0010 0.0001
## 440 1.1709 nan 0.0010 0.0001
## 460 1.1669 nan 0.0010 0.0001
## 480 1.1630 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2914 nan 0.0010 0.0002
## 6 1.2910 nan 0.0010 0.0002
## 7 1.2906 nan 0.0010 0.0002
## 8 1.2902 nan 0.0010 0.0002
## 9 1.2898 nan 0.0010 0.0002
## 10 1.2894 nan 0.0010 0.0002
## 20 1.2855 nan 0.0010 0.0002
## 40 1.2781 nan 0.0010 0.0002
## 60 1.2708 nan 0.0010 0.0001
## 80 1.2640 nan 0.0010 0.0002
## 100 1.2575 nan 0.0010 0.0002
## 120 1.2510 nan 0.0010 0.0001
## 140 1.2448 nan 0.0010 0.0001
## 160 1.2389 nan 0.0010 0.0002
## 180 1.2329 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2905 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2860 nan 0.0010 0.0002
## 40 1.2785 nan 0.0010 0.0002
## 60 1.2715 nan 0.0010 0.0001
## 80 1.2647 nan 0.0010 0.0001
## 100 1.2580 nan 0.0010 0.0001
## 120 1.2515 nan 0.0010 0.0002
## 140 1.2452 nan 0.0010 0.0001
## 160 1.2389 nan 0.0010 0.0001
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## 380 1.1836 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0003
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2903 nan 0.0010 0.0002
## 7 1.2898 nan 0.0010 0.0002
## 8 1.2893 nan 0.0010 0.0002
## 9 1.2888 nan 0.0010 0.0002
## 10 1.2883 nan 0.0010 0.0003
## 20 1.2832 nan 0.0010 0.0002
## 40 1.2734 nan 0.0010 0.0002
## 60 1.2642 nan 0.0010 0.0002
## 80 1.2551 nan 0.0010 0.0002
## 100 1.2465 nan 0.0010 0.0002
## 120 1.2381 nan 0.0010 0.0002
## 140 1.2298 nan 0.0010 0.0002
## 160 1.2220 nan 0.0010 0.0002
## 180 1.2142 nan 0.0010 0.0002
## 200 1.2065 nan 0.0010 0.0002
## 220 1.1993 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0002
## 5 1.2907 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2897 nan 0.0010 0.0002
## 8 1.2891 nan 0.0010 0.0002
## 9 1.2886 nan 0.0010 0.0002
## 10 1.2881 nan 0.0010 0.0002
## 20 1.2832 nan 0.0010 0.0002
## 40 1.2735 nan 0.0010 0.0002
## 60 1.2640 nan 0.0010 0.0002
## 80 1.2550 nan 0.0010 0.0002
## 100 1.2461 nan 0.0010 0.0002
## 120 1.2375 nan 0.0010 0.0002
## 140 1.2292 nan 0.0010 0.0002
## 160 1.2211 nan 0.0010 0.0002
## 180 1.2132 nan 0.0010 0.0002
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## 220 1.1985 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0003
## 4 1.2913 nan 0.0010 0.0002
## 5 1.2908 nan 0.0010 0.0002
## 6 1.2903 nan 0.0010 0.0002
## 7 1.2899 nan 0.0010 0.0002
## 8 1.2894 nan 0.0010 0.0002
## 9 1.2889 nan 0.0010 0.0002
## 10 1.2884 nan 0.0010 0.0002
## 20 1.2834 nan 0.0010 0.0002
## 40 1.2736 nan 0.0010 0.0002
## 60 1.2641 nan 0.0010 0.0002
## 80 1.2551 nan 0.0010 0.0002
## 100 1.2463 nan 0.0010 0.0002
## 120 1.2376 nan 0.0010 0.0002
## 140 1.2295 nan 0.0010 0.0002
## 160 1.2213 nan 0.0010 0.0002
## 180 1.2134 nan 0.0010 0.0002
## 200 1.2060 nan 0.0010 0.0001
## 220 1.1985 nan 0.0010 0.0002
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## 280 1.1778 nan 0.0010 0.0001
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## 320 1.1651 nan 0.0010 0.0001
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## 380 1.1468 nan 0.0010 0.0001
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## 480 1.1190 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2927 nan 0.0010 0.0002
## 2 1.2921 nan 0.0010 0.0003
## 3 1.2915 nan 0.0010 0.0003
## 4 1.2909 nan 0.0010 0.0003
## 5 1.2903 nan 0.0010 0.0003
## 6 1.2897 nan 0.0010 0.0003
## 7 1.2891 nan 0.0010 0.0003
## 8 1.2886 nan 0.0010 0.0003
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## 10 1.2875 nan 0.0010 0.0003
## 20 1.2815 nan 0.0010 0.0003
## 40 1.2705 nan 0.0010 0.0002
## 60 1.2597 nan 0.0010 0.0002
## 80 1.2492 nan 0.0010 0.0003
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## 120 1.2294 nan 0.0010 0.0002
## 140 1.2198 nan 0.0010 0.0002
## 160 1.2105 nan 0.0010 0.0002
## 180 1.2014 nan 0.0010 0.0002
## 200 1.1926 nan 0.0010 0.0002
## 220 1.1843 nan 0.0010 0.0002
## 240 1.1762 nan 0.0010 0.0002
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## 280 1.1603 nan 0.0010 0.0001
## 300 1.1529 nan 0.0010 0.0001
## 320 1.1455 nan 0.0010 0.0001
## 340 1.1383 nan 0.0010 0.0002
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## 380 1.1247 nan 0.0010 0.0001
## 400 1.1183 nan 0.0010 0.0001
## 420 1.1119 nan 0.0010 0.0001
## 440 1.1055 nan 0.0010 0.0001
## 460 1.0996 nan 0.0010 0.0001
## 480 1.0937 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2927 nan 0.0010 0.0003
## 2 1.2921 nan 0.0010 0.0003
## 3 1.2915 nan 0.0010 0.0003
## 4 1.2909 nan 0.0010 0.0003
## 5 1.2903 nan 0.0010 0.0003
## 6 1.2898 nan 0.0010 0.0003
## 7 1.2892 nan 0.0010 0.0002
## 8 1.2886 nan 0.0010 0.0003
## 9 1.2881 nan 0.0010 0.0003
## 10 1.2875 nan 0.0010 0.0002
## 20 1.2818 nan 0.0010 0.0003
## 40 1.2704 nan 0.0010 0.0002
## 60 1.2595 nan 0.0010 0.0002
## 80 1.2489 nan 0.0010 0.0002
## 100 1.2386 nan 0.0010 0.0002
## 120 1.2286 nan 0.0010 0.0002
## 140 1.2193 nan 0.0010 0.0002
## 160 1.2099 nan 0.0010 0.0002
## 180 1.2012 nan 0.0010 0.0002
## 200 1.1925 nan 0.0010 0.0002
## 220 1.1840 nan 0.0010 0.0002
## 240 1.1758 nan 0.0010 0.0002
## 260 1.1680 nan 0.0010 0.0001
## 280 1.1602 nan 0.0010 0.0001
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## 320 1.1457 nan 0.0010 0.0002
## 340 1.1388 nan 0.0010 0.0001
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## 380 1.1252 nan 0.0010 0.0001
## 400 1.1188 nan 0.0010 0.0002
## 420 1.1125 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2927 nan 0.0010 0.0002
## 2 1.2921 nan 0.0010 0.0003
## 3 1.2915 nan 0.0010 0.0003
## 4 1.2908 nan 0.0010 0.0003
## 5 1.2902 nan 0.0010 0.0003
## 6 1.2896 nan 0.0010 0.0003
## 7 1.2890 nan 0.0010 0.0003
## 8 1.2885 nan 0.0010 0.0003
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## 10 1.2872 nan 0.0010 0.0003
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## 40 1.2701 nan 0.0010 0.0002
## 60 1.2592 nan 0.0010 0.0002
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## 140 1.2188 nan 0.0010 0.0002
## 160 1.2100 nan 0.0010 0.0002
## 180 1.2011 nan 0.0010 0.0001
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## 220 1.1842 nan 0.0010 0.0002
## 240 1.1759 nan 0.0010 0.0002
## 260 1.1677 nan 0.0010 0.0002
## 280 1.1600 nan 0.0010 0.0002
## 300 1.1525 nan 0.0010 0.0002
## 320 1.1454 nan 0.0010 0.0001
## 340 1.1383 nan 0.0010 0.0002
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## 380 1.1245 nan 0.0010 0.0001
## 400 1.1178 nan 0.0010 0.0001
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## 440 1.1052 nan 0.0010 0.0002
## 460 1.0993 nan 0.0010 0.0001
## 480 1.0935 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2575 nan 0.1000 0.0171
## 2 1.2271 nan 0.1000 0.0146
## 3 1.1978 nan 0.1000 0.0127
## 4 1.1756 nan 0.1000 0.0103
## 5 1.1523 nan 0.1000 0.0086
## 6 1.1321 nan 0.1000 0.0061
## 7 1.1154 nan 0.1000 0.0073
## 8 1.1017 nan 0.1000 0.0056
## 9 1.0885 nan 0.1000 0.0048
## 10 1.0746 nan 0.1000 0.0064
## 20 0.9860 nan 0.1000 0.0010
## 40 0.9036 nan 0.1000 -0.0010
## 60 0.8612 nan 0.1000 -0.0001
## 80 0.8307 nan 0.1000 -0.0003
## 100 0.8130 nan 0.1000 -0.0014
## 120 0.7960 nan 0.1000 -0.0009
## 140 0.7849 nan 0.1000 -0.0007
## 160 0.7737 nan 0.1000 -0.0013
## 180 0.7668 nan 0.1000 -0.0006
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## 220 0.7503 nan 0.1000 -0.0010
## 240 0.7439 nan 0.1000 -0.0008
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## 280 0.7301 nan 0.1000 -0.0010
## 300 0.7255 nan 0.1000 -0.0013
## 320 0.7181 nan 0.1000 -0.0008
## 340 0.7113 nan 0.1000 -0.0011
## 360 0.7050 nan 0.1000 -0.0001
## 380 0.7001 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2582 nan 0.1000 0.0181
## 2 1.2276 nan 0.1000 0.0144
## 3 1.1959 nan 0.1000 0.0117
## 4 1.1704 nan 0.1000 0.0105
## 5 1.1509 nan 0.1000 0.0064
## 6 1.1329 nan 0.1000 0.0067
## 7 1.1183 nan 0.1000 0.0051
## 8 1.1048 nan 0.1000 0.0053
## 9 1.0909 nan 0.1000 0.0033
## 10 1.0768 nan 0.1000 0.0068
## 20 0.9808 nan 0.1000 0.0036
## 40 0.8989 nan 0.1000 0.0003
## 60 0.8556 nan 0.1000 -0.0003
## 80 0.8320 nan 0.1000 -0.0010
## 100 0.8104 nan 0.1000 -0.0005
## 120 0.7949 nan 0.1000 0.0002
## 140 0.7837 nan 0.1000 -0.0011
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## 180 0.7642 nan 0.1000 -0.0005
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## 280 0.7283 nan 0.1000 -0.0008
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## 320 0.7170 nan 0.1000 -0.0006
## 340 0.7105 nan 0.1000 -0.0003
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## 380 0.6992 nan 0.1000 -0.0009
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## 480 0.6720 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2538 nan 0.1000 0.0143
## 2 1.2210 nan 0.1000 0.0155
## 3 1.1950 nan 0.1000 0.0106
## 4 1.1768 nan 0.1000 0.0080
## 5 1.1562 nan 0.1000 0.0090
## 6 1.1357 nan 0.1000 0.0082
## 7 1.1179 nan 0.1000 0.0065
## 8 1.1016 nan 0.1000 0.0069
## 9 1.0874 nan 0.1000 0.0063
## 10 1.0758 nan 0.1000 0.0043
## 20 0.9869 nan 0.1000 0.0019
## 40 0.8996 nan 0.1000 0.0006
## 60 0.8559 nan 0.1000 0.0003
## 80 0.8327 nan 0.1000 -0.0001
## 100 0.8127 nan 0.1000 -0.0012
## 120 0.7988 nan 0.1000 -0.0010
## 140 0.7847 nan 0.1000 -0.0002
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## 180 0.7656 nan 0.1000 -0.0014
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## 480 0.6769 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2467 nan 0.1000 0.0221
## 2 1.2026 nan 0.1000 0.0172
## 3 1.1676 nan 0.1000 0.0163
## 4 1.1333 nan 0.1000 0.0150
## 5 1.1094 nan 0.1000 0.0087
## 6 1.0855 nan 0.1000 0.0117
## 7 1.0660 nan 0.1000 0.0100
## 8 1.0446 nan 0.1000 0.0092
## 9 1.0306 nan 0.1000 0.0050
## 10 1.0147 nan 0.1000 0.0074
## 20 0.9222 nan 0.1000 0.0004
## 40 0.8268 nan 0.1000 -0.0003
## 60 0.7808 nan 0.1000 -0.0008
## 80 0.7475 nan 0.1000 -0.0010
## 100 0.7163 nan 0.1000 -0.0013
## 120 0.6904 nan 0.1000 -0.0005
## 140 0.6664 nan 0.1000 -0.0011
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## 180 0.6220 nan 0.1000 -0.0015
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2421 nan 0.1000 0.0209
## 2 1.2044 nan 0.1000 0.0163
## 3 1.1631 nan 0.1000 0.0165
## 4 1.1308 nan 0.1000 0.0145
## 5 1.1065 nan 0.1000 0.0116
## 6 1.0843 nan 0.1000 0.0107
## 7 1.0622 nan 0.1000 0.0091
## 8 1.0439 nan 0.1000 0.0065
## 9 1.0268 nan 0.1000 0.0072
## 10 1.0125 nan 0.1000 0.0045
## 20 0.9158 nan 0.1000 0.0005
## 40 0.8301 nan 0.1000 -0.0014
## 60 0.7842 nan 0.1000 -0.0009
## 80 0.7514 nan 0.1000 -0.0013
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## 120 0.6942 nan 0.1000 -0.0023
## 140 0.6701 nan 0.1000 -0.0009
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## 180 0.6223 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2469 nan 0.1000 0.0212
## 2 1.2043 nan 0.1000 0.0170
## 3 1.1702 nan 0.1000 0.0144
## 4 1.1396 nan 0.1000 0.0128
## 5 1.1135 nan 0.1000 0.0098
## 6 1.0842 nan 0.1000 0.0118
## 7 1.0637 nan 0.1000 0.0080
## 8 1.0454 nan 0.1000 0.0081
## 9 1.0265 nan 0.1000 0.0061
## 10 1.0075 nan 0.1000 0.0070
## 20 0.9138 nan 0.1000 0.0006
## 40 0.8305 nan 0.1000 -0.0022
## 60 0.7845 nan 0.1000 -0.0008
## 80 0.7534 nan 0.1000 -0.0017
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2369 nan 0.1000 0.0233
## 2 1.1870 nan 0.1000 0.0227
## 3 1.1471 nan 0.1000 0.0164
## 4 1.1096 nan 0.1000 0.0165
## 5 1.0800 nan 0.1000 0.0131
## 6 1.0584 nan 0.1000 0.0086
## 7 1.0410 nan 0.1000 0.0052
## 8 1.0151 nan 0.1000 0.0093
## 9 0.9933 nan 0.1000 0.0080
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## 40 0.7790 nan 0.1000 -0.0017
## 60 0.7071 nan 0.1000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2353 nan 0.1000 0.0263
## 2 1.1880 nan 0.1000 0.0212
## 3 1.1525 nan 0.1000 0.0164
## 4 1.1113 nan 0.1000 0.0169
## 5 1.0813 nan 0.1000 0.0116
## 6 1.0588 nan 0.1000 0.0104
## 7 1.0335 nan 0.1000 0.0105
## 8 1.0154 nan 0.1000 0.0064
## 9 0.9949 nan 0.1000 0.0076
## 10 0.9791 nan 0.1000 0.0052
## 20 0.8742 nan 0.1000 0.0007
## 40 0.7781 nan 0.1000 0.0000
## 60 0.7225 nan 0.1000 -0.0011
## 80 0.6761 nan 0.1000 -0.0013
## 100 0.6354 nan 0.1000 -0.0010
## 120 0.6018 nan 0.1000 -0.0009
## 140 0.5683 nan 0.1000 -0.0017
## 160 0.5408 nan 0.1000 -0.0006
## 180 0.5162 nan 0.1000 -0.0010
## 200 0.4927 nan 0.1000 -0.0006
## 220 0.4700 nan 0.1000 -0.0004
## 240 0.4522 nan 0.1000 -0.0016
## 260 0.4346 nan 0.1000 -0.0015
## 280 0.4154 nan 0.1000 -0.0015
## 300 0.3976 nan 0.1000 -0.0002
## 320 0.3832 nan 0.1000 -0.0014
## 340 0.3636 nan 0.1000 -0.0010
## 360 0.3500 nan 0.1000 -0.0015
## 380 0.3369 nan 0.1000 -0.0004
## 400 0.3270 nan 0.1000 -0.0012
## 420 0.3134 nan 0.1000 -0.0005
## 440 0.3015 nan 0.1000 -0.0003
## 460 0.2897 nan 0.1000 -0.0010
## 480 0.2793 nan 0.1000 -0.0007
## 500 0.2678 nan 0.1000 -0.0015
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2370 nan 0.1000 0.0257
## 2 1.1888 nan 0.1000 0.0210
## 3 1.1471 nan 0.1000 0.0163
## 4 1.1112 nan 0.1000 0.0163
## 5 1.0758 nan 0.1000 0.0115
## 6 1.0488 nan 0.1000 0.0080
## 7 1.0244 nan 0.1000 0.0078
## 8 1.0049 nan 0.1000 0.0082
## 9 0.9902 nan 0.1000 0.0060
## 10 0.9750 nan 0.1000 0.0057
## 20 0.8731 nan 0.1000 0.0014
## 40 0.7803 nan 0.1000 -0.0013
## 60 0.7214 nan 0.1000 -0.0008
## 80 0.6751 nan 0.1000 -0.0008
## 100 0.6343 nan 0.1000 -0.0019
## 120 0.5951 nan 0.1000 -0.0007
## 140 0.5679 nan 0.1000 -0.0003
## 160 0.5405 nan 0.1000 -0.0020
## 180 0.5132 nan 0.1000 -0.0012
## 200 0.4879 nan 0.1000 -0.0006
## 220 0.4641 nan 0.1000 -0.0017
## 240 0.4422 nan 0.1000 -0.0000
## 260 0.4265 nan 0.1000 -0.0017
## 280 0.4131 nan 0.1000 -0.0015
## 300 0.3960 nan 0.1000 -0.0013
## 320 0.3818 nan 0.1000 -0.0011
## 340 0.3696 nan 0.1000 -0.0006
## 360 0.3536 nan 0.1000 -0.0010
## 380 0.3372 nan 0.1000 -0.0007
## 400 0.3243 nan 0.1000 -0.0006
## 420 0.3110 nan 0.1000 -0.0009
## 440 0.2989 nan 0.1000 -0.0004
## 460 0.2869 nan 0.1000 -0.0014
## 480 0.2758 nan 0.1000 -0.0004
## 500 0.2647 nan 0.1000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2354 nan 0.2000 0.0338
## 2 1.1837 nan 0.2000 0.0210
## 3 1.1376 nan 0.2000 0.0194
## 4 1.1085 nan 0.2000 0.0134
## 5 1.0846 nan 0.2000 0.0100
## 6 1.0712 nan 0.2000 0.0008
## 7 1.0440 nan 0.2000 0.0064
## 8 1.0263 nan 0.2000 0.0073
## 9 1.0116 nan 0.2000 0.0048
## 10 0.9842 nan 0.2000 0.0103
## 20 0.9027 nan 0.2000 -0.0008
## 40 0.8334 nan 0.2000 0.0002
## 60 0.7998 nan 0.2000 -0.0014
## 80 0.7763 nan 0.2000 -0.0025
## 100 0.7610 nan 0.2000 -0.0007
## 120 0.7400 nan 0.2000 -0.0037
## 140 0.7263 nan 0.2000 -0.0010
## 160 0.7110 nan 0.2000 -0.0010
## 180 0.7026 nan 0.2000 -0.0015
## 200 0.6914 nan 0.2000 -0.0013
## 220 0.6802 nan 0.2000 -0.0020
## 240 0.6735 nan 0.2000 -0.0004
## 260 0.6639 nan 0.2000 -0.0014
## 280 0.6531 nan 0.2000 -0.0006
## 300 0.6450 nan 0.2000 -0.0035
## 320 0.6365 nan 0.2000 -0.0049
## 340 0.6285 nan 0.2000 -0.0029
## 360 0.6222 nan 0.2000 -0.0012
## 380 0.6162 nan 0.2000 -0.0011
## 400 0.6115 nan 0.2000 -0.0040
## 420 0.6057 nan 0.2000 -0.0028
## 440 0.6033 nan 0.2000 -0.0020
## 460 0.6006 nan 0.2000 -0.0034
## 480 0.5929 nan 0.2000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2197 nan 0.2000 0.0345
## 2 1.1730 nan 0.2000 0.0217
## 3 1.1366 nan 0.2000 0.0147
## 4 1.1058 nan 0.2000 0.0122
## 5 1.0780 nan 0.2000 0.0100
## 6 1.0523 nan 0.2000 0.0116
## 7 1.0350 nan 0.2000 0.0058
## 8 1.0163 nan 0.2000 0.0067
## 9 1.0022 nan 0.2000 0.0054
## 10 0.9816 nan 0.2000 0.0056
## 20 0.9011 nan 0.2000 -0.0002
## 40 0.8341 nan 0.2000 -0.0001
## 60 0.7917 nan 0.2000 -0.0007
## 80 0.7712 nan 0.2000 -0.0024
## 100 0.7569 nan 0.2000 -0.0037
## 120 0.7378 nan 0.2000 -0.0010
## 140 0.7273 nan 0.2000 -0.0020
## 160 0.7149 nan 0.2000 -0.0005
## 180 0.7031 nan 0.2000 -0.0013
## 200 0.6933 nan 0.2000 -0.0017
## 220 0.6810 nan 0.2000 -0.0016
## 240 0.6747 nan 0.2000 -0.0015
## 260 0.6640 nan 0.2000 -0.0021
## 280 0.6566 nan 0.2000 -0.0020
## 300 0.6441 nan 0.2000 -0.0017
## 320 0.6382 nan 0.2000 -0.0012
## 340 0.6350 nan 0.2000 -0.0019
## 360 0.6278 nan 0.2000 -0.0034
## 380 0.6194 nan 0.2000 -0.0003
## 400 0.6113 nan 0.2000 -0.0014
## 420 0.6055 nan 0.2000 -0.0038
## 440 0.5983 nan 0.2000 -0.0012
## 460 0.5929 nan 0.2000 -0.0009
## 480 0.5882 nan 0.2000 -0.0015
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2249 nan 0.2000 0.0320
## 2 1.1736 nan 0.2000 0.0208
## 3 1.1315 nan 0.2000 0.0196
## 4 1.1094 nan 0.2000 0.0057
## 5 1.0806 nan 0.2000 0.0127
## 6 1.0559 nan 0.2000 0.0082
## 7 1.0319 nan 0.2000 0.0085
## 8 1.0084 nan 0.2000 0.0077
## 9 0.9926 nan 0.2000 0.0037
## 10 0.9747 nan 0.2000 0.0073
## 20 0.8989 nan 0.2000 0.0014
## 40 0.8327 nan 0.2000 -0.0015
## 60 0.7986 nan 0.2000 -0.0017
## 80 0.7801 nan 0.2000 -0.0013
## 100 0.7611 nan 0.2000 -0.0016
## 120 0.7438 nan 0.2000 -0.0022
## 140 0.7293 nan 0.2000 -0.0019
## 160 0.7178 nan 0.2000 -0.0050
## 180 0.7078 nan 0.2000 -0.0026
## 200 0.6955 nan 0.2000 -0.0013
## 220 0.6866 nan 0.2000 -0.0029
## 240 0.6748 nan 0.2000 -0.0004
## 260 0.6656 nan 0.2000 -0.0018
## 280 0.6585 nan 0.2000 -0.0013
## 300 0.6501 nan 0.2000 -0.0029
## 320 0.6440 nan 0.2000 -0.0024
## 340 0.6351 nan 0.2000 -0.0019
## 360 0.6259 nan 0.2000 -0.0013
## 380 0.6212 nan 0.2000 -0.0012
## 400 0.6158 nan 0.2000 -0.0026
## 420 0.6092 nan 0.2000 -0.0027
## 440 0.6031 nan 0.2000 -0.0015
## 460 0.5973 nan 0.2000 -0.0019
## 480 0.5929 nan 0.2000 -0.0017
## 500 0.5886 nan 0.2000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2020 nan 0.2000 0.0380
## 2 1.1426 nan 0.2000 0.0243
## 3 1.0938 nan 0.2000 0.0264
## 4 1.0581 nan 0.2000 0.0133
## 5 1.0202 nan 0.2000 0.0162
## 6 0.9921 nan 0.2000 0.0088
## 7 0.9680 nan 0.2000 0.0086
## 8 0.9522 nan 0.2000 0.0028
## 9 0.9333 nan 0.2000 0.0062
## 10 0.9229 nan 0.2000 -0.0008
## 20 0.8281 nan 0.2000 -0.0012
## 40 0.7477 nan 0.2000 0.0001
## 60 0.6946 nan 0.2000 -0.0022
## 80 0.6523 nan 0.2000 -0.0032
## 100 0.6096 nan 0.2000 -0.0012
## 120 0.5744 nan 0.2000 -0.0024
## 140 0.5523 nan 0.2000 -0.0022
## 160 0.5250 nan 0.2000 -0.0015
## 180 0.5064 nan 0.2000 -0.0041
## 200 0.4790 nan 0.2000 -0.0015
## 220 0.4579 nan 0.2000 -0.0005
## 240 0.4374 nan 0.2000 -0.0015
## 260 0.4180 nan 0.2000 -0.0041
## 280 0.4054 nan 0.2000 -0.0002
## 300 0.3861 nan 0.2000 -0.0006
## 320 0.3607 nan 0.2000 -0.0044
## 340 0.3468 nan 0.2000 -0.0012
## 360 0.3324 nan 0.2000 -0.0006
## 380 0.3124 nan 0.2000 -0.0023
## 400 0.2953 nan 0.2000 -0.0013
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## 440 0.2691 nan 0.2000 -0.0015
## 460 0.2606 nan 0.2000 -0.0027
## 480 0.2513 nan 0.2000 -0.0021
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2000 nan 0.2000 0.0410
## 2 1.1422 nan 0.2000 0.0269
## 3 1.0919 nan 0.2000 0.0203
## 4 1.0477 nan 0.2000 0.0205
## 5 1.0213 nan 0.2000 0.0118
## 6 0.9945 nan 0.2000 0.0092
## 7 0.9746 nan 0.2000 0.0009
## 8 0.9529 nan 0.2000 0.0054
## 9 0.9389 nan 0.2000 0.0014
## 10 0.9246 nan 0.2000 0.0031
## 20 0.8326 nan 0.2000 -0.0010
## 40 0.7526 nan 0.2000 -0.0043
## 60 0.7024 nan 0.2000 -0.0011
## 80 0.6441 nan 0.2000 -0.0034
## 100 0.6097 nan 0.2000 -0.0023
## 120 0.5771 nan 0.2000 -0.0012
## 140 0.5443 nan 0.2000 -0.0037
## 160 0.5179 nan 0.2000 -0.0034
## 180 0.4920 nan 0.2000 -0.0033
## 200 0.4647 nan 0.2000 -0.0021
## 220 0.4426 nan 0.2000 -0.0027
## 240 0.4224 nan 0.2000 -0.0011
## 260 0.3989 nan 0.2000 -0.0019
## 280 0.3846 nan 0.2000 -0.0009
## 300 0.3667 nan 0.2000 -0.0000
## 320 0.3508 nan 0.2000 -0.0009
## 340 0.3269 nan 0.2000 -0.0020
## 360 0.3164 nan 0.2000 -0.0016
## 380 0.3020 nan 0.2000 -0.0001
## 400 0.2931 nan 0.2000 -0.0028
## 420 0.2822 nan 0.2000 -0.0021
## 440 0.2705 nan 0.2000 -0.0012
## 460 0.2613 nan 0.2000 -0.0012
## 480 0.2496 nan 0.2000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2068 nan 0.2000 0.0387
## 2 1.1403 nan 0.2000 0.0311
## 3 1.0861 nan 0.2000 0.0203
## 4 1.0507 nan 0.2000 0.0116
## 5 1.0127 nan 0.2000 0.0132
## 6 0.9903 nan 0.2000 0.0076
## 7 0.9634 nan 0.2000 0.0061
## 8 0.9418 nan 0.2000 0.0048
## 9 0.9218 nan 0.2000 0.0053
## 10 0.9105 nan 0.2000 0.0019
## 20 0.8340 nan 0.2000 -0.0022
## 40 0.7633 nan 0.2000 -0.0043
## 60 0.7243 nan 0.2000 -0.0045
## 80 0.6804 nan 0.2000 -0.0011
## 100 0.6423 nan 0.2000 -0.0031
## 120 0.6032 nan 0.2000 -0.0001
## 140 0.5777 nan 0.2000 -0.0025
## 160 0.5469 nan 0.2000 -0.0007
## 180 0.5190 nan 0.2000 -0.0017
## 200 0.4940 nan 0.2000 -0.0014
## 220 0.4722 nan 0.2000 -0.0014
## 240 0.4491 nan 0.2000 -0.0044
## 260 0.4285 nan 0.2000 -0.0029
## 280 0.4096 nan 0.2000 -0.0026
## 300 0.3907 nan 0.2000 -0.0014
## 320 0.3747 nan 0.2000 -0.0012
## 340 0.3634 nan 0.2000 -0.0017
## 360 0.3513 nan 0.2000 -0.0013
## 380 0.3376 nan 0.2000 -0.0002
## 400 0.3233 nan 0.2000 -0.0012
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## 460 0.2871 nan 0.2000 -0.0010
## 480 0.2735 nan 0.2000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1844 nan 0.2000 0.0484
## 2 1.1073 nan 0.2000 0.0296
## 3 1.0550 nan 0.2000 0.0221
## 4 1.0067 nan 0.2000 0.0206
## 5 0.9749 nan 0.2000 0.0049
## 6 0.9460 nan 0.2000 0.0105
## 7 0.9213 nan 0.2000 0.0051
## 8 0.9038 nan 0.2000 0.0027
## 9 0.8864 nan 0.2000 0.0021
## 10 0.8716 nan 0.2000 0.0009
## 20 0.7744 nan 0.2000 -0.0010
## 40 0.6769 nan 0.2000 -0.0003
## 60 0.6107 nan 0.2000 -0.0039
## 80 0.5489 nan 0.2000 -0.0032
## 100 0.4983 nan 0.2000 -0.0009
## 120 0.4536 nan 0.2000 -0.0031
## 140 0.4126 nan 0.2000 0.0000
## 160 0.3787 nan 0.2000 -0.0042
## 180 0.3489 nan 0.2000 -0.0032
## 200 0.3213 nan 0.2000 -0.0007
## 220 0.2958 nan 0.2000 -0.0007
## 240 0.2699 nan 0.2000 -0.0017
## 260 0.2526 nan 0.2000 -0.0016
## 280 0.2371 nan 0.2000 -0.0009
## 300 0.2240 nan 0.2000 -0.0024
## 320 0.2078 nan 0.2000 -0.0010
## 340 0.1950 nan 0.2000 -0.0008
## 360 0.1834 nan 0.2000 -0.0006
## 380 0.1731 nan 0.2000 -0.0008
## 400 0.1617 nan 0.2000 -0.0019
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## 460 0.1369 nan 0.2000 -0.0001
## 480 0.1281 nan 0.2000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1952 nan 0.2000 0.0501
## 2 1.1144 nan 0.2000 0.0357
## 3 1.0645 nan 0.2000 0.0167
## 4 1.0197 nan 0.2000 0.0180
## 5 0.9862 nan 0.2000 0.0117
## 6 0.9611 nan 0.2000 0.0041
## 7 0.9349 nan 0.2000 0.0076
## 8 0.9139 nan 0.2000 0.0071
## 9 0.8945 nan 0.2000 0.0053
## 10 0.8822 nan 0.2000 -0.0015
## 20 0.7834 nan 0.2000 -0.0013
## 40 0.6824 nan 0.2000 -0.0033
## 60 0.6101 nan 0.2000 -0.0036
## 80 0.5501 nan 0.2000 -0.0016
## 100 0.5070 nan 0.2000 -0.0060
## 120 0.4755 nan 0.2000 -0.0022
## 140 0.4374 nan 0.2000 -0.0025
## 160 0.4015 nan 0.2000 -0.0013
## 180 0.3722 nan 0.2000 -0.0009
## 200 0.3426 nan 0.2000 -0.0019
## 220 0.3145 nan 0.2000 -0.0009
## 240 0.2935 nan 0.2000 -0.0023
## 260 0.2746 nan 0.2000 -0.0009
## 280 0.2539 nan 0.2000 -0.0008
## 300 0.2372 nan 0.2000 -0.0005
## 320 0.2215 nan 0.2000 -0.0011
## 340 0.2071 nan 0.2000 -0.0010
## 360 0.1937 nan 0.2000 -0.0012
## 380 0.1792 nan 0.2000 -0.0006
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## 480 0.1329 nan 0.2000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1873 nan 0.2000 0.0490
## 2 1.1016 nan 0.2000 0.0430
## 3 1.0468 nan 0.2000 0.0188
## 4 1.0074 nan 0.2000 0.0175
## 5 0.9670 nan 0.2000 0.0156
## 6 0.9409 nan 0.2000 0.0093
## 7 0.9204 nan 0.2000 0.0008
## 8 0.9064 nan 0.2000 0.0023
## 9 0.8966 nan 0.2000 -0.0013
## 10 0.8772 nan 0.2000 0.0031
## 20 0.7797 nan 0.2000 0.0009
## 40 0.6723 nan 0.2000 -0.0033
## 60 0.6109 nan 0.2000 -0.0006
## 80 0.5510 nan 0.2000 -0.0041
## 100 0.5007 nan 0.2000 -0.0015
## 120 0.4580 nan 0.2000 -0.0018
## 140 0.4223 nan 0.2000 -0.0009
## 160 0.3878 nan 0.2000 -0.0023
## 180 0.3553 nan 0.2000 -0.0020
## 200 0.3299 nan 0.2000 -0.0017
## 220 0.2982 nan 0.2000 -0.0021
## 240 0.2771 nan 0.2000 -0.0009
## 260 0.2538 nan 0.2000 -0.0009
## 280 0.2377 nan 0.2000 -0.0021
## 300 0.2184 nan 0.2000 -0.0013
## 320 0.2042 nan 0.2000 -0.0010
## 340 0.1927 nan 0.2000 -0.0014
## 360 0.1787 nan 0.2000 -0.0007
## 380 0.1684 nan 0.2000 -0.0011
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## 480 0.1282 nan 0.2000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1999 nan 0.3000 0.0451
## 2 1.1333 nan 0.3000 0.0231
## 3 1.1014 nan 0.3000 0.0112
## 4 1.0626 nan 0.3000 0.0190
## 5 1.0299 nan 0.3000 0.0106
## 6 1.0061 nan 0.3000 0.0087
## 7 0.9836 nan 0.3000 0.0084
## 8 0.9671 nan 0.3000 0.0044
## 9 0.9483 nan 0.3000 0.0056
## 10 0.9391 nan 0.3000 0.0021
## 20 0.8645 nan 0.3000 -0.0014
## 40 0.8034 nan 0.3000 -0.0024
## 60 0.7740 nan 0.3000 -0.0047
## 80 0.7436 nan 0.3000 -0.0016
## 100 0.7226 nan 0.3000 -0.0035
## 120 0.7056 nan 0.3000 -0.0017
## 140 0.6962 nan 0.3000 -0.0043
## 160 0.6925 nan 0.3000 -0.0011
## 180 0.6759 nan 0.3000 -0.0031
## 200 0.6607 nan 0.3000 -0.0036
## 220 0.6486 nan 0.3000 -0.0017
## 240 0.6343 nan 0.3000 -0.0027
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## 280 0.6174 nan 0.3000 -0.0051
## 300 0.6125 nan 0.3000 -0.0006
## 320 0.6041 nan 0.3000 -0.0047
## 340 0.5937 nan 0.3000 -0.0008
## 360 0.5818 nan 0.3000 -0.0029
## 380 0.5763 nan 0.3000 -0.0012
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## 460 0.5537 nan 0.3000 -0.0051
## 480 0.5399 nan 0.3000 -0.0017
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1955 nan 0.3000 0.0448
## 2 1.1322 nan 0.3000 0.0321
## 3 1.0918 nan 0.3000 0.0166
## 4 1.0603 nan 0.3000 0.0127
## 5 1.0232 nan 0.3000 0.0173
## 6 1.0006 nan 0.3000 0.0041
## 7 0.9855 nan 0.3000 0.0018
## 8 0.9578 nan 0.3000 0.0126
## 9 0.9470 nan 0.3000 0.0021
## 10 0.9351 nan 0.3000 0.0019
## 20 0.8535 nan 0.3000 -0.0007
## 40 0.8010 nan 0.3000 -0.0046
## 60 0.7737 nan 0.3000 -0.0019
## 80 0.7488 nan 0.3000 -0.0021
## 100 0.7187 nan 0.3000 -0.0023
## 120 0.7044 nan 0.3000 0.0006
## 140 0.6855 nan 0.3000 -0.0041
## 160 0.6667 nan 0.3000 -0.0070
## 180 0.6574 nan 0.3000 -0.0018
## 200 0.6419 nan 0.3000 -0.0006
## 220 0.6313 nan 0.3000 -0.0026
## 240 0.6217 nan 0.3000 -0.0004
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## 280 0.6070 nan 0.3000 -0.0038
## 300 0.6025 nan 0.3000 -0.0037
## 320 0.5937 nan 0.3000 -0.0034
## 340 0.5880 nan 0.3000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1912 nan 0.3000 0.0448
## 2 1.1169 nan 0.3000 0.0288
## 3 1.0743 nan 0.3000 0.0180
## 4 1.0419 nan 0.3000 0.0132
## 5 1.0087 nan 0.3000 0.0086
## 6 0.9870 nan 0.3000 0.0076
## 7 0.9728 nan 0.3000 0.0038
## 8 0.9551 nan 0.3000 0.0072
## 9 0.9349 nan 0.3000 0.0083
## 10 0.9245 nan 0.3000 0.0002
## 20 0.8711 nan 0.3000 -0.0023
## 40 0.8125 nan 0.3000 -0.0013
## 60 0.7813 nan 0.3000 -0.0068
## 80 0.7623 nan 0.3000 -0.0016
## 100 0.7482 nan 0.3000 -0.0023
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## 140 0.7102 nan 0.3000 -0.0064
## 160 0.6950 nan 0.3000 -0.0050
## 180 0.6753 nan 0.3000 -0.0024
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## 220 0.6407 nan 0.3000 -0.0028
## 240 0.6334 nan 0.3000 -0.0032
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## 320 0.5965 nan 0.3000 -0.0037
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## 460 0.5424 nan 0.3000 -0.0036
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1612 nan 0.3000 0.0509
## 2 1.0874 nan 0.3000 0.0293
## 3 1.0309 nan 0.3000 0.0169
## 4 0.9827 nan 0.3000 0.0114
## 5 0.9648 nan 0.3000 0.0004
## 6 0.9359 nan 0.3000 0.0083
## 7 0.9178 nan 0.3000 0.0029
## 8 0.9015 nan 0.3000 0.0001
## 9 0.8874 nan 0.3000 -0.0022
## 10 0.8749 nan 0.3000 -0.0071
## 20 0.7988 nan 0.3000 0.0022
## 40 0.7233 nan 0.3000 -0.0045
## 60 0.6543 nan 0.3000 -0.0040
## 80 0.5880 nan 0.3000 0.0011
## 100 0.5523 nan 0.3000 0.0004
## 120 0.5051 nan 0.3000 -0.0053
## 140 0.4742 nan 0.3000 -0.0043
## 160 0.4347 nan 0.3000 -0.0050
## 180 0.4013 nan 0.3000 -0.0021
## 200 0.3780 nan 0.3000 -0.0020
## 220 0.3530 nan 0.3000 -0.0024
## 240 0.3305 nan 0.3000 -0.0018
## 260 0.3141 nan 0.3000 -0.0037
## 280 0.3009 nan 0.3000 -0.0032
## 300 0.2813 nan 0.3000 -0.0035
## 320 0.2648 nan 0.3000 -0.0004
## 340 0.2500 nan 0.3000 -0.0028
## 360 0.2306 nan 0.3000 -0.0008
## 380 0.2178 nan 0.3000 -0.0026
## 400 0.2098 nan 0.3000 -0.0010
## 420 0.1973 nan 0.3000 -0.0018
## 440 0.1863 nan 0.3000 -0.0012
## 460 0.1739 nan 0.3000 -0.0013
## 480 0.1642 nan 0.3000 -0.0009
## 500 0.1594 nan 0.3000 -0.0026
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1960 nan 0.3000 0.0401
## 2 1.1031 nan 0.3000 0.0409
## 3 1.0512 nan 0.3000 0.0186
## 4 1.0014 nan 0.3000 0.0202
## 5 0.9630 nan 0.3000 0.0083
## 6 0.9343 nan 0.3000 0.0060
## 7 0.9147 nan 0.3000 0.0021
## 8 0.8953 nan 0.3000 0.0011
## 9 0.8828 nan 0.3000 -0.0011
## 10 0.8718 nan 0.3000 -0.0009
## 20 0.7911 nan 0.3000 -0.0024
## 40 0.7140 nan 0.3000 -0.0021
## 60 0.6408 nan 0.3000 -0.0014
## 80 0.5924 nan 0.3000 -0.0038
## 100 0.5359 nan 0.3000 -0.0003
## 120 0.5017 nan 0.3000 -0.0048
## 140 0.4671 nan 0.3000 -0.0044
## 160 0.4311 nan 0.3000 -0.0040
## 180 0.4036 nan 0.3000 -0.0007
## 200 0.3728 nan 0.3000 -0.0017
## 220 0.3543 nan 0.3000 -0.0013
## 240 0.3338 nan 0.3000 -0.0036
## 260 0.3137 nan 0.3000 -0.0003
## 280 0.2981 nan 0.3000 -0.0027
## 300 0.2792 nan 0.3000 -0.0010
## 320 0.2622 nan 0.3000 -0.0029
## 340 0.2467 nan 0.3000 -0.0014
## 360 0.2330 nan 0.3000 -0.0006
## 380 0.2171 nan 0.3000 -0.0018
## 400 0.2075 nan 0.3000 -0.0019
## 420 0.1966 nan 0.3000 -0.0016
## 440 0.1892 nan 0.3000 -0.0012
## 460 0.1801 nan 0.3000 -0.0012
## 480 0.1695 nan 0.3000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1630 nan 0.3000 0.0578
## 2 1.0945 nan 0.3000 0.0194
## 3 1.0276 nan 0.3000 0.0335
## 4 0.9873 nan 0.3000 0.0142
## 5 0.9590 nan 0.3000 0.0069
## 6 0.9323 nan 0.3000 0.0090
## 7 0.9129 nan 0.3000 0.0057
## 8 0.8900 nan 0.3000 0.0071
## 9 0.8780 nan 0.3000 -0.0009
## 10 0.8650 nan 0.3000 0.0006
## 20 0.8029 nan 0.3000 -0.0019
## 40 0.7089 nan 0.3000 -0.0048
## 60 0.6497 nan 0.3000 -0.0041
## 80 0.5993 nan 0.3000 -0.0023
## 100 0.5562 nan 0.3000 -0.0021
## 120 0.5136 nan 0.3000 -0.0029
## 140 0.4841 nan 0.3000 -0.0028
## 160 0.4451 nan 0.3000 -0.0017
## 180 0.4203 nan 0.3000 -0.0024
## 200 0.3900 nan 0.3000 -0.0026
## 220 0.3630 nan 0.3000 -0.0050
## 240 0.3375 nan 0.3000 -0.0017
## 260 0.3228 nan 0.3000 -0.0032
## 280 0.3088 nan 0.3000 -0.0033
## 300 0.2928 nan 0.3000 -0.0022
## 320 0.2757 nan 0.3000 -0.0019
## 340 0.2611 nan 0.3000 -0.0018
## 360 0.2520 nan 0.3000 -0.0009
## 380 0.2355 nan 0.3000 -0.0011
## 400 0.2195 nan 0.3000 -0.0011
## 420 0.2081 nan 0.3000 -0.0021
## 440 0.2007 nan 0.3000 -0.0031
## 460 0.1937 nan 0.3000 -0.0008
## 480 0.1863 nan 0.3000 -0.0027
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1362 nan 0.3000 0.0683
## 2 1.0572 nan 0.3000 0.0293
## 3 1.0029 nan 0.3000 0.0228
## 4 0.9657 nan 0.3000 0.0058
## 5 0.9326 nan 0.3000 0.0071
## 6 0.9035 nan 0.3000 0.0047
## 7 0.8794 nan 0.3000 0.0006
## 8 0.8651 nan 0.3000 -0.0008
## 9 0.8498 nan 0.3000 -0.0028
## 10 0.8291 nan 0.3000 0.0029
## 20 0.7420 nan 0.3000 -0.0005
## 40 0.6418 nan 0.3000 -0.0020
## 60 0.5847 nan 0.3000 -0.0036
## 80 inf nan 0.3000 nan
## 100 inf nan 0.3000 nan
## 120 inf nan 0.3000 nan
## 140 inf nan 0.3000 nan
## 160 inf nan 0.3000 -0.0012
## 180 inf nan 0.3000 nan
## 200 inf nan 0.3000 nan
## 220 inf nan 0.3000 -0.0010
## 240 inf nan 0.3000 -0.0007
## 260 inf nan 0.3000 -0.0012
## 280 inf nan 0.3000 -0.0005
## 300 inf nan 0.3000 nan
## 320 inf nan 0.3000 -0.0003
## 340 inf nan 0.3000 nan
## 360 inf nan 0.3000 -0.0002
## 380 inf nan 0.3000 nan
## 400 inf nan 0.3000 -0.0004
## 420 inf nan 0.3000 nan
## 440 inf nan 0.3000 nan
## 460 inf nan 0.3000 nan
## 480 inf nan 0.3000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1487 nan 0.3000 0.0626
## 2 1.0614 nan 0.3000 0.0384
## 3 1.0079 nan 0.3000 0.0159
## 4 0.9499 nan 0.3000 0.0262
## 5 0.9183 nan 0.3000 0.0057
## 6 0.8823 nan 0.3000 0.0116
## 7 0.8564 nan 0.3000 0.0052
## 8 0.8459 nan 0.3000 -0.0042
## 9 0.8286 nan 0.3000 0.0004
## 10 0.8165 nan 0.3000 -0.0023
## 20 0.7389 nan 0.3000 -0.0042
## 40 0.6231 nan 0.3000 0.0010
## 60 0.5371 nan 0.3000 -0.0061
## 80 0.4647 nan 0.3000 -0.0033
## 100 0.4077 nan 0.3000 -0.0040
## 120 0.3523 nan 0.3000 -0.0029
## 140 0.3169 nan 0.3000 -0.0051
## 160 0.2807 nan 0.3000 -0.0028
## 180 0.2518 nan 0.3000 -0.0033
## 200 0.2214 nan 0.3000 -0.0011
## 220 0.1977 nan 0.3000 -0.0024
## 240 0.1770 nan 0.3000 0.0003
## 260 0.1621 nan 0.3000 -0.0002
## 280 0.1476 nan 0.3000 -0.0012
## 300 0.1371 nan 0.3000 -0.0013
## 320 0.1242 nan 0.3000 -0.0015
## 340 0.1133 nan 0.3000 -0.0007
## 360 0.1046 nan 0.3000 -0.0009
## 380 0.0933 nan 0.3000 0.0000
## 400 0.0863 nan 0.3000 -0.0009
## 420 0.0806 nan 0.3000 -0.0010
## 440 0.0742 nan 0.3000 -0.0010
## 460 0.0670 nan 0.3000 -0.0002
## 480 0.0619 nan 0.3000 -0.0006
## 500 0.0567 nan 0.3000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1462 nan 0.3000 0.0677
## 2 1.0644 nan 0.3000 0.0326
## 3 1.0106 nan 0.3000 0.0103
## 4 0.9629 nan 0.3000 0.0061
## 5 0.9320 nan 0.3000 0.0042
## 6 0.9019 nan 0.3000 0.0027
## 7 0.8749 nan 0.3000 0.0044
## 8 0.8541 nan 0.3000 -0.0034
## 9 0.8345 nan 0.3000 -0.0012
## 10 0.8245 nan 0.3000 -0.0030
## 20 0.7302 nan 0.3000 -0.0058
## 40 0.6255 nan 0.3000 -0.0085
## 60 0.5372 nan 0.3000 -0.0065
## 80 0.4601 nan 0.3000 -0.0080
## 100 0.4068 nan 0.3000 -0.0050
## 120 0.3663 nan 0.3000 -0.0025
## 140 0.3216 nan 0.3000 -0.0033
## 160 0.2862 nan 0.3000 -0.0024
## 180 0.2572 nan 0.3000 -0.0012
## 200 0.2305 nan 0.3000 -0.0014
## 220 0.2076 nan 0.3000 -0.0009
## 240 0.1907 nan 0.3000 -0.0009
## 260 0.1731 nan 0.3000 -0.0017
## 280 0.1550 nan 0.3000 -0.0009
## 300 0.1423 nan 0.3000 -0.0018
## 320 0.1295 nan 0.3000 -0.0011
## 340 0.1179 nan 0.3000 -0.0014
## 360 0.1061 nan 0.3000 -0.0009
## 380 0.0961 nan 0.3000 -0.0005
## 400 0.0884 nan 0.3000 -0.0010
## 420 0.0821 nan 0.3000 -0.0010
## 440 0.0752 nan 0.3000 -0.0003
## 460 0.0694 nan 0.3000 -0.0004
## 480 0.0646 nan 0.3000 -0.0006
## 500 0.0588 nan 0.3000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1571 nan 0.5000 0.0587
## 2 1.0963 nan 0.5000 0.0265
## 3 1.0360 nan 0.5000 0.0213
## 4 0.9997 nan 0.5000 0.0133
## 5 0.9651 nan 0.5000 0.0127
## 6 0.9445 nan 0.5000 0.0020
## 7 0.9343 nan 0.5000 -0.0015
## 8 0.9159 nan 0.5000 0.0031
## 9 0.8996 nan 0.5000 0.0055
## 10 0.8965 nan 0.5000 -0.0102
## 20 0.8395 nan 0.5000 -0.0027
## 40 0.7920 nan 0.5000 -0.0055
## 60 0.7492 nan 0.5000 -0.0070
## 80 0.7233 nan 0.5000 -0.0124
## 100 0.7035 nan 0.5000 -0.0047
## 120 0.6614 nan 0.5000 -0.0043
## 140 0.6547 nan 0.5000 -0.0066
## 160 0.6336 nan 0.5000 -0.0010
## 180 0.6120 nan 0.5000 -0.0047
## 200 0.5935 nan 0.5000 -0.0047
## 220 0.5912 nan 0.5000 -0.0065
## 240 0.5767 nan 0.5000 -0.0086
## 260 0.5702 nan 0.5000 -0.0080
## 280 0.5580 nan 0.5000 -0.0044
## 300 0.5416 nan 0.5000 -0.0026
## 320 0.5354 nan 0.5000 -0.0063
## 340 0.5264 nan 0.5000 -0.0027
## 360 0.5099 nan 0.5000 -0.0064
## 380 0.5020 nan 0.5000 -0.0018
## 400 0.4872 nan 0.5000 -0.0056
## 420 0.4760 nan 0.5000 -0.0002
## 440 0.4740 nan 0.5000 -0.0081
## 460 0.4706 nan 0.5000 -0.0097
## 480 0.4592 nan 0.5000 -0.0020
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1472 nan 0.5000 0.0707
## 2 1.0811 nan 0.5000 0.0250
## 3 1.0412 nan 0.5000 0.0092
## 4 0.9937 nan 0.5000 0.0218
## 5 0.9656 nan 0.5000 0.0131
## 6 0.9530 nan 0.5000 0.0014
## 7 0.9237 nan 0.5000 0.0116
## 8 0.9137 nan 0.5000 -0.0013
## 9 0.8901 nan 0.5000 0.0049
## 10 0.8795 nan 0.5000 -0.0016
## 20 0.8274 nan 0.5000 -0.0071
## 40 0.7884 nan 0.5000 -0.0105
## 60 0.7549 nan 0.5000 -0.0047
## 80 0.7236 nan 0.5000 -0.0023
## 100 0.6889 nan 0.5000 0.0009
## 120 0.6611 nan 0.5000 -0.0007
## 140 0.6373 nan 0.5000 -0.0056
## 160 0.6192 nan 0.5000 -0.0004
## 180 0.6008 nan 0.5000 -0.0048
## 200 0.5891 nan 0.5000 -0.0028
## 220 0.5850 nan 0.5000 -0.0017
## 240 0.5664 nan 0.5000 -0.0032
## 260 0.5565 nan 0.5000 -0.0056
## 280 0.5397 nan 0.5000 -0.0009
## 300 0.5310 nan 0.5000 -0.0064
## 320 0.5248 nan 0.5000 -0.0086
## 340 0.5127 nan 0.5000 -0.0033
## 360 0.5088 nan 0.5000 -0.0038
## 380 0.4918 nan 0.5000 -0.0042
## 400 0.4880 nan 0.5000 -0.0025
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## 440 0.4690 nan 0.5000 -0.0032
## 460 0.4635 nan 0.5000 -0.0034
## 480 0.4550 nan 0.5000 -0.0026
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1498 nan 0.5000 0.0706
## 2 1.0935 nan 0.5000 0.0260
## 3 1.0457 nan 0.5000 0.0140
## 4 0.9898 nan 0.5000 0.0226
## 5 0.9635 nan 0.5000 0.0048
## 6 0.9534 nan 0.5000 -0.0019
## 7 0.9408 nan 0.5000 -0.0008
## 8 0.9310 nan 0.5000 -0.0035
## 9 0.9157 nan 0.5000 0.0011
## 10 0.8956 nan 0.5000 0.0022
## 20 0.8464 nan 0.5000 -0.0058
## 40 0.7839 nan 0.5000 -0.0037
## 60 0.7675 nan 0.5000 -0.0120
## 80 0.7425 nan 0.5000 -0.0148
## 100 0.7098 nan 0.5000 -0.0097
## 120 0.6840 nan 0.5000 -0.0014
## 140 0.6730 nan 0.5000 -0.0081
## 160 0.6447 nan 0.5000 -0.0022
## 180 0.6263 nan 0.5000 -0.0009
## 200 0.6069 nan 0.5000 -0.0055
## 220 0.5902 nan 0.5000 -0.0045
## 240 0.5834 nan 0.5000 -0.0032
## 260 0.5819 nan 0.5000 -0.0067
## 280 0.5649 nan 0.5000 -0.0063
## 300 0.5486 nan 0.5000 -0.0083
## 320 0.5327 nan 0.5000 -0.0049
## 340 0.5203 nan 0.5000 -0.0010
## 360 0.5098 nan 0.5000 -0.0023
## 380 0.5077 nan 0.5000 -0.0023
## 400 0.4974 nan 0.5000 -0.0096
## 420 0.4955 nan 0.5000 -0.0118
## 440 0.4769 nan 0.5000 -0.0103
## 460 0.4785 nan 0.5000 -0.0057
## 480 0.4675 nan 0.5000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1131 nan 0.5000 0.0635
## 2 1.0313 nan 0.5000 0.0268
## 3 0.9718 nan 0.5000 0.0127
## 4 0.9176 nan 0.5000 0.0159
## 5 0.9070 nan 0.5000 -0.0072
## 6 0.8897 nan 0.5000 -0.0062
## 7 0.8737 nan 0.5000 -0.0047
## 8 0.8624 nan 0.5000 -0.0045
## 9 0.8445 nan 0.5000 0.0037
## 10 0.8312 nan 0.5000 -0.0013
## 20 0.7654 nan 0.5000 -0.0105
## 40 0.6675 nan 0.5000 -0.0112
## 60 0.5706 nan 0.5000 -0.0017
## 80 0.5204 nan 0.5000 -0.0102
## 100 0.4792 nan 0.5000 0.0010
## 120 0.4480 nan 0.5000 -0.0138
## 140 0.8232 nan 0.5000 -0.0065
## 160 0.8040 nan 0.5000 -0.0016
## 180 0.7836 nan 0.5000 0.0018
## 200 0.7721 nan 0.5000 -0.0019
## 220 0.7683 nan 0.5000 -0.0007
## 240 0.7577 nan 0.5000 -0.0044
## 260 0.7429 nan 0.5000 -0.0076
## 280 0.7315 nan 0.5000 -0.0005
## 300 0.9335 nan 0.5000 -0.0010
## 320 0.9192 nan 0.5000 -0.0073
## 340 0.9069 nan 0.5000 -0.0047
## 360 0.9023 nan 0.5000 -0.0057
## 380 0.9039 nan 0.5000 -0.0031
## 400 0.8961 nan 0.5000 -0.0068
## 420 0.8741 nan 0.5000 -0.0010
## 440 0.8966 nan 0.5000 -0.0003
## 460 0.8662 nan 0.5000 -0.0030
## 480 0.8665 nan 0.5000 -0.0024
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1051 nan 0.5000 0.0783
## 2 1.0079 nan 0.5000 0.0442
## 3 0.9442 nan 0.5000 0.0127
## 4 0.9114 nan 0.5000 0.0076
## 5 0.8681 nan 0.5000 0.0067
## 6 0.8583 nan 0.5000 -0.0108
## 7 0.8369 nan 0.5000 0.0005
## 8 0.8301 nan 0.5000 -0.0045
## 9 0.8222 nan 0.5000 -0.0072
## 10 0.8257 nan 0.5000 -0.0224
## 20 0.7441 nan 0.5000 -0.0070
## 40 0.6444 nan 0.5000 -0.0123
## 60 0.5753 nan 0.5000 -0.0078
## 80 0.5084 nan 0.5000 -0.0076
## 100 0.4597 nan 0.5000 -0.0061
## 120 0.4150 nan 0.5000 0.0009
## 140 0.3872 nan 0.5000 -0.0085
## 160 0.3570 nan 0.5000 -0.0021
## 180 0.3170 nan 0.5000 -0.0044
## 200 0.3004 nan 0.5000 -0.0017
## 220 0.2764 nan 0.5000 -0.0058
## 240 0.2756 nan 0.5000 -0.0003
## 260 0.2134 nan 0.5000 -0.0035
## 280 0.2047 nan 0.5000 -0.0040
## 300 0.1888 nan 0.5000 -0.0042
## 320 0.1730 nan 0.5000 -0.0039
## 340 0.1553 nan 0.5000 -0.0014
## 360 0.1432 nan 0.5000 -0.0007
## 380 0.1297 nan 0.5000 -0.0022
## 400 0.1167 nan 0.5000 -0.0009
## 420 0.1087 nan 0.5000 -0.0016
## 440 0.0978 nan 0.5000 -0.0004
## 460 0.0891 nan 0.5000 -0.0003
## 480 0.0851 nan 0.5000 -0.0035
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1104 nan 0.5000 0.0927
## 2 0.9943 nan 0.5000 0.0523
## 3 0.9455 nan 0.5000 0.0126
## 4 0.9120 nan 0.5000 0.0048
## 5 0.8725 nan 0.5000 0.0154
## 6 0.8615 nan 0.5000 -0.0061
## 7 0.8384 nan 0.5000 -0.0029
## 8 0.8270 nan 0.5000 -0.0040
## 9 0.8188 nan 0.5000 -0.0054
## 10 0.8098 nan 0.5000 -0.0074
## 20 0.7256 nan 0.5000 -0.0016
## 40 0.6514 nan 0.5000 -0.0068
## 60 0.5931 nan 0.5000 -0.0106
## 80 0.5179 nan 0.5000 -0.0055
## 100 0.4668 nan 0.5000 -0.0156
## 120 0.4269 nan 0.5000 -0.0053
## 140 0.3882 nan 0.5000 -0.0027
## 160 0.3583 nan 0.5000 -0.0053
## 180 0.3238 nan 0.5000 -0.0012
## 200 0.2957 nan 0.5000 -0.0101
## 220 0.2626 nan 0.5000 -0.0031
## 240 0.2237 nan 0.5000 -0.0014
## 260 0.2059 nan 0.5000 -0.0035
## 280 0.1908 nan 0.5000 -0.0012
## 300 0.1719 nan 0.5000 -0.0016
## 320 0.1606 nan 0.5000 -0.0007
## 340 0.1469 nan 0.5000 -0.0030
## 360 0.1345 nan 0.5000 -0.0023
## 380 0.1226 nan 0.5000 -0.0004
## 400 0.1113 nan 0.5000 -0.0002
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## 460 0.0907 nan 0.5000 -0.0019
## 480 0.0831 nan 0.5000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1080 nan 0.5000 0.0810
## 2 0.9793 nan 0.5000 0.0500
## 3 0.9336 nan 0.5000 0.0120
## 4 0.9073 nan 0.5000 -0.0009
## 5 0.8733 nan 0.5000 0.0027
## 6 0.8540 nan 0.5000 -0.0085
## 7 0.8398 nan 0.5000 -0.0124
## 8 0.8216 nan 0.5000 -0.0061
## 9 0.8002 nan 0.5000 -0.0013
## 10 0.7782 nan 0.5000 -0.0015
## 20 0.6881 nan 0.5000 -0.0038
## 40 0.5412 nan 0.5000 -0.0017
## 60 0.4464 nan 0.5000 -0.0058
## 80 54.9539 nan 0.5000 -0.0060
## 100 54.4749 nan 0.5000 -0.0283
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1042 nan 0.5000 0.0758
## 2 1.0047 nan 0.5000 0.0350
## 3 0.9314 nan 0.5000 0.0301
## 4 0.9069 nan 0.5000 -0.0061
## 5 0.8869 nan 0.5000 -0.0023
## 6 0.8692 nan 0.5000 -0.0171
## 7 0.8497 nan 0.5000 -0.0065
## 8 0.8350 nan 0.5000 -0.0168
## 9 0.8112 nan 0.5000 0.0066
## 10 0.8024 nan 0.5000 -0.0152
## 20 0.6933 nan 0.5000 -0.0093
## 40 0.6224 nan 0.5000 -0.0513
## 60 0.4877 nan 0.5000 -0.0031
## 80 0.3552 nan 0.5000 -0.0053
## 100 0.2832 nan 0.5000 -0.0037
## 120 0.2244 nan 0.5000 -0.0031
## 140 0.1852 nan 0.5000 -0.0019
## 160 0.1476 nan 0.5000 -0.0021
## 180 0.1225 nan 0.5000 -0.0009
## 200 0.1062 nan 0.5000 -0.0036
## 220 0.0916 nan 0.5000 -0.0006
## 240 0.0770 nan 0.5000 -0.0013
## 260 0.0655 nan 0.5000 -0.0007
## 280 0.0571 nan 0.5000 -0.0009
## 300 0.0489 nan 0.5000 -0.0002
## 320 0.0415 nan 0.5000 -0.0005
## 340 0.0372 nan 0.5000 -0.0005
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## 380 0.0285 nan 0.5000 -0.0011
## 400 0.0265 nan 0.5000 -0.0004
## 420 0.0228 nan 0.5000 -0.0003
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## 480 0.0162 nan 0.5000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0841 nan 0.5000 0.0935
## 2 0.9820 nan 0.5000 0.0322
## 3 0.9343 nan 0.5000 0.0058
## 4 0.8762 nan 0.5000 0.0160
## 5 0.8500 nan 0.5000 -0.0042
## 6 0.8284 nan 0.5000 -0.0023
## 7 0.8143 nan 0.5000 -0.0065
## 8 0.8059 nan 0.5000 -0.0144
## 9 0.8028 nan 0.5000 -0.0240
## 10 0.7943 nan 0.5000 -0.0086
## 20 0.7106 nan 0.5000 -0.0020
## 40 0.5763 nan 0.5000 -0.0196
## 60 0.4514 nan 0.5000 -0.0098
## 80 inf nan 0.5000 nan
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## 500 inf nan 0.5000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1208 nan 1.0000 0.0704
## 2 1.0504 nan 1.0000 0.0135
## 3 0.9880 nan 1.0000 0.0062
## 4 0.9716 nan 1.0000 -0.0011
## 5 0.9603 nan 1.0000 -0.0101
## 6 0.9494 nan 1.0000 -0.0157
## 7 0.9292 nan 1.0000 0.0019
## 8 0.9209 nan 1.0000 -0.0079
## 9 0.8966 nan 1.0000 0.0061
## 10 0.8980 nan 1.0000 -0.0133
## 20 0.9293 nan 1.0000 -0.0586
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1280 nan 1.0000 0.0366
## 2 1.0392 nan 1.0000 0.0377
## 3 1.0049 nan 1.0000 -0.0145
## 4 0.9814 nan 1.0000 -0.0044
## 5 0.9690 nan 1.0000 -0.0020
## 6 0.9581 nan 1.0000 -0.0156
## 7 0.9467 nan 1.0000 -0.0124
## 8 0.9352 nan 1.0000 -0.0084
## 9 0.9279 nan 1.0000 -0.0058
## 10 0.9258 nan 1.0000 -0.0135
## 20 1.0397 nan 1.0000 -0.0151
## 40 23256371.2857 nan 1.0000 0.0002
## 60 23256371.2557 nan 1.0000 -0.0146
## 80 23256371.2597 nan 1.0000 -0.0008
## 100 23256371.2482 nan 1.0000 -0.0019
## 120 23256371.2573 nan 1.0000 -0.0359
## 140 23256371.2422 nan 1.0000 -0.0018
## 160 23256371.2278 nan 1.0000 0.0053
## 180 23256371.2150 nan 1.0000 -0.0004
## 200 28769748.4736 nan 1.0000 -0.0165
## 220 28769748.3704 nan 1.0000 0.0001
## 240 28769748.3731 nan 1.0000 -0.0003
## 260 28769748.3678 nan 1.0000 -0.0005
## 280 28769748.3430 nan 1.0000 -0.0094
## 300 28769748.3365 nan 1.0000 0.0011
## 320 28769748.6136 nan 1.0000 -0.0038
## 340 874024405801.1039 nan 1.0000 -0.0022
## 360 874024405801.1002 nan 1.0000 -0.0017
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 874024413384.2057 nan 1.0000 inf
## 460 874024413383.8336 nan 1.0000 -0.0001
## 480 874024413383.4625 nan 1.0000 0.0231
## 500 4732586512793954.0000 nan 1.0000 -2147670342532169.5000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1151 nan 1.0000 0.0893
## 2 1.0254 nan 1.0000 0.0430
## 3 0.9618 nan 1.0000 0.0166
## 4 0.9160 nan 1.0000 0.0202
## 5 0.9013 nan 1.0000 -0.0117
## 6 0.8961 nan 1.0000 -0.0098
## 7 0.9051 nan 1.0000 -0.0250
## 8 0.8823 nan 1.0000 0.0092
## 9 0.8822 nan 1.0000 -0.0107
## 10 0.8602 nan 1.0000 0.0072
## 20 0.8209 nan 1.0000 -0.0013
## 40 952062.3927 nan 1.0000 -0.0025
## 60 952062.3793 nan 1.0000 -0.0165
## 80 952077.4157 nan 1.0000 -0.0022
## 100 952346.0480 nan 1.0000 0.0066
## 120 952345.9997 nan 1.0000 0.0024
## 140 952347.6261 nan 1.0000 -0.0033
## 160 952347.6136 nan 1.0000 -0.0006
## 180 952347.5544 nan 1.0000 0.0094
## 200 952347.5223 nan 1.0000 -0.0043
## 220 952347.4048 nan 1.0000 0.0014
## 240 952347.3206 nan 1.0000 0.0032
## 260 952347.2438 nan 1.0000 0.0039
## 280 952347.2009 nan 1.0000 0.0018
## 300 952347.1606 nan 1.0000 -0.0034
## 320 952347.0706 nan 1.0000 0.0025
## 340 952347.0192 nan 1.0000 0.0015
## 360 952346.9572 nan 1.0000 0.0012
## 380 952346.8309 nan 1.0000 0.0025
## 400 952346.7729 nan 1.0000 0.0007
## 420 312840496051886.6875 nan 1.0000 0.0030
## 440 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0847 nan 1.0000 0.0661
## 2 1.0126 nan 1.0000 -0.0412
## 3 0.9179 nan 1.0000 0.0169
## 4 0.8921 nan 1.0000 -0.0028
## 5 1.0599 nan 1.0000 -0.1921
## 6 1.0395 nan 1.0000 -0.0079
## 7 1.0608 nan 1.0000 -0.0450
## 8 1.0376 nan 1.0000 -0.0116
## 9 1.0237 nan 1.0000 -0.0062
## 10 1.0172 nan 1.0000 -0.0067
## 20 0.9849 nan 1.0000 -0.0152
## 40 0.9716 nan 1.0000 -0.0085
## 60 inf nan 1.0000 -inf
## 80 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0517 nan 1.0000 0.0732
## 2 0.9675 nan 1.0000 0.0363
## 3 0.9510 nan 1.0000 -0.0221
## 4 1.0101 nan 1.0000 -0.0869
## 5 0.9791 nan 1.0000 -0.0187
## 6 6.7741 nan 1.0000 -2.9161
## 7 6.7856 nan 1.0000 -0.0720
## 8 6.7951 nan 1.0000 -0.0589
## 9 6.8549 nan 1.0000 -0.0984
## 10 6.8603 nan 1.0000 -0.0480
## 20 6.8397 nan 1.0000 -0.0470
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0560 nan 1.0000 0.0860
## 2 0.9959 nan 1.0000 -0.0277
## 3 0.9565 nan 1.0000 0.0010
## 4 0.9289 nan 1.0000 -0.0117
## 5 0.9181 nan 1.0000 -0.0338
## 6 0.9019 nan 1.0000 -0.0119
## 7 0.9273 nan 1.0000 -0.0647
## 8 0.9328 nan 1.0000 -0.0372
## 9 0.9514 nan 1.0000 -0.0527
## 10 0.9315 nan 1.0000 0.0023
## 20 1.0462 nan 1.0000 -0.0322
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9950 nan 1.0000 0.1372
## 2 0.9698 nan 1.0000 -0.0352
## 3 0.9589 nan 1.0000 -0.0389
## 4 0.9459 nan 1.0000 -0.0200
## 5 1.0035 nan 1.0000 -0.1079
## 6 1.0742 nan 1.0000 -0.1186
## 7 1.0606 nan 1.0000 -0.0598
## 8 1.0607 nan 1.0000 -0.0486
## 9 1.0112 nan 1.0000 -0.0258
## 10 0.9946 nan 1.0000 -0.0378
## 20 1408633044.0288 nan 1.0000 -0.7810
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0220 nan 1.0000 0.1078
## 2 0.9493 nan 1.0000 0.0062
## 3 0.9002 nan 1.0000 0.0156
## 4 0.8788 nan 1.0000 -0.0182
## 5 0.8696 nan 1.0000 -0.0217
## 6 0.8449 nan 1.0000 -0.0095
## 7 0.8710 nan 1.0000 -0.0624
## 8 0.8910 nan 1.0000 -0.0665
## 9 0.8741 nan 1.0000 -0.0165
## 10 0.8750 nan 1.0000 -0.0418
## 20 0.8579 nan 1.0000 -0.0707
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0349 nan 1.0000 0.0901
## 2 0.9304 nan 1.0000 0.0240
## 3 0.9240 nan 1.0000 -0.0290
## 4 0.9055 nan 1.0000 -0.0164
## 5 0.8842 nan 1.0000 -0.0079
## 6 0.9083 nan 1.0000 -0.0583
## 7 1.3992 nan 1.0000 -0.3016
## 8 1.3855 nan 1.0000 -0.0164
## 9 1.3917 nan 1.0000 -0.0354
## 10 1.0221 nan 1.0000 0.1536
## 20 0.8629 nan 1.0000 -0.0072
## 40 29217001642.9100 nan 1.0000 -0.0001
## 60 151930677290.9368 nan 1.0000 0.0034
## 80 151930677290.9258 nan 1.0000 -0.0002
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
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## 480 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
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## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
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## 480 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0001
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2900 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2789 nan 0.0010 0.0001
## 60 1.2720 nan 0.0010 0.0001
## 80 1.2653 nan 0.0010 0.0002
## 100 1.2588 nan 0.0010 0.0001
## 120 1.2524 nan 0.0010 0.0001
## 140 1.2465 nan 0.0010 0.0001
## 160 1.2406 nan 0.0010 0.0001
## 180 1.2351 nan 0.0010 0.0001
## 200 1.2294 nan 0.0010 0.0001
## 220 1.2243 nan 0.0010 0.0001
## 240 1.2191 nan 0.0010 0.0001
## 260 1.2141 nan 0.0010 0.0001
## 280 1.2093 nan 0.0010 0.0001
## 300 1.2044 nan 0.0010 0.0001
## 320 1.1999 nan 0.0010 0.0001
## 340 1.1955 nan 0.0010 0.0001
## 360 1.1912 nan 0.0010 0.0001
## 380 1.1869 nan 0.0010 0.0001
## 400 1.1829 nan 0.0010 0.0001
## 420 1.1788 nan 0.0010 0.0001
## 440 1.1750 nan 0.0010 0.0001
## 460 1.1711 nan 0.0010 0.0001
## 480 1.1675 nan 0.0010 0.0001
## 500 1.1637 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0001
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2862 nan 0.0010 0.0001
## 40 1.2791 nan 0.0010 0.0002
## 60 1.2723 nan 0.0010 0.0001
## 80 1.2660 nan 0.0010 0.0002
## 100 1.2593 nan 0.0010 0.0001
## 120 1.2531 nan 0.0010 0.0001
## 140 1.2469 nan 0.0010 0.0001
## 160 1.2409 nan 0.0010 0.0001
## 180 1.2351 nan 0.0010 0.0001
## 200 1.2296 nan 0.0010 0.0001
## 220 1.2242 nan 0.0010 0.0001
## 240 1.2190 nan 0.0010 0.0001
## 260 1.2141 nan 0.0010 0.0001
## 280 1.2093 nan 0.0010 0.0001
## 300 1.2046 nan 0.0010 0.0001
## 320 1.1998 nan 0.0010 0.0001
## 340 1.1953 nan 0.0010 0.0001
## 360 1.1908 nan 0.0010 0.0001
## 380 1.1867 nan 0.0010 0.0001
## 400 1.1827 nan 0.0010 0.0001
## 420 1.1787 nan 0.0010 0.0001
## 440 1.1748 nan 0.0010 0.0001
## 460 1.1709 nan 0.0010 0.0001
## 480 1.1672 nan 0.0010 0.0001
## 500 1.1635 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2914 nan 0.0010 0.0002
## 6 1.2910 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0001
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2900 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2790 nan 0.0010 0.0001
## 60 1.2720 nan 0.0010 0.0002
## 80 1.2653 nan 0.0010 0.0001
## 100 1.2588 nan 0.0010 0.0001
## 120 1.2526 nan 0.0010 0.0001
## 140 1.2466 nan 0.0010 0.0001
## 160 1.2408 nan 0.0010 0.0001
## 180 1.2352 nan 0.0010 0.0001
## 200 1.2299 nan 0.0010 0.0001
## 220 1.2246 nan 0.0010 0.0001
## 240 1.2196 nan 0.0010 0.0001
## 260 1.2148 nan 0.0010 0.0001
## 280 1.2101 nan 0.0010 0.0001
## 300 1.2055 nan 0.0010 0.0001
## 320 1.2010 nan 0.0010 0.0001
## 340 1.1963 nan 0.0010 0.0001
## 360 1.1918 nan 0.0010 0.0001
## 380 1.1876 nan 0.0010 0.0001
## 400 1.1835 nan 0.0010 0.0001
## 420 1.1794 nan 0.0010 0.0001
## 440 1.1755 nan 0.0010 0.0001
## 460 1.1716 nan 0.0010 0.0001
## 480 1.1678 nan 0.0010 0.0001
## 500 1.1640 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
## 9 1.2891 nan 0.0010 0.0002
## 10 1.2887 nan 0.0010 0.0002
## 20 1.2841 nan 0.0010 0.0002
## 40 1.2747 nan 0.0010 0.0002
## 60 1.2658 nan 0.0010 0.0002
## 80 1.2572 nan 0.0010 0.0002
## 100 1.2488 nan 0.0010 0.0002
## 120 1.2406 nan 0.0010 0.0002
## 140 1.2329 nan 0.0010 0.0002
## 160 1.2253 nan 0.0010 0.0002
## 180 1.2183 nan 0.0010 0.0002
## 200 1.2111 nan 0.0010 0.0002
## 220 1.2041 nan 0.0010 0.0001
## 240 1.1974 nan 0.0010 0.0001
## 260 1.1908 nan 0.0010 0.0002
## 280 1.1845 nan 0.0010 0.0002
## 300 1.1784 nan 0.0010 0.0001
## 320 1.1724 nan 0.0010 0.0001
## 340 1.1666 nan 0.0010 0.0001
## 360 1.1608 nan 0.0010 0.0001
## 380 1.1553 nan 0.0010 0.0001
## 400 1.1496 nan 0.0010 0.0001
## 420 1.1445 nan 0.0010 0.0001
## 440 1.1394 nan 0.0010 0.0001
## 460 1.1344 nan 0.0010 0.0001
## 480 1.1296 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2895 nan 0.0010 0.0002
## 9 1.2890 nan 0.0010 0.0002
## 10 1.2885 nan 0.0010 0.0002
## 20 1.2838 nan 0.0010 0.0002
## 40 1.2748 nan 0.0010 0.0002
## 60 1.2660 nan 0.0010 0.0002
## 80 1.2573 nan 0.0010 0.0002
## 100 1.2489 nan 0.0010 0.0002
## 120 1.2409 nan 0.0010 0.0002
## 140 1.2332 nan 0.0010 0.0002
## 160 1.2254 nan 0.0010 0.0002
## 180 1.2180 nan 0.0010 0.0002
## 200 1.2110 nan 0.0010 0.0002
## 220 1.2043 nan 0.0010 0.0001
## 240 1.1975 nan 0.0010 0.0001
## 260 1.1908 nan 0.0010 0.0002
## 280 1.1846 nan 0.0010 0.0002
## 300 1.1784 nan 0.0010 0.0001
## 320 1.1723 nan 0.0010 0.0001
## 340 1.1664 nan 0.0010 0.0001
## 360 1.1607 nan 0.0010 0.0001
## 380 1.1552 nan 0.0010 0.0001
## 400 1.1498 nan 0.0010 0.0001
## 420 1.1445 nan 0.0010 0.0001
## 440 1.1392 nan 0.0010 0.0001
## 460 1.1341 nan 0.0010 0.0001
## 480 1.1292 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0003
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2895 nan 0.0010 0.0002
## 9 1.2890 nan 0.0010 0.0002
## 10 1.2886 nan 0.0010 0.0003
## 20 1.2839 nan 0.0010 0.0002
## 40 1.2747 nan 0.0010 0.0002
## 60 1.2659 nan 0.0010 0.0002
## 80 1.2572 nan 0.0010 0.0002
## 100 1.2490 nan 0.0010 0.0002
## 120 1.2410 nan 0.0010 0.0002
## 140 1.2329 nan 0.0010 0.0002
## 160 1.2254 nan 0.0010 0.0002
## 180 1.2181 nan 0.0010 0.0002
## 200 1.2109 nan 0.0010 0.0002
## 220 1.2040 nan 0.0010 0.0002
## 240 1.1973 nan 0.0010 0.0002
## 260 1.1905 nan 0.0010 0.0001
## 280 1.1845 nan 0.0010 0.0001
## 300 1.1783 nan 0.0010 0.0002
## 320 1.1723 nan 0.0010 0.0001
## 340 1.1664 nan 0.0010 0.0001
## 360 1.1607 nan 0.0010 0.0001
## 380 1.1552 nan 0.0010 0.0001
## 400 1.1496 nan 0.0010 0.0001
## 420 1.1446 nan 0.0010 0.0001
## 440 1.1394 nan 0.0010 0.0001
## 460 1.1344 nan 0.0010 0.0001
## 480 1.1296 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0003
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2907 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0002
## 8 1.2890 nan 0.0010 0.0003
## 9 1.2884 nan 0.0010 0.0002
## 10 1.2878 nan 0.0010 0.0003
## 20 1.2824 nan 0.0010 0.0002
## 40 1.2719 nan 0.0010 0.0002
## 60 1.2619 nan 0.0010 0.0002
## 80 1.2521 nan 0.0010 0.0002
## 100 1.2426 nan 0.0010 0.0002
## 120 1.2333 nan 0.0010 0.0002
## 140 1.2244 nan 0.0010 0.0002
## 160 1.2161 nan 0.0010 0.0002
## 180 1.2076 nan 0.0010 0.0002
## 200 1.1996 nan 0.0010 0.0002
## 220 1.1916 nan 0.0010 0.0002
## 240 1.1838 nan 0.0010 0.0002
## 260 1.1764 nan 0.0010 0.0001
## 280 1.1691 nan 0.0010 0.0001
## 300 1.1621 nan 0.0010 0.0002
## 320 1.1552 nan 0.0010 0.0002
## 340 1.1484 nan 0.0010 0.0001
## 360 1.1418 nan 0.0010 0.0001
## 380 1.1355 nan 0.0010 0.0001
## 400 1.1294 nan 0.0010 0.0001
## 420 1.1233 nan 0.0010 0.0001
## 440 1.1177 nan 0.0010 0.0001
## 460 1.1121 nan 0.0010 0.0001
## 480 1.1064 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0003
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0003
## 6 1.2900 nan 0.0010 0.0003
## 7 1.2895 nan 0.0010 0.0003
## 8 1.2889 nan 0.0010 0.0003
## 9 1.2884 nan 0.0010 0.0002
## 10 1.2878 nan 0.0010 0.0002
## 20 1.2824 nan 0.0010 0.0003
## 40 1.2720 nan 0.0010 0.0002
## 60 1.2618 nan 0.0010 0.0003
## 80 1.2519 nan 0.0010 0.0002
## 100 1.2424 nan 0.0010 0.0002
## 120 1.2334 nan 0.0010 0.0002
## 140 1.2245 nan 0.0010 0.0002
## 160 1.2159 nan 0.0010 0.0002
## 180 1.2075 nan 0.0010 0.0002
## 200 1.1993 nan 0.0010 0.0001
## 220 1.1912 nan 0.0010 0.0002
## 240 1.1834 nan 0.0010 0.0002
## 260 1.1760 nan 0.0010 0.0002
## 280 1.1685 nan 0.0010 0.0002
## 300 1.1617 nan 0.0010 0.0001
## 320 1.1547 nan 0.0010 0.0001
## 340 1.1480 nan 0.0010 0.0001
## 360 1.1419 nan 0.0010 0.0001
## 380 1.1358 nan 0.0010 0.0001
## 400 1.1297 nan 0.0010 0.0001
## 420 1.1237 nan 0.0010 0.0001
## 440 1.1180 nan 0.0010 0.0001
## 460 1.1124 nan 0.0010 0.0001
## 480 1.1069 nan 0.0010 0.0001
## 500 1.1015 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2917 nan 0.0010 0.0003
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0002
## 8 1.2891 nan 0.0010 0.0002
## 9 1.2885 nan 0.0010 0.0003
## 10 1.2880 nan 0.0010 0.0002
## 20 1.2826 nan 0.0010 0.0002
## 40 1.2721 nan 0.0010 0.0002
## 60 1.2618 nan 0.0010 0.0002
## 80 1.2518 nan 0.0010 0.0002
## 100 1.2422 nan 0.0010 0.0002
## 120 1.2331 nan 0.0010 0.0002
## 140 1.2245 nan 0.0010 0.0002
## 160 1.2157 nan 0.0010 0.0002
## 180 1.2072 nan 0.0010 0.0002
## 200 1.1990 nan 0.0010 0.0002
## 220 1.1910 nan 0.0010 0.0002
## 240 1.1833 nan 0.0010 0.0002
## 260 1.1760 nan 0.0010 0.0002
## 280 1.1688 nan 0.0010 0.0001
## 300 1.1618 nan 0.0010 0.0001
## 320 1.1550 nan 0.0010 0.0002
## 340 1.1483 nan 0.0010 0.0001
## 360 1.1417 nan 0.0010 0.0001
## 380 1.1353 nan 0.0010 0.0001
## 400 1.1292 nan 0.0010 0.0001
## 420 1.1235 nan 0.0010 0.0001
## 440 1.1177 nan 0.0010 0.0001
## 460 1.1122 nan 0.0010 0.0001
## 480 1.1066 nan 0.0010 0.0001
## 500 1.1013 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2583 nan 0.1000 0.0170
## 2 1.2295 nan 0.1000 0.0142
## 3 1.2036 nan 0.1000 0.0112
## 4 1.1826 nan 0.1000 0.0093
## 5 1.1629 nan 0.1000 0.0090
## 6 1.1484 nan 0.1000 0.0066
## 7 1.1320 nan 0.1000 0.0080
## 8 1.1178 nan 0.1000 0.0068
## 9 1.1040 nan 0.1000 0.0050
## 10 1.0903 nan 0.1000 0.0040
## 20 1.0024 nan 0.1000 0.0016
## 40 0.9193 nan 0.1000 0.0006
## 60 0.8785 nan 0.1000 0.0004
## 80 0.8508 nan 0.1000 -0.0005
## 100 0.8319 nan 0.1000 -0.0010
## 120 0.8167 nan 0.1000 -0.0010
## 140 0.8025 nan 0.1000 -0.0005
## 160 0.7931 nan 0.1000 -0.0005
## 180 0.7826 nan 0.1000 -0.0014
## 200 0.7768 nan 0.1000 -0.0007
## 220 0.7685 nan 0.1000 -0.0008
## 240 0.7640 nan 0.1000 -0.0016
## 260 0.7557 nan 0.1000 -0.0006
## 280 0.7510 nan 0.1000 -0.0002
## 300 0.7458 nan 0.1000 -0.0004
## 320 0.7392 nan 0.1000 -0.0010
## 340 0.7328 nan 0.1000 -0.0006
## 360 0.7246 nan 0.1000 -0.0006
## 380 0.7191 nan 0.1000 -0.0004
## 400 0.7131 nan 0.1000 -0.0013
## 420 0.7094 nan 0.1000 -0.0009
## 440 0.7033 nan 0.1000 -0.0007
## 460 0.6996 nan 0.1000 -0.0011
## 480 0.6956 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2563 nan 0.1000 0.0176
## 2 1.2310 nan 0.1000 0.0122
## 3 1.2060 nan 0.1000 0.0128
## 4 1.1814 nan 0.1000 0.0098
## 5 1.1613 nan 0.1000 0.0096
## 6 1.1445 nan 0.1000 0.0080
## 7 1.1291 nan 0.1000 0.0060
## 8 1.1132 nan 0.1000 0.0063
## 9 1.0987 nan 0.1000 0.0060
## 10 1.0864 nan 0.1000 0.0044
## 20 0.9996 nan 0.1000 0.0027
## 40 0.9199 nan 0.1000 -0.0007
## 60 0.8767 nan 0.1000 -0.0028
## 80 0.8501 nan 0.1000 -0.0008
## 100 0.8334 nan 0.1000 -0.0006
## 120 0.8175 nan 0.1000 -0.0001
## 140 0.8056 nan 0.1000 -0.0006
## 160 0.7940 nan 0.1000 -0.0007
## 180 0.7826 nan 0.1000 -0.0007
## 200 0.7740 nan 0.1000 0.0000
## 220 0.7673 nan 0.1000 -0.0013
## 240 0.7589 nan 0.1000 -0.0009
## 260 0.7520 nan 0.1000 -0.0003
## 280 0.7443 nan 0.1000 -0.0005
## 300 0.7386 nan 0.1000 -0.0027
## 320 0.7335 nan 0.1000 -0.0007
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## 360 0.7212 nan 0.1000 -0.0009
## 380 0.7162 nan 0.1000 -0.0006
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## 440 0.7017 nan 0.1000 -0.0017
## 460 0.6972 nan 0.1000 -0.0013
## 480 0.6913 nan 0.1000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2601 nan 0.1000 0.0158
## 2 1.2287 nan 0.1000 0.0130
## 3 1.2034 nan 0.1000 0.0120
## 4 1.1839 nan 0.1000 0.0084
## 5 1.1626 nan 0.1000 0.0084
## 6 1.1468 nan 0.1000 0.0068
## 7 1.1305 nan 0.1000 0.0071
## 8 1.1149 nan 0.1000 0.0056
## 9 1.1024 nan 0.1000 0.0031
## 10 1.0876 nan 0.1000 0.0060
## 20 1.0029 nan 0.1000 0.0024
## 40 0.9236 nan 0.1000 -0.0007
## 60 0.8829 nan 0.1000 0.0001
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## 100 0.8375 nan 0.1000 -0.0013
## 120 0.8242 nan 0.1000 -0.0012
## 140 0.8143 nan 0.1000 -0.0010
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## 180 0.7926 nan 0.1000 -0.0004
## 200 0.7799 nan 0.1000 -0.0014
## 220 0.7718 nan 0.1000 -0.0010
## 240 0.7622 nan 0.1000 -0.0003
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## 280 0.7477 nan 0.1000 -0.0003
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## 320 0.7350 nan 0.1000 -0.0010
## 340 0.7295 nan 0.1000 -0.0021
## 360 0.7245 nan 0.1000 -0.0010
## 380 0.7196 nan 0.1000 -0.0007
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## 460 0.7018 nan 0.1000 -0.0008
## 480 0.6971 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2455 nan 0.1000 0.0212
## 2 1.2090 nan 0.1000 0.0182
## 3 1.1747 nan 0.1000 0.0157
## 4 1.1439 nan 0.1000 0.0104
## 5 1.1192 nan 0.1000 0.0124
## 6 1.0955 nan 0.1000 0.0103
## 7 1.0755 nan 0.1000 0.0086
## 8 1.0606 nan 0.1000 0.0054
## 9 1.0475 nan 0.1000 0.0054
## 10 1.0335 nan 0.1000 0.0032
## 20 0.9371 nan 0.1000 0.0003
## 40 0.8548 nan 0.1000 -0.0002
## 60 0.8155 nan 0.1000 -0.0065
## 80 0.7785 nan 0.1000 -0.0021
## 100 0.7411 nan 0.1000 -0.0007
## 120 0.7104 nan 0.1000 -0.0011
## 140 0.6846 nan 0.1000 -0.0001
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## 180 0.6434 nan 0.1000 -0.0010
## 200 0.6256 nan 0.1000 -0.0006
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## 380 0.4976 nan 0.1000 -0.0013
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## 500 0.4276 nan 0.1000 -0.0010
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2443 nan 0.1000 0.0217
## 2 1.2105 nan 0.1000 0.0170
## 3 1.1775 nan 0.1000 0.0172
## 4 1.1500 nan 0.1000 0.0102
## 5 1.1261 nan 0.1000 0.0106
## 6 1.1027 nan 0.1000 0.0099
## 7 1.0838 nan 0.1000 0.0063
## 8 1.0642 nan 0.1000 0.0074
## 9 1.0470 nan 0.1000 0.0073
## 10 1.0338 nan 0.1000 0.0052
## 20 0.9346 nan 0.1000 -0.0001
## 40 0.8503 nan 0.1000 -0.0021
## 60 0.8004 nan 0.1000 -0.0008
## 80 0.7649 nan 0.1000 -0.0012
## 100 0.7361 nan 0.1000 -0.0019
## 120 0.7135 nan 0.1000 -0.0022
## 140 0.6909 nan 0.1000 -0.0004
## 160 0.6721 nan 0.1000 -0.0014
## 180 0.6485 nan 0.1000 -0.0011
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## 220 0.6153 nan 0.1000 -0.0012
## 240 0.5999 nan 0.1000 -0.0016
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## 280 0.5677 nan 0.1000 -0.0012
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## 380 0.5031 nan 0.1000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2511 nan 0.1000 0.0154
## 2 1.2112 nan 0.1000 0.0191
## 3 1.1792 nan 0.1000 0.0135
## 4 1.1502 nan 0.1000 0.0127
## 5 1.1212 nan 0.1000 0.0107
## 6 1.1025 nan 0.1000 0.0066
## 7 1.0860 nan 0.1000 0.0052
## 8 1.0670 nan 0.1000 0.0090
## 9 1.0478 nan 0.1000 0.0069
## 10 1.0351 nan 0.1000 0.0032
## 20 0.9319 nan 0.1000 0.0005
## 40 0.8482 nan 0.1000 0.0004
## 60 0.7987 nan 0.1000 -0.0008
## 80 0.7617 nan 0.1000 0.0001
## 100 0.7394 nan 0.1000 -0.0011
## 120 0.7146 nan 0.1000 -0.0004
## 140 0.6899 nan 0.1000 -0.0002
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## 180 0.6489 nan 0.1000 0.0000
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## 240 0.6049 nan 0.1000 -0.0009
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## 280 0.5722 nan 0.1000 -0.0012
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## 380 0.5022 nan 0.1000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2426 nan 0.1000 0.0222
## 2 1.1932 nan 0.1000 0.0219
## 3 1.1542 nan 0.1000 0.0154
## 4 1.1220 nan 0.1000 0.0142
## 5 1.0909 nan 0.1000 0.0091
## 6 1.0700 nan 0.1000 0.0072
## 7 1.0464 nan 0.1000 0.0065
## 8 1.0239 nan 0.1000 0.0091
## 9 1.0065 nan 0.1000 0.0019
## 10 0.9904 nan 0.1000 0.0043
## 20 0.8937 nan 0.1000 0.0014
## 40 0.7940 nan 0.1000 0.0001
## 60 0.7330 nan 0.1000 -0.0017
## 80 0.6927 nan 0.1000 -0.0012
## 100 0.6530 nan 0.1000 -0.0007
## 120 0.6161 nan 0.1000 -0.0003
## 140 0.5835 nan 0.1000 -0.0005
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## 180 0.5292 nan 0.1000 -0.0014
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## 280 0.4269 nan 0.1000 -0.0012
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## 320 0.3910 nan 0.1000 -0.0007
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## 380 0.3489 nan 0.1000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2421 nan 0.1000 0.0254
## 2 1.2042 nan 0.1000 0.0194
## 3 1.1650 nan 0.1000 0.0161
## 4 1.1312 nan 0.1000 0.0141
## 5 1.0989 nan 0.1000 0.0099
## 6 1.0756 nan 0.1000 0.0090
## 7 1.0533 nan 0.1000 0.0093
## 8 1.0366 nan 0.1000 0.0038
## 9 1.0203 nan 0.1000 0.0044
## 10 1.0025 nan 0.1000 0.0058
## 20 0.8992 nan 0.1000 0.0003
## 40 0.7937 nan 0.1000 -0.0010
## 60 0.7336 nan 0.1000 -0.0022
## 80 0.6898 nan 0.1000 -0.0015
## 100 0.6453 nan 0.1000 -0.0003
## 120 0.6105 nan 0.1000 -0.0006
## 140 0.5763 nan 0.1000 -0.0011
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## 180 0.5287 nan 0.1000 -0.0004
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## 220 0.4809 nan 0.1000 -0.0013
## 240 0.4614 nan 0.1000 -0.0003
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## 280 0.4202 nan 0.1000 -0.0015
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## 380 0.3397 nan 0.1000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2402 nan 0.1000 0.0258
## 2 1.2001 nan 0.1000 0.0145
## 3 1.1552 nan 0.1000 0.0182
## 4 1.1232 nan 0.1000 0.0118
## 5 1.0933 nan 0.1000 0.0095
## 6 1.0685 nan 0.1000 0.0090
## 7 1.0406 nan 0.1000 0.0090
## 8 1.0236 nan 0.1000 0.0048
## 9 1.0054 nan 0.1000 0.0061
## 10 0.9903 nan 0.1000 0.0055
## 20 0.8938 nan 0.1000 0.0009
## 40 0.8024 nan 0.1000 -0.0006
## 60 0.7474 nan 0.1000 -0.0027
## 80 0.7083 nan 0.1000 -0.0009
## 100 0.6637 nan 0.1000 -0.0019
## 120 0.6331 nan 0.1000 -0.0018
## 140 0.5999 nan 0.1000 -0.0006
## 160 0.5731 nan 0.1000 -0.0017
## 180 0.5407 nan 0.1000 -0.0001
## 200 0.5177 nan 0.1000 -0.0003
## 220 0.4929 nan 0.1000 -0.0020
## 240 0.4762 nan 0.1000 -0.0010
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## 280 0.4330 nan 0.1000 -0.0020
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## 320 0.4049 nan 0.1000 -0.0006
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## 380 0.3550 nan 0.1000 -0.0011
## 400 0.3434 nan 0.1000 -0.0015
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## 460 0.3012 nan 0.1000 -0.0010
## 480 0.2903 nan 0.1000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2237 nan 0.2000 0.0306
## 2 1.1711 nan 0.2000 0.0219
## 3 1.1309 nan 0.2000 0.0132
## 4 1.1043 nan 0.2000 0.0122
## 5 1.0799 nan 0.2000 0.0095
## 6 1.0608 nan 0.2000 0.0076
## 7 1.0446 nan 0.2000 0.0054
## 8 1.0265 nan 0.2000 0.0050
## 9 1.0141 nan 0.2000 0.0046
## 10 0.9994 nan 0.2000 0.0040
## 20 0.9174 nan 0.2000 0.0022
## 40 0.8538 nan 0.2000 -0.0021
## 60 0.8160 nan 0.2000 -0.0007
## 80 0.7954 nan 0.2000 -0.0010
## 100 0.7795 nan 0.2000 -0.0028
## 120 0.7600 nan 0.2000 -0.0016
## 140 0.7490 nan 0.2000 -0.0011
## 160 0.7374 nan 0.2000 -0.0031
## 180 0.7264 nan 0.2000 -0.0011
## 200 0.7172 nan 0.2000 -0.0007
## 220 0.7092 nan 0.2000 -0.0013
## 240 0.6987 nan 0.2000 -0.0017
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## 300 0.6724 nan 0.2000 -0.0002
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## 480 0.6200 nan 0.2000 -0.0037
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2239 nan 0.2000 0.0300
## 2 1.1738 nan 0.2000 0.0220
## 3 1.1432 nan 0.2000 0.0136
## 4 1.1140 nan 0.2000 0.0120
## 5 1.0822 nan 0.2000 0.0127
## 6 1.0654 nan 0.2000 0.0046
## 7 1.0428 nan 0.2000 0.0104
## 8 1.0267 nan 0.2000 0.0057
## 9 1.0142 nan 0.2000 0.0018
## 10 0.9984 nan 0.2000 0.0052
## 20 0.9179 nan 0.2000 0.0003
## 40 0.8553 nan 0.2000 -0.0008
## 60 0.8225 nan 0.2000 -0.0009
## 80 0.7972 nan 0.2000 -0.0004
## 100 0.7783 nan 0.2000 -0.0025
## 120 0.7642 nan 0.2000 -0.0027
## 140 0.7426 nan 0.2000 -0.0002
## 160 0.7306 nan 0.2000 -0.0019
## 180 0.7197 nan 0.2000 -0.0014
## 200 0.7114 nan 0.2000 -0.0028
## 220 0.7070 nan 0.2000 -0.0036
## 240 0.6959 nan 0.2000 -0.0013
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## 280 0.6778 nan 0.2000 -0.0016
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## 320 0.6643 nan 0.2000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2287 nan 0.2000 0.0327
## 2 1.1733 nan 0.2000 0.0206
## 3 1.1400 nan 0.2000 0.0127
## 4 1.1108 nan 0.2000 0.0129
## 5 1.0882 nan 0.2000 0.0081
## 6 1.0703 nan 0.2000 0.0067
## 7 1.0475 nan 0.2000 0.0110
## 8 1.0278 nan 0.2000 0.0057
## 9 1.0144 nan 0.2000 0.0037
## 10 0.9979 nan 0.2000 0.0047
## 20 0.9232 nan 0.2000 -0.0022
## 40 0.8665 nan 0.2000 -0.0017
## 60 0.8324 nan 0.2000 -0.0031
## 80 0.8041 nan 0.2000 -0.0012
## 100 0.7841 nan 0.2000 -0.0019
## 120 0.7739 nan 0.2000 -0.0020
## 140 0.7575 nan 0.2000 -0.0029
## 160 0.7443 nan 0.2000 -0.0035
## 180 0.7343 nan 0.2000 -0.0031
## 200 0.7255 nan 0.2000 -0.0012
## 220 0.7148 nan 0.2000 -0.0036
## 240 0.7062 nan 0.2000 -0.0011
## 260 0.7003 nan 0.2000 -0.0016
## 280 0.6932 nan 0.2000 -0.0007
## 300 0.6879 nan 0.2000 -0.0016
## 320 0.6784 nan 0.2000 -0.0005
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## 380 0.6577 nan 0.2000 -0.0018
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2067 nan 0.2000 0.0411
## 2 1.1440 nan 0.2000 0.0278
## 3 1.0909 nan 0.2000 0.0199
## 4 1.0562 nan 0.2000 0.0142
## 5 1.0252 nan 0.2000 0.0076
## 6 1.0061 nan 0.2000 0.0074
## 7 0.9851 nan 0.2000 0.0074
## 8 0.9738 nan 0.2000 0.0006
## 9 0.9514 nan 0.2000 0.0050
## 10 0.9378 nan 0.2000 0.0049
## 20 0.8557 nan 0.2000 -0.0039
## 40 0.7752 nan 0.2000 -0.0045
## 60 0.7229 nan 0.2000 -0.0020
## 80 0.6839 nan 0.2000 -0.0025
## 100 0.6484 nan 0.2000 -0.0032
## 120 0.6172 nan 0.2000 -0.0048
## 140 0.5842 nan 0.2000 -0.0049
## 160 0.5561 nan 0.2000 -0.0016
## 180 0.5251 nan 0.2000 -0.0015
## 200 0.5018 nan 0.2000 -0.0028
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## 240 0.4555 nan 0.2000 -0.0026
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## 280 0.4192 nan 0.2000 -0.0026
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## 380 0.3371 nan 0.2000 -0.0025
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2042 nan 0.2000 0.0399
## 2 1.1508 nan 0.2000 0.0227
## 3 1.0981 nan 0.2000 0.0191
## 4 1.0621 nan 0.2000 0.0148
## 5 1.0337 nan 0.2000 0.0084
## 6 1.0098 nan 0.2000 0.0086
## 7 0.9894 nan 0.2000 0.0085
## 8 0.9756 nan 0.2000 0.0013
## 9 0.9574 nan 0.2000 0.0050
## 10 0.9435 nan 0.2000 -0.0015
## 20 0.8558 nan 0.2000 -0.0010
## 40 0.7751 nan 0.2000 -0.0013
## 60 0.7224 nan 0.2000 -0.0013
## 80 0.6828 nan 0.2000 -0.0021
## 100 0.6507 nan 0.2000 -0.0018
## 120 0.6167 nan 0.2000 -0.0036
## 140 0.5884 nan 0.2000 -0.0018
## 160 0.5543 nan 0.2000 -0.0014
## 180 0.5323 nan 0.2000 -0.0037
## 200 0.5029 nan 0.2000 -0.0007
## 220 0.4778 nan 0.2000 -0.0014
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## 280 0.4214 nan 0.2000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2015 nan 0.2000 0.0407
## 2 1.1346 nan 0.2000 0.0270
## 3 1.0936 nan 0.2000 0.0197
## 4 1.0578 nan 0.2000 0.0124
## 5 1.0337 nan 0.2000 0.0071
## 6 1.0084 nan 0.2000 0.0104
## 7 0.9867 nan 0.2000 0.0077
## 8 0.9665 nan 0.2000 0.0082
## 9 0.9525 nan 0.2000 0.0024
## 10 0.9436 nan 0.2000 -0.0018
## 20 0.8554 nan 0.2000 -0.0030
## 40 0.7788 nan 0.2000 -0.0032
## 60 0.7309 nan 0.2000 -0.0010
## 80 0.6787 nan 0.2000 0.0003
## 100 0.6429 nan 0.2000 -0.0022
## 120 0.6133 nan 0.2000 -0.0014
## 140 0.5858 nan 0.2000 -0.0022
## 160 0.5515 nan 0.2000 -0.0023
## 180 0.5240 nan 0.2000 -0.0010
## 200 0.5005 nan 0.2000 -0.0014
## 220 0.4815 nan 0.2000 -0.0017
## 240 0.4571 nan 0.2000 -0.0031
## 260 0.4278 nan 0.2000 -0.0001
## 280 0.4098 nan 0.2000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1993 nan 0.2000 0.0392
## 2 1.1217 nan 0.2000 0.0317
## 3 1.0730 nan 0.2000 0.0181
## 4 1.0356 nan 0.2000 0.0149
## 5 1.0024 nan 0.2000 0.0136
## 6 0.9706 nan 0.2000 0.0075
## 7 0.9506 nan 0.2000 0.0048
## 8 0.9266 nan 0.2000 0.0038
## 9 0.9112 nan 0.2000 0.0004
## 10 0.9021 nan 0.2000 -0.0024
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## 40 0.7102 nan 0.2000 -0.0014
## 60 0.6427 nan 0.2000 -0.0061
## 80 0.5840 nan 0.2000 -0.0011
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## 140 0.4345 nan 0.2000 -0.0031
## 160 0.3974 nan 0.2000 -0.0039
## 180 0.3593 nan 0.2000 -0.0018
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## 240 0.2910 nan 0.2000 -0.0019
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1895 nan 0.2000 0.0413
## 2 1.1124 nan 0.2000 0.0317
## 3 1.0714 nan 0.2000 0.0131
## 4 1.0308 nan 0.2000 0.0179
## 5 0.9977 nan 0.2000 0.0082
## 6 0.9767 nan 0.2000 0.0079
## 7 0.9476 nan 0.2000 0.0107
## 8 0.9293 nan 0.2000 0.0025
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## 40 0.6975 nan 0.2000 -0.0051
## 60 0.6408 nan 0.2000 -0.0033
## 80 0.5734 nan 0.2000 -0.0017
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## 120 0.4779 nan 0.2000 -0.0006
## 140 0.4414 nan 0.2000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1877 nan 0.2000 0.0478
## 2 1.1334 nan 0.2000 0.0222
## 3 1.0850 nan 0.2000 0.0194
## 4 1.0445 nan 0.2000 0.0159
## 5 1.0085 nan 0.2000 0.0123
## 6 0.9816 nan 0.2000 0.0082
## 7 0.9629 nan 0.2000 0.0021
## 8 0.9372 nan 0.2000 0.0116
## 9 0.9178 nan 0.2000 0.0048
## 10 0.9036 nan 0.2000 0.0037
## 20 0.7985 nan 0.2000 0.0011
## 40 0.7063 nan 0.2000 -0.0030
## 60 0.6374 nan 0.2000 -0.0029
## 80 0.5805 nan 0.2000 -0.0030
## 100 0.5236 nan 0.2000 -0.0036
## 120 0.4853 nan 0.2000 -0.0006
## 140 0.4437 nan 0.2000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2038 nan 0.3000 0.0465
## 2 1.1382 nan 0.3000 0.0289
## 3 1.1053 nan 0.3000 0.0099
## 4 1.0730 nan 0.3000 0.0125
## 5 1.0339 nan 0.3000 0.0121
## 6 1.0158 nan 0.3000 0.0044
## 7 0.9980 nan 0.3000 0.0043
## 8 0.9808 nan 0.3000 0.0038
## 9 0.9642 nan 0.3000 0.0058
## 10 0.9550 nan 0.3000 -0.0002
## 20 0.8896 nan 0.3000 -0.0023
## 40 0.8368 nan 0.3000 -0.0030
## 60 0.8056 nan 0.3000 -0.0007
## 80 0.7854 nan 0.3000 -0.0020
## 100 0.7573 nan 0.3000 -0.0006
## 120 0.7332 nan 0.3000 -0.0011
## 140 0.7168 nan 0.3000 -0.0011
## 160 0.7053 nan 0.3000 -0.0011
## 180 0.6931 nan 0.3000 -0.0029
## 200 0.6833 nan 0.3000 -0.0020
## 220 0.6726 nan 0.3000 -0.0047
## 240 0.6589 nan 0.3000 -0.0022
## 260 0.6459 nan 0.3000 -0.0018
## 280 0.6416 nan 0.3000 -0.0039
## 300 0.6272 nan 0.3000 -0.0030
## 320 0.6189 nan 0.3000 -0.0022
## 340 0.6161 nan 0.3000 -0.0057
## 360 0.6076 nan 0.3000 -0.0038
## 380 0.5993 nan 0.3000 0.0002
## 400 0.5901 nan 0.3000 -0.0009
## 420 0.5812 nan 0.3000 -0.0014
## 440 0.5800 nan 0.3000 -0.0056
## 460 0.5712 nan 0.3000 -0.0035
## 480 0.5732 nan 0.3000 -0.0029
## 500 0.5636 nan 0.3000 -0.0026
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1883 nan 0.3000 0.0456
## 2 1.1331 nan 0.3000 0.0282
## 3 1.0941 nan 0.3000 0.0138
## 4 1.0574 nan 0.3000 0.0144
## 5 1.0376 nan 0.3000 0.0056
## 6 1.0239 nan 0.3000 0.0029
## 7 0.9953 nan 0.3000 0.0121
## 8 0.9827 nan 0.3000 0.0001
## 9 0.9664 nan 0.3000 0.0019
## 10 0.9491 nan 0.3000 0.0046
## 20 0.8809 nan 0.3000 -0.0001
## 40 0.8182 nan 0.3000 -0.0047
## 60 0.7917 nan 0.3000 -0.0032
## 80 0.7788 nan 0.3000 -0.0051
## 100 0.7621 nan 0.3000 -0.0033
## 120 0.7367 nan 0.3000 -0.0064
## 140 0.7151 nan 0.3000 -0.0032
## 160 0.7057 nan 0.3000 -0.0032
## 180 0.6913 nan 0.3000 -0.0020
## 200 0.6768 nan 0.3000 -0.0011
## 220 0.6712 nan 0.3000 -0.0036
## 240 0.6661 nan 0.3000 -0.0024
## 260 0.6530 nan 0.3000 -0.0058
## 280 0.6466 nan 0.3000 -0.0002
## 300 0.6385 nan 0.3000 -0.0038
## 320 0.6263 nan 0.3000 -0.0026
## 340 0.6209 nan 0.3000 -0.0014
## 360 0.6107 nan 0.3000 -0.0011
## 380 0.6030 nan 0.3000 -0.0026
## 400 0.5928 nan 0.3000 -0.0036
## 420 0.5840 nan 0.3000 -0.0018
## 440 0.5763 nan 0.3000 -0.0016
## 460 0.5710 nan 0.3000 -0.0013
## 480 0.5684 nan 0.3000 -0.0038
## 500 0.5592 nan 0.3000 -0.0024
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1981 nan 0.3000 0.0418
## 2 1.1525 nan 0.3000 0.0139
## 3 1.0997 nan 0.3000 0.0197
## 4 1.0642 nan 0.3000 0.0178
## 5 1.0404 nan 0.3000 0.0066
## 6 1.0122 nan 0.3000 0.0136
## 7 0.9952 nan 0.3000 0.0039
## 8 0.9808 nan 0.3000 0.0013
## 9 0.9680 nan 0.3000 0.0031
## 10 0.9512 nan 0.3000 0.0073
## 20 0.8924 nan 0.3000 -0.0020
## 40 0.8223 nan 0.3000 -0.0007
## 60 0.7871 nan 0.3000 -0.0040
## 80 0.7613 nan 0.3000 -0.0020
## 100 0.7426 nan 0.3000 0.0003
## 120 0.7281 nan 0.3000 -0.0084
## 140 0.7099 nan 0.3000 -0.0042
## 160 0.6954 nan 0.3000 -0.0023
## 180 0.6886 nan 0.3000 -0.0043
## 200 0.6758 nan 0.3000 -0.0011
## 220 0.6660 nan 0.3000 -0.0053
## 240 0.6568 nan 0.3000 -0.0034
## 260 0.6457 nan 0.3000 -0.0035
## 280 0.6328 nan 0.3000 -0.0034
## 300 0.6246 nan 0.3000 -0.0050
## 320 0.6137 nan 0.3000 -0.0007
## 340 0.6110 nan 0.3000 -0.0014
## 360 0.6079 nan 0.3000 -0.0073
## 380 0.6001 nan 0.3000 -0.0062
## 400 0.5933 nan 0.3000 -0.0025
## 420 0.5873 nan 0.3000 -0.0025
## 440 0.5828 nan 0.3000 -0.0014
## 460 0.5761 nan 0.3000 -0.0049
## 480 0.5708 nan 0.3000 -0.0016
## 500 0.5668 nan 0.3000 -0.0024
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1594 nan 0.3000 0.0546
## 2 1.0841 nan 0.3000 0.0298
## 3 1.0332 nan 0.3000 0.0197
## 4 0.9903 nan 0.3000 0.0081
## 5 0.9613 nan 0.3000 0.0079
## 6 0.9491 nan 0.3000 -0.0005
## 7 0.9341 nan 0.3000 0.0017
## 8 0.9146 nan 0.3000 0.0031
## 9 0.9050 nan 0.3000 -0.0019
## 10 0.8916 nan 0.3000 -0.0010
## 20 0.8207 nan 0.3000 0.0015
## 40 0.7303 nan 0.3000 -0.0020
## 60 0.6693 nan 0.3000 -0.0052
## 80 0.6198 nan 0.3000 -0.0056
## 100 1.0629 nan 0.3000 -0.0789
## 120 54.9271 nan 0.3000 -0.0029
## 140 54.8984 nan 0.3000 -0.0024
## 160 54.8690 nan 0.3000 -0.0002
## 180 54.8546 nan 0.3000 -0.0057
## 200 54.8416 nan 0.3000 -0.0008
## 220 inf nan 0.3000 -inf
## 240 inf nan 0.3000 nan
## 260 inf nan 0.3000 nan
## 280 inf nan 0.3000 nan
## 300 inf nan 0.3000 nan
## 320 inf nan 0.3000 nan
## 340 inf nan 0.3000 nan
## 360 inf nan 0.3000 nan
## 380 inf nan 0.3000 nan
## 400 inf nan 0.3000 nan
## 420 inf nan 0.3000 nan
## 440 inf nan 0.3000 nan
## 460 inf nan 0.3000 nan
## 480 inf nan 0.3000 nan
## 500 inf nan 0.3000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1652 nan 0.3000 0.0550
## 2 1.1026 nan 0.3000 0.0199
## 3 1.0418 nan 0.3000 0.0171
## 4 1.0004 nan 0.3000 0.0136
## 5 0.9704 nan 0.3000 0.0093
## 6 0.9458 nan 0.3000 0.0098
## 7 0.9207 nan 0.3000 0.0028
## 8 0.9095 nan 0.3000 -0.0021
## 9 0.9009 nan 0.3000 -0.0016
## 10 0.8877 nan 0.3000 0.0002
## 20 0.8096 nan 0.3000 -0.0057
## 40 0.7211 nan 0.3000 0.0000
## 60 0.6575 nan 0.3000 -0.0020
## 80 0.6139 nan 0.3000 -0.0044
## 100 0.5736 nan 0.3000 -0.0042
## 120 0.5382 nan 0.3000 -0.0066
## 140 0.5007 nan 0.3000 -0.0015
## 160 0.4609 nan 0.3000 -0.0053
## 180 0.4316 nan 0.3000 -0.0008
## 200 0.4058 nan 0.3000 -0.0006
## 220 0.3893 nan 0.3000 -0.0037
## 240 0.3575 nan 0.3000 -0.0007
## 260 0.3323 nan 0.3000 0.0005
## 280 0.3076 nan 0.3000 -0.0008
## 300 0.2910 nan 0.3000 -0.0029
## 320 0.2794 nan 0.3000 -0.0016
## 340 0.2602 nan 0.3000 -0.0019
## 360 0.2468 nan 0.3000 -0.0012
## 380 0.2346 nan 0.3000 -0.0041
## 400 0.2218 nan 0.3000 -0.0020
## 420 0.2119 nan 0.3000 -0.0011
## 440 0.1996 nan 0.3000 -0.0006
## 460 0.1868 nan 0.3000 -0.0012
## 480 0.1778 nan 0.3000 -0.0004
## 500 0.1677 nan 0.3000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1707 nan 0.3000 0.0620
## 2 1.0881 nan 0.3000 0.0384
## 3 1.0388 nan 0.3000 0.0179
## 4 1.0004 nan 0.3000 0.0143
## 5 0.9646 nan 0.3000 0.0129
## 6 0.9398 nan 0.3000 0.0050
## 7 0.9249 nan 0.3000 -0.0007
## 8 0.9107 nan 0.3000 0.0016
## 9 0.8944 nan 0.3000 0.0007
## 10 0.8849 nan 0.3000 -0.0011
## 20 0.8033 nan 0.3000 -0.0024
## 40 0.7204 nan 0.3000 -0.0031
## 60 0.6607 nan 0.3000 -0.0041
## 80 0.6115 nan 0.3000 -0.0003
## 100 0.5582 nan 0.3000 -0.0027
## 120 0.5188 nan 0.3000 -0.0003
## 140 0.4792 nan 0.3000 -0.0044
## 160 0.4517 nan 0.3000 -0.0035
## 180 0.4300 nan 0.3000 -0.0008
## 200 0.4034 nan 0.3000 -0.0036
## 220 0.3847 nan 0.3000 -0.0039
## 240 0.3659 nan 0.3000 -0.0032
## 260 0.3433 nan 0.3000 -0.0011
## 280 0.3259 nan 0.3000 -0.0044
## 300 0.3119 nan 0.3000 -0.0039
## 320 0.2942 nan 0.3000 -0.0018
## 340 0.2753 nan 0.3000 -0.0019
## 360 0.2626 nan 0.3000 -0.0020
## 380 0.2500 nan 0.3000 -0.0022
## 400 0.2338 nan 0.3000 -0.0024
## 420 0.2200 nan 0.3000 -0.0015
## 440 0.2045 nan 0.3000 -0.0026
## 460 0.1948 nan 0.3000 -0.0011
## 480 0.1841 nan 0.3000 -0.0014
## 500 0.1733 nan 0.3000 -0.0010
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1493 nan 0.3000 0.0525
## 2 1.0621 nan 0.3000 0.0383
## 3 1.0180 nan 0.3000 0.0057
## 4 0.9760 nan 0.3000 0.0098
## 5 0.9499 nan 0.3000 0.0006
## 6 0.9190 nan 0.3000 0.0073
## 7 0.9087 nan 0.3000 -0.0115
## 8 0.8908 nan 0.3000 -0.0019
## 9 0.8737 nan 0.3000 -0.0003
## 10 0.8622 nan 0.3000 -0.0048
## 20 0.7722 nan 0.3000 -0.0028
## 40 0.6478 nan 0.3000 -0.0069
## 60 0.5452 nan 0.3000 0.0008
## 80 0.4865 nan 0.3000 -0.0120
## 100 0.4415 nan 0.3000 -0.0058
## 120 0.3792 nan 0.3000 -0.0023
## 140 0.3322 nan 0.3000 -0.0027
## 160 0.2947 nan 0.3000 -0.0034
## 180 0.2678 nan 0.3000 -0.0032
## 200 0.2344 nan 0.3000 -0.0007
## 220 0.2190 nan 0.3000 -0.0022
## 240 0.1951 nan 0.3000 -0.0021
## 260 0.1753 nan 0.3000 -0.0008
## 280 0.1619 nan 0.3000 -0.0013
## 300 0.1461 nan 0.3000 0.0003
## 320 0.1351 nan 0.3000 -0.0007
## 340 0.1246 nan 0.3000 -0.0004
## 360 0.1140 nan 0.3000 -0.0008
## 380 0.1042 nan 0.3000 -0.0009
## 400 0.0973 nan 0.3000 -0.0006
## 420 0.0900 nan 0.3000 -0.0011
## 440 0.0830 nan 0.3000 -0.0006
## 460 0.0777 nan 0.3000 -0.0006
## 480 0.0737 nan 0.3000 -0.0004
## 500 0.0687 nan 0.3000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1709 nan 0.3000 0.0612
## 2 1.0923 nan 0.3000 0.0243
## 3 1.0163 nan 0.3000 0.0320
## 4 0.9700 nan 0.3000 0.0125
## 5 0.9453 nan 0.3000 0.0020
## 6 0.9262 nan 0.3000 -0.0027
## 7 0.9030 nan 0.3000 0.0021
## 8 0.8749 nan 0.3000 0.0088
## 9 0.8611 nan 0.3000 -0.0032
## 10 0.8493 nan 0.3000 -0.0043
## 20 0.7499 nan 0.3000 -0.0049
## 40 0.6548 nan 0.3000 -0.0039
## 60 0.5532 nan 0.3000 -0.0050
## 80 0.4931 nan 0.3000 -0.0070
## 100 0.4386 nan 0.3000 -0.0037
## 120 0.3860 nan 0.3000 -0.0034
## 140 0.3418 nan 0.3000 -0.0007
## 160 0.3036 nan 0.3000 -0.0029
## 180 0.2721 nan 0.3000 -0.0021
## 200 0.2391 nan 0.3000 -0.0034
## 220 0.2136 nan 0.3000 -0.0018
## 240 0.1913 nan 0.3000 -0.0024
## 260 0.1755 nan 0.3000 -0.0011
## 280 0.1575 nan 0.3000 -0.0005
## 300 0.1426 nan 0.3000 -0.0006
## 320 0.1303 nan 0.3000 -0.0016
## 340 0.1214 nan 0.3000 -0.0007
## 360 0.1120 nan 0.3000 -0.0007
## 380 0.1032 nan 0.3000 -0.0006
## 400 0.0922 nan 0.3000 -0.0003
## 420 0.0846 nan 0.3000 -0.0001
## 440 0.0773 nan 0.3000 -0.0007
## 460 0.0718 nan 0.3000 -0.0008
## 480 0.0672 nan 0.3000 -0.0004
## 500 0.0606 nan 0.3000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1573 nan 0.3000 0.0573
## 2 1.0873 nan 0.3000 0.0261
## 3 1.0338 nan 0.3000 0.0153
## 4 0.9816 nan 0.3000 0.0106
## 5 0.9516 nan 0.3000 0.0044
## 6 0.9265 nan 0.3000 0.0044
## 7 0.9093 nan 0.3000 0.0002
## 8 0.8893 nan 0.3000 0.0027
## 9 0.8755 nan 0.3000 -0.0053
## 10 0.8611 nan 0.3000 0.0005
## 20 0.7550 nan 0.3000 -0.0025
## 40 0.6493 nan 0.3000 -0.0017
## 60 0.5704 nan 0.3000 -0.0009
## 80 0.4946 nan 0.3000 -0.0033
## 100 0.4341 nan 0.3000 -0.0018
## 120 0.4007 nan 0.3000 -0.0038
## 140 0.3595 nan 0.3000 -0.0026
## 160 0.3268 nan 0.3000 -0.0014
## 180 0.2905 nan 0.3000 -0.0037
## 200 0.2505 nan 0.3000 -0.0014
## 220 0.2262 nan 0.3000 -0.0018
## 240 0.2045 nan 0.3000 -0.0021
## 260 0.1850 nan 0.3000 -0.0011
## 280 0.1693 nan 0.3000 -0.0009
## 300 0.1558 nan 0.3000 -0.0009
## 320 0.1446 nan 0.3000 -0.0008
## 340 0.1306 nan 0.3000 -0.0011
## 360 0.1186 nan 0.3000 -0.0017
## 380 0.1092 nan 0.3000 -0.0008
## 400 0.0991 nan 0.3000 -0.0008
## 420 0.0890 nan 0.3000 -0.0011
## 440 0.0804 nan 0.3000 -0.0007
## 460 0.0723 nan 0.3000 -0.0008
## 480 0.0672 nan 0.3000 -0.0001
## 500 0.0594 nan 0.3000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1562 nan 0.5000 0.0658
## 2 1.0906 nan 0.5000 0.0270
## 3 1.0405 nan 0.5000 0.0247
## 4 1.0016 nan 0.5000 0.0149
## 5 0.9838 nan 0.5000 -0.0022
## 6 0.9647 nan 0.5000 0.0020
## 7 0.9440 nan 0.5000 0.0012
## 8 0.9361 nan 0.5000 0.0009
## 9 0.9213 nan 0.5000 -0.0002
## 10 0.9084 nan 0.5000 -0.0014
## 20 0.8412 nan 0.5000 -0.0044
## 40 0.7815 nan 0.5000 -0.0042
## 60 0.7410 nan 0.5000 -0.0040
## 80 0.7161 nan 0.5000 -0.0034
## 100 0.7027 nan 0.5000 -0.0012
## 120 0.6908 nan 0.5000 -0.0039
## 140 0.6715 nan 0.5000 -0.0030
## 160 0.6587 nan 0.5000 -0.0068
## 180 0.6480 nan 0.5000 -0.0075
## 200 0.6210 nan 0.5000 -0.0057
## 220 0.6091 nan 0.5000 -0.0004
## 240 0.5924 nan 0.5000 0.0013
## 260 0.5835 nan 0.5000 -0.0113
## 280 0.5717 nan 0.5000 -0.0042
## 300 0.5671 nan 0.5000 -0.0073
## 320 0.5489 nan 0.5000 -0.0079
## 340 0.5418 nan 0.5000 -0.0020
## 360 0.5300 nan 0.5000 -0.0053
## 380 0.5190 nan 0.5000 -0.0031
## 400 0.5189 nan 0.5000 -0.0049
## 420 0.5128 nan 0.5000 -0.0062
## 440 0.5075 nan 0.5000 -0.0094
## 460 0.4975 nan 0.5000 -0.0034
## 480 0.4927 nan 0.5000 -0.0056
## 500 0.4852 nan 0.5000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1586 nan 0.5000 0.0501
## 2 1.1011 nan 0.5000 0.0199
## 3 1.0487 nan 0.5000 0.0219
## 4 1.0138 nan 0.5000 0.0147
## 5 0.9768 nan 0.5000 0.0129
## 6 0.9673 nan 0.5000 -0.0043
## 7 0.9569 nan 0.5000 -0.0026
## 8 0.9525 nan 0.5000 -0.0115
## 9 0.9337 nan 0.5000 -0.0003
## 10 0.9305 nan 0.5000 -0.0065
## 20 0.8526 nan 0.5000 -0.0009
## 40 0.7934 nan 0.5000 -0.0043
## 60 0.7567 nan 0.5000 -0.0030
## 80 0.7393 nan 0.5000 -0.0041
## 100 0.7067 nan 0.5000 0.0020
## 120 0.6904 nan 0.5000 -0.0033
## 140 0.6814 nan 0.5000 -0.0039
## 160 0.6736 nan 0.5000 -0.0028
## 180 0.6543 nan 0.5000 -0.0083
## 200 0.6415 nan 0.5000 -0.0047
## 220 0.6237 nan 0.5000 -0.0048
## 240 0.6202 nan 0.5000 -0.0142
## 260 0.6010 nan 0.5000 0.0001
## 280 0.5918 nan 0.5000 -0.0072
## 300 0.5757 nan 0.5000 -0.0036
## 320 0.5750 nan 0.5000 -0.0038
## 340 0.5633 nan 0.5000 -0.0058
## 360 0.5525 nan 0.5000 -0.0007
## 380 0.5462 nan 0.5000 -0.0051
## 400 0.5425 nan 0.5000 -0.0033
## 420 0.5417 nan 0.5000 -0.0007
## 440 0.5365 nan 0.5000 -0.0070
## 460 0.5275 nan 0.5000 -0.0046
## 480 0.5200 nan 0.5000 -0.0070
## 500 0.5001 nan 0.5000 -0.0030
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1694 nan 0.5000 0.0471
## 2 1.0959 nan 0.5000 0.0144
## 3 1.0441 nan 0.5000 0.0187
## 4 0.9983 nan 0.5000 0.0149
## 5 0.9681 nan 0.5000 0.0117
## 6 0.9484 nan 0.5000 0.0082
## 7 0.9469 nan 0.5000 -0.0114
## 8 0.9272 nan 0.5000 0.0067
## 9 0.9220 nan 0.5000 -0.0089
## 10 0.9160 nan 0.5000 -0.0018
## 20 0.8587 nan 0.5000 -0.0063
## 40 0.8094 nan 0.5000 -0.0072
## 60 0.7832 nan 0.5000 -0.0082
## 80 0.7422 nan 0.5000 -0.0025
## 100 0.7201 nan 0.5000 -0.0035
## 120 0.7019 nan 0.5000 -0.0057
## 140 0.6928 nan 0.5000 -0.0008
## 160 0.6752 nan 0.5000 -0.0028
## 180 0.6555 nan 0.5000 -0.0030
## 200 0.6397 nan 0.5000 -0.0023
## 220 0.6297 nan 0.5000 -0.0034
## 240 0.6112 nan 0.5000 -0.0030
## 260 0.6116 nan 0.5000 -0.0026
## 280 0.5908 nan 0.5000 -0.0034
## 300 0.5820 nan 0.5000 -0.0025
## 320 0.5674 nan 0.5000 -0.0019
## 340 0.5618 nan 0.5000 -0.0050
## 360 0.5579 nan 0.5000 -0.0004
## 380 0.5491 nan 0.5000 -0.0036
## 400 0.5388 nan 0.5000 -0.0040
## 420 0.5349 nan 0.5000 -0.0047
## 440 0.5289 nan 0.5000 -0.0135
## 460 0.5108 nan 0.5000 0.0004
## 480 0.5051 nan 0.5000 -0.0013
## 500 0.4998 nan 0.5000 -0.0053
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1272 nan 0.5000 0.0862
## 2 1.0330 nan 0.5000 0.0373
## 3 0.9774 nan 0.5000 0.0145
## 4 0.9537 nan 0.5000 0.0018
## 5 0.9293 nan 0.5000 0.0046
## 6 0.9058 nan 0.5000 -0.0003
## 7 0.8943 nan 0.5000 -0.0076
## 8 0.8838 nan 0.5000 -0.0053
## 9 0.8746 nan 0.5000 -0.0060
## 10 0.8578 nan 0.5000 0.0033
## 20 0.7910 nan 0.5000 -0.0091
## 40 0.6926 nan 0.5000 -0.0117
## 60 inf nan 0.5000 -0.0075
## 80 inf nan 0.5000 -0.0223
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 -0.0035
## 160 inf nan 0.5000 -0.0080
## 180 inf nan 0.5000 -0.0023
## 200 inf nan 0.5000 -0.0011
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
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## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1207 nan 0.5000 0.0690
## 2 1.0506 nan 0.5000 0.0153
## 3 0.9845 nan 0.5000 0.0136
## 4 0.9430 nan 0.5000 -0.0027
## 5 0.9219 nan 0.5000 -0.0028
## 6 0.9162 nan 0.5000 -0.0116
## 7 0.9031 nan 0.5000 -0.0001
## 8 0.8834 nan 0.5000 0.0059
## 9 0.8693 nan 0.5000 -0.0034
## 10 0.8653 nan 0.5000 -0.0093
## 20 0.7892 nan 0.5000 -0.0114
## 40 0.7015 nan 0.5000 -0.0012
## 60 0.6319 nan 0.5000 -0.0034
## 80 0.5899 nan 0.5000 -0.0110
## 100 0.5064 nan 0.5000 -0.0051
## 120 0.4585 nan 0.5000 -0.0095
## 140 0.4048 nan 0.5000 -0.0038
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
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## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1243 nan 0.5000 0.0619
## 2 1.0308 nan 0.5000 0.0322
## 3 0.9870 nan 0.5000 0.0107
## 4 0.9663 nan 0.5000 -0.0123
## 5 0.9294 nan 0.5000 -0.0016
## 6 0.9052 nan 0.5000 0.0031
## 7 0.8886 nan 0.5000 0.0010
## 8 0.8702 nan 0.5000 -0.0043
## 9 0.8605 nan 0.5000 -0.0062
## 10 0.8530 nan 0.5000 -0.0049
## 20 0.7821 nan 0.5000 -0.0132
## 40 0.6752 nan 0.5000 -0.0023
## 60 0.5936 nan 0.5000 -0.0006
## 80 0.5339 nan 0.5000 -0.0001
## 100 0.4934 nan 0.5000 -0.0077
## 120 0.4728 nan 0.5000 -0.0102
## 140 0.4122 nan 0.5000 -0.0065
## 160 0.3677 nan 0.5000 -0.0040
## 180 0.3245 nan 0.5000 -0.0034
## 200 0.2864 nan 0.5000 -0.0020
## 220 0.2597 nan 0.5000 -0.0048
## 240 0.2352 nan 0.5000 -0.0024
## 260 0.2147 nan 0.5000 -0.0010
## 280 0.1908 nan 0.5000 -0.0042
## 300 0.1763 nan 0.5000 -0.0015
## 320 0.1632 nan 0.5000 -0.0028
## 340 0.1507 nan 0.5000 -0.0013
## 360 0.1394 nan 0.5000 -0.0013
## 380 0.1314 nan 0.5000 -0.0032
## 400 0.1204 nan 0.5000 -0.0011
## 420 0.1113 nan 0.5000 -0.0011
## 440 0.1031 nan 0.5000 -0.0019
## 460 0.0947 nan 0.5000 -0.0006
## 480 0.0846 nan 0.5000 -0.0008
## 500 0.0784 nan 0.5000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0995 nan 0.5000 0.0840
## 2 1.0316 nan 0.5000 0.0165
## 3 0.9689 nan 0.5000 0.0141
## 4 0.9382 nan 0.5000 -0.0013
## 5 0.8976 nan 0.5000 0.0122
## 6 0.8663 nan 0.5000 -0.0132
## 7 0.8431 nan 0.5000 0.0003
## 8 0.8293 nan 0.5000 -0.0113
## 9 0.8182 nan 0.5000 -0.0069
## 10 0.8241 nan 0.5000 -0.0233
## 20 0.7113 nan 0.5000 -0.0111
## 40 0.5467 nan 0.5000 -0.0031
## 60 inf nan 0.5000 -0.0111
## 80 inf nan 0.5000 -0.0055
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1195 nan 0.5000 0.0877
## 2 1.0171 nan 0.5000 0.0392
## 3 0.9644 nan 0.5000 0.0154
## 4 0.9371 nan 0.5000 -0.0019
## 5 0.8996 nan 0.5000 -0.0048
## 6 0.8706 nan 0.5000 -0.0013
## 7 0.8626 nan 0.5000 -0.0088
## 8 0.8602 nan 0.5000 -0.0236
## 9 0.8391 nan 0.5000 -0.0068
## 10 0.8244 nan 0.5000 -0.0108
## 20 0.6968 nan 0.5000 -0.0167
## 40 0.5663 nan 0.5000 -0.0069
## 60 0.4628 nan 0.5000 -0.0045
## 80 0.3742 nan 0.5000 -0.0000
## 100 0.3037 nan 0.5000 -0.0080
## 120 0.2368 nan 0.5000 -0.0036
## 140 0.2017 nan 0.5000 -0.0019
## 160 0.1630 nan 0.5000 -0.0003
## 180 0.1378 nan 0.5000 -0.0017
## 200 0.1171 nan 0.5000 -0.0002
## 220 0.1056 nan 0.5000 -0.0038
## 240 0.0915 nan 0.5000 -0.0016
## 260 0.0780 nan 0.5000 -0.0013
## 280 0.0682 nan 0.5000 -0.0004
## 300 0.0600 nan 0.5000 -0.0012
## 320 0.0532 nan 0.5000 -0.0007
## 340 0.0470 nan 0.5000 -0.0011
## 360 0.0408 nan 0.5000 -0.0000
## 380 0.0377 nan 0.5000 -0.0006
## 400 0.0324 nan 0.5000 -0.0005
## 420 0.0285 nan 0.5000 -0.0003
## 440 0.0258 nan 0.5000 -0.0002
## 460 0.0231 nan 0.5000 -0.0001
## 480 0.0197 nan 0.5000 -0.0004
## 500 0.0170 nan 0.5000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0806 nan 0.5000 0.0817
## 2 0.9938 nan 0.5000 0.0213
## 3 0.9474 nan 0.5000 0.0097
## 4 0.9071 nan 0.5000 0.0013
## 5 0.8885 nan 0.5000 -0.0119
## 6 0.8669 nan 0.5000 -0.0068
## 7 0.8440 nan 0.5000 -0.0053
## 8 0.8272 nan 0.5000 -0.0035
## 9 0.8151 nan 0.5000 -0.0072
## 10 0.8072 nan 0.5000 -0.0033
## 20 0.7283 nan 0.5000 -0.0084
## 40 0.5781 nan 0.5000 -0.0026
## 60 0.4629 nan 0.5000 -0.0072
## 80 0.3848 nan 0.5000 -0.0037
## 100 0.3064 nan 0.5000 0.0001
## 120 0.2724 nan 0.5000 -0.0008
## 140 223.3358 nan 0.5000 0.5598
## 160 223.3082 nan 0.5000 -0.0008
## 180 223.2906 nan 0.5000 -0.0013
## 200 223.2688 nan 0.5000 -0.0017
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1140 nan 1.0000 0.0923
## 2 1.0563 nan 1.0000 0.0201
## 3 0.9951 nan 1.0000 0.0143
## 4 0.9585 nan 1.0000 0.0127
## 5 0.9565 nan 1.0000 -0.0275
## 6 0.9805 nan 1.0000 -0.0468
## 7 1.0111 nan 1.0000 -0.0465
## 8 1.1297 nan 1.0000 -0.0182
## 9 5.6244 nan 1.0000 -3.0503
## 10 5.5970 nan 1.0000 0.0128
## 20 5.5434 nan 1.0000 0.0016
## 40 5.3270 nan 1.0000 -0.0156
## 60 5.3159 nan 1.0000 -0.0224
## 80 5.2456 nan 1.0000 -0.0158
## 100 6517510314578.6992 nan 1.0000 -0.0020
## 120 6517510314579.3672 nan 1.0000 0.0007
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1239 nan 1.0000 0.0741
## 2 1.0584 nan 1.0000 0.0211
## 3 1.0036 nan 1.0000 0.0179
## 4 0.9606 nan 1.0000 0.0177
## 5 0.9613 nan 1.0000 -0.0149
## 6 0.9647 nan 1.0000 -0.0168
## 7 0.9725 nan 1.0000 -0.0246
## 8 0.9461 nan 1.0000 0.0095
## 9 0.9373 nan 1.0000 -0.0099
## 10 0.9403 nan 1.0000 -0.0218
## 20 163438.9410 nan 1.0000 -0.0156
## 40 163438.9014 nan 1.0000 -0.0219
## 60 163438.9025 nan 1.0000 -0.0091
## 80 163438.9653 nan 1.0000 -0.1053
## 100 163454.1726 nan 1.0000 0.0018
## 120 163453.5858 nan 1.0000 -0.0033
## 140 163453.5845 nan 1.0000 -0.0030
## 160 163453.5415 nan 1.0000 -0.0337
## 180 163453.5737 nan 1.0000 -0.0041
## 200 163453.5668 nan 1.0000 -0.0119
## 220 163453.5275 nan 1.0000 0.0062
## 240 163703.9984 nan 1.0000 0.0075
## 260 163703.9950 nan 1.0000 -0.0059
## 280 163703.9865 nan 1.0000 -0.0579
## 300 163703.9545 nan 1.0000 0.0001
## 320 163703.9532 nan 1.0000 0.0012
## 340 226897.9306 nan 1.0000 -0.0001
## 360 226897.9369 nan 1.0000 0.0000
## 380 226897.9479 nan 1.0000 0.0007
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1213 nan 1.0000 0.0892
## 2 1.0611 nan 1.0000 0.0191
## 3 1.0125 nan 1.0000 0.0174
## 4 0.9968 nan 1.0000 -0.0085
## 5 1.0001 nan 1.0000 -0.0270
## 6 0.9953 nan 1.0000 -0.0224
## 7 0.9554 nan 1.0000 0.0060
## 8 0.9570 nan 1.0000 -0.0214
## 9 0.9385 nan 1.0000 0.0017
## 10 0.9372 nan 1.0000 -0.0150
## 20 1.0574 nan 1.0000 -0.0254
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0571 nan 1.0000 0.0973
## 2 0.9821 nan 1.0000 -0.0000
## 3 0.9690 nan 1.0000 -0.0211
## 4 0.9702 nan 1.0000 -0.0285
## 5 0.9678 nan 1.0000 -0.0273
## 6 0.9832 nan 1.0000 -0.0365
## 7 0.9643 nan 1.0000 -0.0132
## 8 0.9482 nan 1.0000 -0.0219
## 9 0.9621 nan 1.0000 -0.0448
## 10 0.9203 nan 1.0000 0.0112
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0484 nan 1.0000 0.1103
## 2 0.9828 nan 1.0000 -0.0031
## 3 0.9944 nan 1.0000 -0.0598
## 4 0.9420 nan 1.0000 0.0215
## 5 1.0143 nan 1.0000 -0.1116
## 6 1.1866 nan 1.0000 -0.2061
## 7 inf nan 1.0000 -inf
## 8 inf nan 1.0000 nan
## 9 inf nan 1.0000 nan
## 10 inf nan 1.0000 nan
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0658 nan 1.0000 0.1032
## 2 0.9735 nan 1.0000 0.0357
## 3 0.9581 nan 1.0000 -0.0183
## 4 1.0771 nan 1.0000 -0.1233
## 5 1.0456 nan 1.0000 -0.0017
## 6 1.0100 nan 1.0000 0.0038
## 7 inf nan 1.0000 -inf
## 8 inf nan 1.0000 nan
## 9 inf nan 1.0000 nan
## 10 inf nan 1.0000 nan
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## 360 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0203 nan 1.0000 0.1248
## 2 0.9504 nan 1.0000 -0.0101
## 3 0.9459 nan 1.0000 -0.0352
## 4 0.9435 nan 1.0000 -0.0457
## 5 0.9158 nan 1.0000 -0.0238
## 6 0.8850 nan 1.0000 -0.0222
## 7 0.9508 nan 1.0000 -0.0916
## 8 0.9396 nan 1.0000 -0.0430
## 9 0.8694 nan 1.0000 0.0025
## 10 0.8474 nan 1.0000 -0.0204
## 20 1.4553 nan 1.0000 -0.0191
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## 200 inf nan 1.0000 nan
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## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0432 nan 1.0000 0.0964
## 2 0.9659 nan 1.0000 0.0111
## 3 0.9517 nan 1.0000 -0.0285
## 4 0.9410 nan 1.0000 -0.0286
## 5 0.8997 nan 1.0000 -0.0067
## 6 0.8988 nan 1.0000 -0.0322
## 7 0.8818 nan 1.0000 -0.0239
## 8 0.8423 nan 1.0000 -0.0025
## 9 0.8542 nan 1.0000 -0.0498
## 10 0.8266 nan 1.0000 -0.0035
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0209 nan 1.0000 0.1231
## 2 0.9496 nan 1.0000 -0.0088
## 3 0.9186 nan 1.0000 -0.0247
## 4 0.9252 nan 1.0000 -0.0345
## 5 0.9424 nan 1.0000 -0.0556
## 6 1.1138 nan 1.0000 -0.2296
## 7 1.0930 nan 1.0000 -0.0307
## 8 3.3595 nan 1.0000 -1.9786
## 9 4.0013 nan 1.0000 -0.5752
## 10 191267.1195 nan 1.0000 -95493.4622
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
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## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
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## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
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## 40 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 40 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2927 nan 0.0010 0.0001
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0001
## 5 1.2915 nan 0.0010 0.0001
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2905 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2860 nan 0.0010 0.0001
## 40 1.2792 nan 0.0010 0.0001
## 60 1.2724 nan 0.0010 0.0002
## 80 1.2659 nan 0.0010 0.0001
## 100 1.2595 nan 0.0010 0.0001
## 120 1.2535 nan 0.0010 0.0001
## 140 1.2479 nan 0.0010 0.0001
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## 380 1.1916 nan 0.0010 0.0001
## 400 1.1879 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0001
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0001
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2794 nan 0.0010 0.0002
## 60 1.2729 nan 0.0010 0.0002
## 80 1.2667 nan 0.0010 0.0001
## 100 1.2605 nan 0.0010 0.0001
## 120 1.2545 nan 0.0010 0.0001
## 140 1.2488 nan 0.0010 0.0001
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## 340 1.2001 nan 0.0010 0.0001
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## 380 1.1920 nan 0.0010 0.0001
## 400 1.1881 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2914 nan 0.0010 0.0002
## 6 1.2910 nan 0.0010 0.0002
## 7 1.2906 nan 0.0010 0.0001
## 8 1.2903 nan 0.0010 0.0001
## 9 1.2899 nan 0.0010 0.0002
## 10 1.2895 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2792 nan 0.0010 0.0001
## 60 1.2728 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
## 9 1.2891 nan 0.0010 0.0002
## 10 1.2886 nan 0.0010 0.0002
## 20 1.2839 nan 0.0010 0.0002
## 40 1.2752 nan 0.0010 0.0002
## 60 1.2666 nan 0.0010 0.0002
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## 140 1.2359 nan 0.0010 0.0002
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## 220 1.2080 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2906 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
## 9 1.2892 nan 0.0010 0.0002
## 10 1.2887 nan 0.0010 0.0002
## 20 1.2842 nan 0.0010 0.0002
## 40 1.2749 nan 0.0010 0.0002
## 60 1.2663 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2906 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
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## 10 1.2887 nan 0.0010 0.0002
## 20 1.2841 nan 0.0010 0.0002
## 40 1.2752 nan 0.0010 0.0002
## 60 1.2666 nan 0.0010 0.0002
## 80 1.2581 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0003
## 4 1.2913 nan 0.0010 0.0002
## 5 1.2908 nan 0.0010 0.0002
## 6 1.2902 nan 0.0010 0.0002
## 7 1.2897 nan 0.0010 0.0003
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## 10 1.2881 nan 0.0010 0.0003
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## 40 1.2726 nan 0.0010 0.0002
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## 180 1.2096 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0003
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2907 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0003
## 7 1.2897 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0003
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2907 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0002
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## 10 1.2880 nan 0.0010 0.0003
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## 40 1.2730 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2623 nan 0.1000 0.0159
## 2 1.2328 nan 0.1000 0.0127
## 3 1.2086 nan 0.1000 0.0119
## 4 1.1878 nan 0.1000 0.0088
## 5 1.1702 nan 0.1000 0.0072
## 6 1.1550 nan 0.1000 0.0048
## 7 1.1396 nan 0.1000 0.0053
## 8 1.1260 nan 0.1000 0.0054
## 9 1.1142 nan 0.1000 0.0042
## 10 1.1007 nan 0.1000 0.0052
## 20 1.0120 nan 0.1000 0.0018
## 40 0.9325 nan 0.1000 0.0020
## 60 0.8919 nan 0.1000 -0.0010
## 80 0.8690 nan 0.1000 -0.0004
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## 140 0.8215 nan 0.1000 -0.0009
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## 180 0.8051 nan 0.1000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2545 nan 0.1000 0.0168
## 2 1.2272 nan 0.1000 0.0125
## 3 1.2042 nan 0.1000 0.0107
## 4 1.1843 nan 0.1000 0.0094
## 5 1.1650 nan 0.1000 0.0065
## 6 1.1478 nan 0.1000 0.0071
## 7 1.1341 nan 0.1000 0.0047
## 8 1.1203 nan 0.1000 0.0053
## 9 1.1080 nan 0.1000 0.0055
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## 40 0.9303 nan 0.1000 0.0005
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## 280 0.7590 nan 0.1000 -0.0006
## 300 0.7548 nan 0.1000 -0.0013
## 320 0.7479 nan 0.1000 -0.0009
## 340 0.7450 nan 0.1000 -0.0008
## 360 0.7367 nan 0.1000 -0.0007
## 380 0.7307 nan 0.1000 -0.0007
## 400 0.7262 nan 0.1000 -0.0012
## 420 0.7215 nan 0.1000 -0.0007
## 440 0.7170 nan 0.1000 -0.0016
## 460 0.7133 nan 0.1000 -0.0008
## 480 0.7103 nan 0.1000 -0.0002
## 500 0.7052 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2536 nan 0.1000 0.0156
## 2 1.2255 nan 0.1000 0.0125
## 3 1.2049 nan 0.1000 0.0110
## 4 1.1851 nan 0.1000 0.0075
## 5 1.1677 nan 0.1000 0.0063
## 6 1.1510 nan 0.1000 0.0054
## 7 1.1371 nan 0.1000 0.0039
## 8 1.1263 nan 0.1000 0.0033
## 9 1.1103 nan 0.1000 0.0067
## 10 1.0978 nan 0.1000 0.0052
## 20 1.0128 nan 0.1000 0.0011
## 40 0.9261 nan 0.1000 0.0001
## 60 0.8876 nan 0.1000 0.0000
## 80 0.8625 nan 0.1000 -0.0002
## 100 0.8453 nan 0.1000 -0.0014
## 120 0.8277 nan 0.1000 -0.0004
## 140 0.8175 nan 0.1000 -0.0017
## 160 0.8079 nan 0.1000 -0.0010
## 180 0.7998 nan 0.1000 -0.0009
## 200 0.7925 nan 0.1000 -0.0016
## 220 0.7865 nan 0.1000 -0.0005
## 240 0.7808 nan 0.1000 -0.0006
## 260 0.7741 nan 0.1000 -0.0005
## 280 0.7672 nan 0.1000 -0.0008
## 300 0.7595 nan 0.1000 -0.0007
## 320 0.7553 nan 0.1000 -0.0033
## 340 0.7509 nan 0.1000 -0.0011
## 360 0.7458 nan 0.1000 -0.0008
## 380 0.7411 nan 0.1000 -0.0007
## 400 0.7375 nan 0.1000 -0.0019
## 420 0.7323 nan 0.1000 -0.0004
## 440 0.7289 nan 0.1000 -0.0010
## 460 0.7245 nan 0.1000 -0.0002
## 480 0.7196 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2511 nan 0.1000 0.0185
## 2 1.2125 nan 0.1000 0.0157
## 3 1.1773 nan 0.1000 0.0158
## 4 1.1463 nan 0.1000 0.0151
## 5 1.1266 nan 0.1000 0.0079
## 6 1.1072 nan 0.1000 0.0074
## 7 1.0844 nan 0.1000 0.0085
## 8 1.0650 nan 0.1000 0.0066
## 9 1.0496 nan 0.1000 0.0055
## 10 1.0363 nan 0.1000 0.0043
## 20 0.9489 nan 0.1000 -0.0011
## 40 0.8606 nan 0.1000 -0.0006
## 60 0.8148 nan 0.1000 -0.0001
## 80 0.7807 nan 0.1000 -0.0013
## 100 0.7449 nan 0.1000 -0.0001
## 120 0.7222 nan 0.1000 0.0002
## 140 0.7012 nan 0.1000 -0.0023
## 160 0.6815 nan 0.1000 -0.0004
## 180 0.6654 nan 0.1000 -0.0007
## 200 0.6467 nan 0.1000 -0.0010
## 220 0.6270 nan 0.1000 -0.0011
## 240 0.6133 nan 0.1000 -0.0008
## 260 0.6008 nan 0.1000 -0.0014
## 280 0.5837 nan 0.1000 -0.0006
## 300 0.5692 nan 0.1000 -0.0015
## 320 0.5532 nan 0.1000 -0.0010
## 340 0.5399 nan 0.1000 -0.0006
## 360 0.5268 nan 0.1000 -0.0006
## 380 0.5145 nan 0.1000 -0.0008
## 400 0.5035 nan 0.1000 -0.0006
## 420 0.4920 nan 0.1000 -0.0010
## 440 0.4847 nan 0.1000 -0.0012
## 460 0.4750 nan 0.1000 -0.0007
## 480 0.4633 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2491 nan 0.1000 0.0212
## 2 1.2102 nan 0.1000 0.0176
## 3 1.1794 nan 0.1000 0.0146
## 4 1.1545 nan 0.1000 0.0097
## 5 1.1270 nan 0.1000 0.0134
## 6 1.1066 nan 0.1000 0.0078
## 7 1.0850 nan 0.1000 0.0084
## 8 1.0696 nan 0.1000 0.0056
## 9 1.0509 nan 0.1000 0.0046
## 10 1.0315 nan 0.1000 0.0063
## 20 0.9470 nan 0.1000 0.0002
## 40 0.8571 nan 0.1000 0.0002
## 60 0.8118 nan 0.1000 -0.0001
## 80 0.7803 nan 0.1000 -0.0010
## 100 0.7477 nan 0.1000 -0.0014
## 120 0.7268 nan 0.1000 -0.0014
## 140 0.7033 nan 0.1000 -0.0022
## 160 0.6853 nan 0.1000 -0.0008
## 180 0.6688 nan 0.1000 -0.0020
## 200 0.6500 nan 0.1000 -0.0009
## 220 0.6338 nan 0.1000 -0.0016
## 240 0.6119 nan 0.1000 -0.0004
## 260 0.5975 nan 0.1000 -0.0009
## 280 0.5828 nan 0.1000 -0.0010
## 300 0.5699 nan 0.1000 -0.0010
## 320 0.5533 nan 0.1000 -0.0006
## 340 0.5346 nan 0.1000 -0.0003
## 360 0.5237 nan 0.1000 -0.0004
## 380 0.5115 nan 0.1000 -0.0011
## 400 0.5000 nan 0.1000 -0.0009
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## 460 0.4684 nan 0.1000 -0.0015
## 480 0.4556 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2466 nan 0.1000 0.0190
## 2 1.2124 nan 0.1000 0.0138
## 3 1.1825 nan 0.1000 0.0118
## 4 1.1532 nan 0.1000 0.0126
## 5 1.1304 nan 0.1000 0.0094
## 6 1.1121 nan 0.1000 0.0078
## 7 1.0929 nan 0.1000 0.0078
## 8 1.0765 nan 0.1000 0.0068
## 9 1.0610 nan 0.1000 0.0077
## 10 1.0456 nan 0.1000 0.0047
## 20 0.9515 nan 0.1000 0.0019
## 40 0.8592 nan 0.1000 -0.0002
## 60 0.8077 nan 0.1000 -0.0014
## 80 0.7789 nan 0.1000 -0.0013
## 100 0.7527 nan 0.1000 -0.0006
## 120 0.7253 nan 0.1000 -0.0015
## 140 0.7033 nan 0.1000 -0.0010
## 160 0.6865 nan 0.1000 -0.0015
## 180 0.6647 nan 0.1000 -0.0011
## 200 0.6474 nan 0.1000 -0.0012
## 220 0.6278 nan 0.1000 -0.0012
## 240 0.6123 nan 0.1000 -0.0011
## 260 0.5992 nan 0.1000 -0.0009
## 280 0.5800 nan 0.1000 -0.0019
## 300 0.5653 nan 0.1000 -0.0013
## 320 0.5533 nan 0.1000 -0.0006
## 340 0.5406 nan 0.1000 -0.0020
## 360 0.5282 nan 0.1000 -0.0004
## 380 0.5149 nan 0.1000 -0.0009
## 400 0.5011 nan 0.1000 -0.0012
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## 440 0.4774 nan 0.1000 -0.0007
## 460 0.4692 nan 0.1000 -0.0000
## 480 0.4604 nan 0.1000 -0.0014
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2463 nan 0.1000 0.0211
## 2 1.2064 nan 0.1000 0.0191
## 3 1.1720 nan 0.1000 0.0168
## 4 1.1378 nan 0.1000 0.0132
## 5 1.1111 nan 0.1000 0.0098
## 6 1.0828 nan 0.1000 0.0102
## 7 1.0595 nan 0.1000 0.0066
## 8 1.0401 nan 0.1000 0.0056
## 9 1.0212 nan 0.1000 0.0046
## 10 1.0042 nan 0.1000 0.0054
## 20 0.9028 nan 0.1000 0.0002
## 40 0.8074 nan 0.1000 -0.0007
## 60 0.7520 nan 0.1000 -0.0013
## 80 0.7119 nan 0.1000 -0.0018
## 100 0.6729 nan 0.1000 -0.0012
## 120 0.6389 nan 0.1000 -0.0011
## 140 0.6029 nan 0.1000 -0.0015
## 160 0.5708 nan 0.1000 -0.0017
## 180 0.5354 nan 0.1000 -0.0017
## 200 0.5128 nan 0.1000 -0.0019
## 220 0.4931 nan 0.1000 -0.0014
## 240 0.4729 nan 0.1000 -0.0010
## 260 0.4524 nan 0.1000 -0.0013
## 280 0.4350 nan 0.1000 -0.0007
## 300 0.4172 nan 0.1000 -0.0013
## 320 0.4027 nan 0.1000 -0.0016
## 340 0.3857 nan 0.1000 -0.0000
## 360 0.3710 nan 0.1000 -0.0005
## 380 0.3549 nan 0.1000 -0.0010
## 400 0.3412 nan 0.1000 -0.0016
## 420 0.3304 nan 0.1000 -0.0010
## 440 0.3193 nan 0.1000 -0.0004
## 460 0.3071 nan 0.1000 -0.0001
## 480 0.2975 nan 0.1000 -0.0008
## 500 0.2847 nan 0.1000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2443 nan 0.1000 0.0232
## 2 1.2026 nan 0.1000 0.0194
## 3 1.1625 nan 0.1000 0.0156
## 4 1.1303 nan 0.1000 0.0145
## 5 1.1020 nan 0.1000 0.0124
## 6 1.0746 nan 0.1000 0.0085
## 7 1.0546 nan 0.1000 0.0080
## 8 1.0349 nan 0.1000 0.0081
## 9 1.0171 nan 0.1000 0.0075
## 10 1.0027 nan 0.1000 0.0043
## 20 0.9040 nan 0.1000 -0.0005
## 40 0.8156 nan 0.1000 -0.0004
## 60 0.7624 nan 0.1000 -0.0018
## 80 0.7121 nan 0.1000 -0.0008
## 100 0.6722 nan 0.1000 -0.0019
## 120 0.6370 nan 0.1000 -0.0012
## 140 0.6026 nan 0.1000 -0.0027
## 160 0.5709 nan 0.1000 -0.0012
## 180 0.5420 nan 0.1000 -0.0012
## 200 0.5224 nan 0.1000 -0.0008
## 220 0.4959 nan 0.1000 -0.0009
## 240 0.4768 nan 0.1000 -0.0009
## 260 0.4565 nan 0.1000 -0.0009
## 280 0.4400 nan 0.1000 -0.0011
## 300 0.4201 nan 0.1000 -0.0006
## 320 0.4017 nan 0.1000 -0.0011
## 340 0.3864 nan 0.1000 -0.0010
## 360 0.3704 nan 0.1000 -0.0008
## 380 0.3584 nan 0.1000 -0.0013
## 400 0.3434 nan 0.1000 -0.0007
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## 460 0.3063 nan 0.1000 -0.0003
## 480 0.2951 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2413 nan 0.1000 0.0252
## 2 1.2005 nan 0.1000 0.0164
## 3 1.1685 nan 0.1000 0.0146
## 4 1.1353 nan 0.1000 0.0111
## 5 1.1030 nan 0.1000 0.0136
## 6 1.0819 nan 0.1000 0.0086
## 7 1.0609 nan 0.1000 0.0078
## 8 1.0417 nan 0.1000 0.0067
## 9 1.0239 nan 0.1000 0.0063
## 10 1.0120 nan 0.1000 0.0015
## 20 0.9052 nan 0.1000 0.0003
## 40 0.8123 nan 0.1000 -0.0009
## 60 0.7609 nan 0.1000 -0.0003
## 80 0.7193 nan 0.1000 -0.0013
## 100 0.6831 nan 0.1000 -0.0013
## 120 0.6497 nan 0.1000 -0.0016
## 140 0.6165 nan 0.1000 -0.0007
## 160 0.5876 nan 0.1000 -0.0002
## 180 0.5580 nan 0.1000 -0.0004
## 200 0.5364 nan 0.1000 -0.0017
## 220 0.5087 nan 0.1000 -0.0001
## 240 0.4844 nan 0.1000 -0.0009
## 260 0.4650 nan 0.1000 -0.0013
## 280 0.4459 nan 0.1000 -0.0009
## 300 0.4287 nan 0.1000 0.0000
## 320 0.4139 nan 0.1000 -0.0006
## 340 0.3988 nan 0.1000 -0.0006
## 360 0.3861 nan 0.1000 -0.0004
## 380 0.3707 nan 0.1000 -0.0006
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## 460 0.3211 nan 0.1000 -0.0005
## 480 0.3084 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2274 nan 0.2000 0.0327
## 2 1.1815 nan 0.2000 0.0206
## 3 1.1499 nan 0.2000 0.0122
## 4 1.1273 nan 0.2000 0.0047
## 5 1.0916 nan 0.2000 0.0075
## 6 1.0691 nan 0.2000 0.0045
## 7 1.0512 nan 0.2000 0.0055
## 8 1.0300 nan 0.2000 0.0077
## 9 1.0156 nan 0.2000 0.0046
## 10 1.0021 nan 0.2000 0.0050
## 20 0.9301 nan 0.2000 -0.0012
## 40 0.8744 nan 0.2000 -0.0021
## 60 0.8510 nan 0.2000 -0.0008
## 80 0.8246 nan 0.2000 -0.0052
## 100 0.8023 nan 0.2000 -0.0019
## 120 0.7879 nan 0.2000 -0.0002
## 140 0.7718 nan 0.2000 -0.0005
## 160 0.7649 nan 0.2000 -0.0014
## 180 0.7590 nan 0.2000 -0.0023
## 200 0.7452 nan 0.2000 -0.0031
## 220 0.7316 nan 0.2000 -0.0013
## 240 0.7243 nan 0.2000 -0.0008
## 260 0.7183 nan 0.2000 -0.0026
## 280 0.7043 nan 0.2000 -0.0009
## 300 0.6982 nan 0.2000 -0.0025
## 320 0.6910 nan 0.2000 -0.0022
## 340 0.6853 nan 0.2000 -0.0022
## 360 0.6808 nan 0.2000 -0.0018
## 380 0.6760 nan 0.2000 -0.0016
## 400 0.6661 nan 0.2000 -0.0002
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## 440 0.6598 nan 0.2000 -0.0017
## 460 0.6513 nan 0.2000 -0.0008
## 480 0.6461 nan 0.2000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2251 nan 0.2000 0.0306
## 2 1.1804 nan 0.2000 0.0145
## 3 1.1485 nan 0.2000 0.0088
## 4 1.1206 nan 0.2000 0.0135
## 5 1.0953 nan 0.2000 0.0100
## 6 1.0759 nan 0.2000 0.0086
## 7 1.0522 nan 0.2000 0.0109
## 8 1.0368 nan 0.2000 0.0056
## 9 1.0223 nan 0.2000 0.0024
## 10 1.0106 nan 0.2000 0.0028
## 20 0.9287 nan 0.2000 -0.0008
## 40 0.8647 nan 0.2000 -0.0001
## 60 0.8341 nan 0.2000 -0.0030
## 80 0.8090 nan 0.2000 -0.0022
## 100 0.7934 nan 0.2000 -0.0025
## 120 0.7751 nan 0.2000 -0.0019
## 140 0.7663 nan 0.2000 -0.0026
## 160 0.7558 nan 0.2000 -0.0010
## 180 0.7454 nan 0.2000 -0.0017
## 200 0.7379 nan 0.2000 -0.0016
## 220 0.7281 nan 0.2000 -0.0025
## 240 0.7202 nan 0.2000 -0.0006
## 260 0.7096 nan 0.2000 -0.0015
## 280 0.7024 nan 0.2000 -0.0007
## 300 0.6992 nan 0.2000 -0.0044
## 320 0.6887 nan 0.2000 -0.0021
## 340 0.6829 nan 0.2000 -0.0002
## 360 0.6749 nan 0.2000 -0.0024
## 380 0.6702 nan 0.2000 -0.0010
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## 480 0.6474 nan 0.2000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2193 nan 0.2000 0.0303
## 2 1.1788 nan 0.2000 0.0212
## 3 1.1489 nan 0.2000 0.0118
## 4 1.1170 nan 0.2000 0.0126
## 5 1.0924 nan 0.2000 0.0103
## 6 1.0723 nan 0.2000 0.0064
## 7 1.0497 nan 0.2000 0.0088
## 8 1.0347 nan 0.2000 0.0026
## 9 1.0200 nan 0.2000 0.0053
## 10 1.0033 nan 0.2000 0.0060
## 20 0.9264 nan 0.2000 0.0007
## 40 0.8637 nan 0.2000 -0.0037
## 60 0.8377 nan 0.2000 -0.0028
## 80 0.8162 nan 0.2000 -0.0015
## 100 0.7947 nan 0.2000 -0.0012
## 120 0.7779 nan 0.2000 -0.0025
## 140 0.7638 nan 0.2000 -0.0043
## 160 0.7546 nan 0.2000 -0.0009
## 180 0.7426 nan 0.2000 -0.0005
## 200 0.7310 nan 0.2000 -0.0022
## 220 0.7243 nan 0.2000 -0.0008
## 240 0.7138 nan 0.2000 -0.0026
## 260 0.7072 nan 0.2000 -0.0020
## 280 0.6993 nan 0.2000 -0.0014
## 300 0.6888 nan 0.2000 -0.0015
## 320 0.6853 nan 0.2000 -0.0011
## 340 0.6819 nan 0.2000 -0.0006
## 360 0.6759 nan 0.2000 -0.0028
## 380 0.6701 nan 0.2000 -0.0011
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## 440 0.6562 nan 0.2000 -0.0036
## 460 0.6521 nan 0.2000 0.0001
## 480 0.6442 nan 0.2000 -0.0015
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2112 nan 0.2000 0.0339
## 2 1.1480 nan 0.2000 0.0272
## 3 1.1039 nan 0.2000 0.0192
## 4 1.0670 nan 0.2000 0.0142
## 5 1.0390 nan 0.2000 0.0121
## 6 1.0131 nan 0.2000 0.0057
## 7 0.9943 nan 0.2000 0.0052
## 8 0.9829 nan 0.2000 -0.0010
## 9 0.9680 nan 0.2000 0.0033
## 10 0.9516 nan 0.2000 0.0061
## 20 0.8728 nan 0.2000 -0.0011
## 40 0.7910 nan 0.2000 -0.0028
## 60 0.7334 nan 0.2000 -0.0025
## 80 0.7011 nan 0.2000 -0.0016
## 100 0.6641 nan 0.2000 -0.0011
## 120 0.6303 nan 0.2000 -0.0017
## 140 0.5950 nan 0.2000 -0.0007
## 160 0.5580 nan 0.2000 -0.0016
## 180 0.5301 nan 0.2000 -0.0007
## 200 0.5104 nan 0.2000 -0.0019
## 220 0.4941 nan 0.2000 -0.0019
## 240 0.4677 nan 0.2000 -0.0011
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## 280 0.4233 nan 0.2000 -0.0025
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## 320 0.3892 nan 0.2000 -0.0014
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## 380 0.3461 nan 0.2000 -0.0021
## 400 0.3325 nan 0.2000 -0.0013
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## 480 0.2895 nan 0.2000 -0.0017
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2131 nan 0.2000 0.0381
## 2 1.1492 nan 0.2000 0.0275
## 3 1.1120 nan 0.2000 0.0141
## 4 1.0755 nan 0.2000 0.0174
## 5 1.0489 nan 0.2000 0.0097
## 6 1.0175 nan 0.2000 0.0136
## 7 0.9975 nan 0.2000 0.0069
## 8 0.9798 nan 0.2000 0.0062
## 9 0.9596 nan 0.2000 0.0050
## 10 0.9479 nan 0.2000 0.0004
## 20 0.8579 nan 0.2000 -0.0016
## 40 0.7798 nan 0.2000 -0.0016
## 60 0.7305 nan 0.2000 -0.0017
## 80 0.6774 nan 0.2000 -0.0024
## 100 0.6382 nan 0.2000 -0.0004
## 120 0.6057 nan 0.2000 -0.0054
## 140 0.5823 nan 0.2000 -0.0020
## 160 0.5531 nan 0.2000 -0.0015
## 180 0.5297 nan 0.2000 -0.0031
## 200 0.5051 nan 0.2000 -0.0017
## 220 0.4807 nan 0.2000 -0.0011
## 240 0.4590 nan 0.2000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2114 nan 0.2000 0.0385
## 2 1.1560 nan 0.2000 0.0231
## 3 1.1064 nan 0.2000 0.0210
## 4 1.0684 nan 0.2000 0.0105
## 5 1.0390 nan 0.2000 0.0103
## 6 1.0133 nan 0.2000 0.0082
## 7 0.9899 nan 0.2000 0.0093
## 8 0.9684 nan 0.2000 0.0055
## 9 0.9567 nan 0.2000 0.0001
## 10 0.9439 nan 0.2000 0.0030
## 20 0.8698 nan 0.2000 -0.0006
## 40 0.7944 nan 0.2000 -0.0018
## 60 0.7471 nan 0.2000 -0.0039
## 80 0.7038 nan 0.2000 -0.0003
## 100 0.6651 nan 0.2000 -0.0010
## 120 0.6354 nan 0.2000 -0.0007
## 140 0.6058 nan 0.2000 -0.0031
## 160 0.5808 nan 0.2000 -0.0028
## 180 0.5569 nan 0.2000 -0.0027
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## 380 0.3619 nan 0.2000 -0.0007
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## 480 0.3021 nan 0.2000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1931 nan 0.2000 0.0456
## 2 1.1224 nan 0.2000 0.0301
## 3 1.0722 nan 0.2000 0.0202
## 4 1.0345 nan 0.2000 0.0120
## 5 0.9990 nan 0.2000 0.0138
## 6 0.9673 nan 0.2000 0.0128
## 7 0.9436 nan 0.2000 0.0052
## 8 0.9196 nan 0.2000 0.0083
## 9 0.9064 nan 0.2000 0.0017
## 10 0.8956 nan 0.2000 -0.0023
## 20 0.8008 nan 0.2000 -0.0008
## 40 0.7011 nan 0.2000 -0.0010
## 60 0.6239 nan 0.2000 -0.0019
## 80 0.5710 nan 0.2000 -0.0033
## 100 0.5164 nan 0.2000 -0.0007
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## 140 0.4434 nan 0.2000 0.0002
## 160 0.4136 nan 0.2000 -0.0008
## 180 0.3785 nan 0.2000 -0.0027
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## 220 0.3345 nan 0.2000 -0.0032
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## 440 0.1598 nan 0.2000 -0.0018
## 460 0.1507 nan 0.2000 -0.0009
## 480 0.1421 nan 0.2000 -0.0003
## 500 0.1370 nan 0.2000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1991 nan 0.2000 0.0354
## 2 1.1284 nan 0.2000 0.0369
## 3 1.0786 nan 0.2000 0.0205
## 4 1.0396 nan 0.2000 0.0143
## 5 1.0045 nan 0.2000 0.0150
## 6 0.9815 nan 0.2000 0.0034
## 7 0.9585 nan 0.2000 0.0034
## 8 0.9410 nan 0.2000 0.0004
## 9 0.9209 nan 0.2000 0.0055
## 10 0.9089 nan 0.2000 -0.0002
## 20 0.8164 nan 0.2000 -0.0015
## 40 0.7237 nan 0.2000 -0.0030
## 60 0.6536 nan 0.2000 -0.0021
## 80 0.5810 nan 0.2000 -0.0016
## 100 0.5376 nan 0.2000 -0.0023
## 120 0.4975 nan 0.2000 -0.0026
## 140 0.4608 nan 0.2000 -0.0029
## 160 0.4228 nan 0.2000 -0.0033
## 180 0.3893 nan 0.2000 -0.0009
## 200 0.3732 nan 0.2000 -0.0180
## 220 0.3223 nan 0.2000 -0.0021
## 240 0.3007 nan 0.2000 -0.0019
## 260 0.2782 nan 0.2000 -0.0025
## 280 0.2538 nan 0.2000 -0.0011
## 300 0.2367 nan 0.2000 -0.0008
## 320 0.2186 nan 0.2000 -0.0002
## 340 0.2022 nan 0.2000 -0.0011
## 360 0.1920 nan 0.2000 -0.0014
## 380 0.1785 nan 0.2000 -0.0010
## 400 0.1671 nan 0.2000 -0.0009
## 420 0.1570 nan 0.2000 -0.0008
## 440 0.1481 nan 0.2000 -0.0011
## 460 0.1391 nan 0.2000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1989 nan 0.2000 0.0370
## 2 1.1311 nan 0.2000 0.0226
## 3 1.0767 nan 0.2000 0.0245
## 4 1.0309 nan 0.2000 0.0132
## 5 1.0043 nan 0.2000 0.0088
## 6 0.9802 nan 0.2000 0.0051
## 7 0.9553 nan 0.2000 0.0060
## 8 0.9361 nan 0.2000 0.0056
## 9 0.9189 nan 0.2000 0.0052
## 10 0.9044 nan 0.2000 0.0022
## 20 0.8081 nan 0.2000 0.0012
## 40 0.7131 nan 0.2000 -0.0015
## 60 0.6474 nan 0.2000 -0.0036
## 80 0.5889 nan 0.2000 -0.0004
## 100 0.5362 nan 0.2000 -0.0036
## 120 0.4912 nan 0.2000 -0.0012
## 140 0.4408 nan 0.2000 -0.0023
## 160 0.4102 nan 0.2000 -0.0032
## 180 0.3816 nan 0.2000 -0.0006
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## 220 0.3303 nan 0.2000 -0.0032
## 240 0.3070 nan 0.2000 -0.0018
## 260 0.2860 nan 0.2000 -0.0007
## 280 0.2677 nan 0.2000 -0.0012
## 300 0.2524 nan 0.2000 -0.0012
## 320 0.2319 nan 0.2000 -0.0019
## 340 0.2137 nan 0.2000 -0.0005
## 360 0.2005 nan 0.2000 -0.0021
## 380 0.1902 nan 0.2000 -0.0011
## 400 0.1772 nan 0.2000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2031 nan 0.3000 0.0418
## 2 1.1495 nan 0.3000 0.0213
## 3 1.1026 nan 0.3000 0.0141
## 4 1.0689 nan 0.3000 0.0134
## 5 1.0422 nan 0.3000 0.0142
## 6 1.0238 nan 0.3000 0.0062
## 7 1.0023 nan 0.3000 0.0079
## 8 0.9881 nan 0.3000 0.0040
## 9 0.9713 nan 0.3000 0.0051
## 10 0.9590 nan 0.3000 0.0026
## 20 0.9097 nan 0.3000 -0.0002
## 40 0.8394 nan 0.3000 -0.0002
## 60 0.8028 nan 0.3000 -0.0031
## 80 0.7873 nan 0.3000 -0.0007
## 100 0.7746 nan 0.3000 0.0003
## 120 0.7633 nan 0.3000 -0.0034
## 140 0.7463 nan 0.3000 -0.0020
## 160 0.7294 nan 0.3000 -0.0023
## 180 0.7175 nan 0.3000 -0.0067
## 200 0.7038 nan 0.3000 -0.0020
## 220 0.6996 nan 0.3000 -0.0036
## 240 0.6813 nan 0.3000 -0.0016
## 260 0.6708 nan 0.3000 -0.0034
## 280 0.6591 nan 0.3000 -0.0037
## 300 0.6531 nan 0.3000 -0.0036
## 320 0.6468 nan 0.3000 -0.0047
## 340 0.6367 nan 0.3000 -0.0044
## 360 0.6321 nan 0.3000 -0.0035
## 380 0.6271 nan 0.3000 -0.0027
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## 440 0.6084 nan 0.3000 -0.0024
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1908 nan 0.3000 0.0397
## 2 1.1498 nan 0.3000 0.0150
## 3 1.1177 nan 0.3000 0.0063
## 4 1.0863 nan 0.3000 0.0133
## 5 1.0521 nan 0.3000 0.0144
## 6 1.0310 nan 0.3000 0.0041
## 7 1.0105 nan 0.3000 0.0100
## 8 0.9998 nan 0.3000 0.0006
## 9 0.9892 nan 0.3000 0.0006
## 10 0.9725 nan 0.3000 0.0062
## 20 0.8924 nan 0.3000 0.0006
## 40 0.8431 nan 0.3000 0.0008
## 60 0.8163 nan 0.3000 -0.0027
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## 100 0.7784 nan 0.3000 -0.0015
## 120 0.7618 nan 0.3000 -0.0027
## 140 0.7436 nan 0.3000 -0.0029
## 160 0.7328 nan 0.3000 -0.0011
## 180 0.7176 nan 0.3000 -0.0029
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## 220 0.6945 nan 0.3000 -0.0017
## 240 0.6849 nan 0.3000 -0.0034
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## 280 0.6631 nan 0.3000 -0.0036
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## 320 0.6510 nan 0.3000 -0.0020
## 340 0.6454 nan 0.3000 -0.0039
## 360 0.6377 nan 0.3000 -0.0037
## 380 0.6309 nan 0.3000 -0.0025
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## 460 0.6033 nan 0.3000 -0.0046
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2010 nan 0.3000 0.0448
## 2 1.1465 nan 0.3000 0.0223
## 3 1.1093 nan 0.3000 0.0121
## 4 1.0732 nan 0.3000 0.0145
## 5 1.0467 nan 0.3000 0.0088
## 6 1.0311 nan 0.3000 0.0041
## 7 1.0068 nan 0.3000 0.0092
## 8 0.9933 nan 0.3000 0.0006
## 9 0.9735 nan 0.3000 0.0068
## 10 0.9567 nan 0.3000 0.0059
## 20 0.8833 nan 0.3000 -0.0006
## 40 0.8294 nan 0.3000 -0.0055
## 60 0.8012 nan 0.3000 -0.0022
## 80 0.7826 nan 0.3000 -0.0038
## 100 0.7642 nan 0.3000 -0.0002
## 120 0.7464 nan 0.3000 -0.0040
## 140 0.7302 nan 0.3000 -0.0052
## 160 0.7141 nan 0.3000 0.0001
## 180 0.7010 nan 0.3000 -0.0026
## 200 0.6942 nan 0.3000 -0.0042
## 220 0.6852 nan 0.3000 -0.0065
## 240 0.6715 nan 0.3000 -0.0032
## 260 0.6667 nan 0.3000 -0.0040
## 280 0.6575 nan 0.3000 -0.0026
## 300 0.6502 nan 0.3000 -0.0027
## 320 0.6412 nan 0.3000 -0.0021
## 340 0.6383 nan 0.3000 -0.0046
## 360 0.6315 nan 0.3000 -0.0001
## 380 0.6227 nan 0.3000 -0.0031
## 400 0.6164 nan 0.3000 -0.0011
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## 440 0.6081 nan 0.3000 -0.0045
## 460 0.6047 nan 0.3000 -0.0042
## 480 0.6004 nan 0.3000 -0.0048
## 500 0.5945 nan 0.3000 -0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1759 nan 0.3000 0.0530
## 2 1.0943 nan 0.3000 0.0339
## 3 1.0534 nan 0.3000 0.0098
## 4 1.0194 nan 0.3000 0.0078
## 5 0.9777 nan 0.3000 0.0163
## 6 0.9523 nan 0.3000 0.0066
## 7 0.9385 nan 0.3000 0.0034
## 8 0.9267 nan 0.3000 0.0008
## 9 0.9078 nan 0.3000 0.0029
## 10 0.8958 nan 0.3000 0.0027
## 20 0.8199 nan 0.3000 -0.0042
## 40 0.7343 nan 0.3000 -0.0010
## 60 0.6795 nan 0.3000 -0.0001
## 80 0.6244 nan 0.3000 -0.0041
## 100 0.5887 nan 0.3000 -0.0058
## 120 0.5448 nan 0.3000 -0.0057
## 140 0.5023 nan 0.3000 -0.0034
## 160 0.4718 nan 0.3000 -0.0034
## 180 0.4428 nan 0.3000 -0.0017
## 200 0.4194 nan 0.3000 -0.0022
## 220 0.3879 nan 0.3000 -0.0005
## 240 0.3628 nan 0.3000 -0.0033
## 260 0.3452 nan 0.3000 -0.0039
## 280 0.3185 nan 0.3000 -0.0011
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## 320 0.2849 nan 0.3000 -0.0039
## 340 0.2674 nan 0.3000 -0.0028
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## 380 0.2434 nan 0.3000 -0.0004
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## 460 0.1989 nan 0.3000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1775 nan 0.3000 0.0536
## 2 1.1009 nan 0.3000 0.0372
## 3 1.0549 nan 0.3000 0.0159
## 4 1.0150 nan 0.3000 0.0123
## 5 0.9894 nan 0.3000 0.0080
## 6 0.9598 nan 0.3000 0.0090
## 7 0.9367 nan 0.3000 0.0040
## 8 0.9206 nan 0.3000 0.0035
## 9 0.9075 nan 0.3000 -0.0012
## 10 0.8974 nan 0.3000 -0.0034
## 20 0.8253 nan 0.3000 0.0012
## 40 0.7497 nan 0.3000 -0.0062
## 60 0.6848 nan 0.3000 -0.0028
## 80 0.6363 nan 0.3000 -0.0055
## 100 0.5923 nan 0.3000 -0.0062
## 120 0.5514 nan 0.3000 -0.0018
## 140 0.5141 nan 0.3000 -0.0061
## 160 0.4807 nan 0.3000 -0.0019
## 180 0.4437 nan 0.3000 -0.0040
## 200 0.4250 nan 0.3000 -0.0056
## 220 0.4016 nan 0.3000 -0.0017
## 240 0.3763 nan 0.3000 -0.0023
## 260 0.3496 nan 0.3000 -0.0027
## 280 0.3335 nan 0.3000 -0.0024
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## 320 0.2942 nan 0.3000 -0.0023
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1670 nan 0.3000 0.0544
## 2 1.0990 nan 0.3000 0.0307
## 3 1.0573 nan 0.3000 0.0118
## 4 1.0136 nan 0.3000 0.0182
## 5 0.9827 nan 0.3000 0.0079
## 6 0.9595 nan 0.3000 0.0074
## 7 0.9381 nan 0.3000 0.0058
## 8 0.9244 nan 0.3000 0.0037
## 9 0.9061 nan 0.3000 0.0006
## 10 0.8895 nan 0.3000 0.0040
## 20 0.8315 nan 0.3000 -0.0043
## 40 0.7595 nan 0.3000 -0.0019
## 60 0.7029 nan 0.3000 0.0004
## 80 0.6565 nan 0.3000 -0.0034
## 100 0.6094 nan 0.3000 -0.0015
## 120 0.5688 nan 0.3000 -0.0003
## 140 0.5297 nan 0.3000 -0.0037
## 160 0.4998 nan 0.3000 -0.0021
## 180 0.4655 nan 0.3000 -0.0026
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## 220 0.4086 nan 0.3000 -0.0015
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## 280 0.3395 nan 0.3000 -0.0031
## 300 0.3215 nan 0.3000 -0.0025
## 320 0.3067 nan 0.3000 -0.0007
## 340 0.2897 nan 0.3000 -0.0033
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## 380 0.2663 nan 0.3000 -0.0016
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## 480 0.2033 nan 0.3000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1474 nan 0.3000 0.0713
## 2 1.0615 nan 0.3000 0.0287
## 3 1.0159 nan 0.3000 0.0151
## 4 0.9696 nan 0.3000 0.0177
## 5 0.9441 nan 0.3000 0.0057
## 6 0.9175 nan 0.3000 0.0034
## 7 0.8965 nan 0.3000 0.0003
## 8 0.8816 nan 0.3000 -0.0012
## 9 0.8581 nan 0.3000 0.0061
## 10 0.8483 nan 0.3000 -0.0070
## 20 0.7645 nan 0.3000 -0.0016
## 40 0.6678 nan 0.3000 -0.0013
## 60 0.5858 nan 0.3000 -0.0008
## 80 0.5140 nan 0.3000 -0.0093
## 100 0.4557 nan 0.3000 -0.0035
## 120 0.4023 nan 0.3000 -0.0017
## 140 0.3638 nan 0.3000 -0.0026
## 160 0.3209 nan 0.3000 -0.0026
## 180 0.2902 nan 0.3000 -0.0005
## 200 0.2574 nan 0.3000 -0.0014
## 220 0.2342 nan 0.3000 -0.0027
## 240 0.2136 nan 0.3000 -0.0015
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## 320 0.1460 nan 0.3000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1470 nan 0.3000 0.0553
## 2 1.0744 nan 0.3000 0.0248
## 3 1.0221 nan 0.3000 0.0181
## 4 0.9783 nan 0.3000 0.0155
## 5 0.9497 nan 0.3000 0.0036
## 6 0.9197 nan 0.3000 0.0066
## 7 0.9001 nan 0.3000 0.0043
## 8 0.8859 nan 0.3000 0.0005
## 9 0.8712 nan 0.3000 -0.0018
## 10 0.8561 nan 0.3000 -0.0017
## 20 0.7803 nan 0.3000 -0.0100
## 40 0.6606 nan 0.3000 -0.0008
## 60 0.5826 nan 0.3000 -0.0039
## 80 0.4961 nan 0.3000 -0.0021
## 100 0.4432 nan 0.3000 -0.0037
## 120 0.3918 nan 0.3000 -0.0006
## 140 0.3535 nan 0.3000 -0.0041
## 160 0.3076 nan 0.3000 -0.0021
## 180 0.2805 nan 0.3000 -0.0019
## 200 0.2543 nan 0.3000 -0.0009
## 220 0.2333 nan 0.3000 -0.0017
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1644 nan 0.3000 0.0558
## 2 1.0740 nan 0.3000 0.0398
## 3 1.0216 nan 0.3000 0.0157
## 4 0.9810 nan 0.3000 0.0044
## 5 0.9519 nan 0.3000 0.0063
## 6 0.9374 nan 0.3000 -0.0057
## 7 0.9085 nan 0.3000 0.0002
## 8 0.8842 nan 0.3000 0.0011
## 9 0.8770 nan 0.3000 -0.0045
## 10 0.8655 nan 0.3000 -0.0039
## 20 0.7731 nan 0.3000 -0.0032
## 40 0.6689 nan 0.3000 -0.0017
## 60 0.5899 nan 0.3000 -0.0024
## 80 0.5211 nan 0.3000 -0.0015
## 100 0.4504 nan 0.3000 -0.0015
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## 140 0.3494 nan 0.3000 -0.0061
## 160 0.3175 nan 0.3000 -0.0041
## 180 0.2785 nan 0.3000 -0.0017
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## 240 0.1986 nan 0.3000 -0.0016
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## 280 0.1625 nan 0.3000 -0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1595 nan 0.5000 0.0654
## 2 1.1059 nan 0.5000 0.0221
## 3 1.0640 nan 0.5000 0.0129
## 4 1.0158 nan 0.5000 0.0096
## 5 0.9849 nan 0.5000 0.0129
## 6 0.9552 nan 0.5000 0.0107
## 7 0.9409 nan 0.5000 0.0005
## 8 0.9206 nan 0.5000 -0.0006
## 9 0.9132 nan 0.5000 -0.0039
## 10 0.8988 nan 0.5000 0.0045
## 20 0.8409 nan 0.5000 -0.0017
## 40 0.7962 nan 0.5000 -0.0044
## 60 0.7632 nan 0.5000 -0.0075
## 80 0.7514 nan 0.5000 -0.0104
## 100 0.7399 nan 0.5000 -0.0062
## 120 0.7062 nan 0.5000 -0.0061
## 140 0.6943 nan 0.5000 -0.0104
## 160 0.6781 nan 0.5000 -0.0015
## 180 0.6594 nan 0.5000 -0.0040
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## 220 0.6425 nan 0.5000 -0.0032
## 240 0.6303 nan 0.5000 -0.0056
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## 340 0.5820 nan 0.5000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1784 nan 0.5000 0.0512
## 2 1.1041 nan 0.5000 0.0341
## 3 1.0487 nan 0.5000 0.0247
## 4 0.9977 nan 0.5000 0.0129
## 5 0.9754 nan 0.5000 0.0032
## 6 0.9540 nan 0.5000 0.0022
## 7 0.9410 nan 0.5000 -0.0001
## 8 0.9246 nan 0.5000 0.0080
## 9 0.9202 nan 0.5000 -0.0060
## 10 0.9136 nan 0.5000 0.0005
## 20 0.8588 nan 0.5000 -0.0079
## 40 0.8055 nan 0.5000 -0.0020
## 60 0.7784 nan 0.5000 -0.0003
## 80 0.7503 nan 0.5000 -0.0063
## 100 0.7391 nan 0.5000 -0.0058
## 120 0.7200 nan 0.5000 -0.0055
## 140 0.7050 nan 0.5000 -0.0045
## 160 0.6916 nan 0.5000 0.0023
## 180 0.6782 nan 0.5000 -0.0035
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## 280 0.6131 nan 0.5000 -0.0056
## 300 0.6034 nan 0.5000 -0.0063
## 320 inf nan 0.5000 nan
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## 380 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1504 nan 0.5000 0.0627
## 2 1.1000 nan 0.5000 0.0239
## 3 1.0452 nan 0.5000 0.0237
## 4 1.0056 nan 0.5000 0.0178
## 5 0.9854 nan 0.5000 0.0058
## 6 0.9685 nan 0.5000 0.0026
## 7 0.9437 nan 0.5000 0.0052
## 8 0.9184 nan 0.5000 0.0007
## 9 0.9119 nan 0.5000 -0.0023
## 10 0.8992 nan 0.5000 -0.0002
## 20 0.8496 nan 0.5000 -0.0035
## 40 0.8067 nan 0.5000 -0.0037
## 60 0.7811 nan 0.5000 -0.0042
## 80 0.7629 nan 0.5000 -0.0045
## 100 0.7383 nan 0.5000 -0.0054
## 120 0.7171 nan 0.5000 -0.0030
## 140 0.7091 nan 0.5000 -0.0037
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## 220 0.6508 nan 0.5000 -0.0053
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## 260 0.6498 nan 0.5000 -0.0028
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## 440 0.5555 nan 0.5000 -0.0037
## 460 0.5554 nan 0.5000 -0.0091
## 480 0.5451 nan 0.5000 -0.0034
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1349 nan 0.5000 0.0563
## 2 1.0497 nan 0.5000 0.0275
## 3 1.0033 nan 0.5000 -0.0021
## 4 0.9741 nan 0.5000 0.0062
## 5 0.9455 nan 0.5000 0.0039
## 6 0.9250 nan 0.5000 -0.0067
## 7 0.8982 nan 0.5000 0.0045
## 8 0.8928 nan 0.5000 -0.0120
## 9 0.8873 nan 0.5000 -0.0128
## 10 0.8812 nan 0.5000 -0.0118
## 20 0.7868 nan 0.5000 -0.0117
## 40 0.6830 nan 0.5000 -0.0061
## 60 0.8574 nan 0.5000 -0.0080
## 80 0.8233 nan 0.5000 -0.0034
## 100 0.7827 nan 0.5000 -0.0061
## 120 0.7177 nan 0.5000 -0.0042
## 140 0.6825 nan 0.5000 -0.0086
## 160 0.6643 nan 0.5000 -0.0033
## 180 0.6401 nan 0.5000 -0.0000
## 200 0.6071 nan 0.5000 -0.0059
## 220 0.5797 nan 0.5000 -0.0038
## 240 0.5707 nan 0.5000 -0.0018
## 260 0.5478 nan 0.5000 -0.0021
## 280 0.5638 nan 0.5000 0.0007
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 -0.0014
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 0.0000
## 420 inf nan 0.5000 -0.0044
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 -0.0031
## 500 inf nan 0.5000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1282 nan 0.5000 0.0723
## 2 1.0738 nan 0.5000 0.0071
## 3 0.9964 nan 0.5000 0.0201
## 4 0.9672 nan 0.5000 0.0007
## 5 0.9441 nan 0.5000 0.0002
## 6 0.9235 nan 0.5000 -0.0028
## 7 0.8942 nan 0.5000 0.0044
## 8 0.8857 nan 0.5000 -0.0117
## 9 0.8730 nan 0.5000 -0.0013
## 10 0.8620 nan 0.5000 -0.0028
## 20 0.7830 nan 0.5000 -0.0021
## 40 0.6981 nan 0.5000 -0.0032
## 60 0.6519 nan 0.5000 -0.0111
## 80 0.5733 nan 0.5000 -0.0093
## 100 0.5139 nan 0.5000 -0.0218
## 120 0.4602 nan 0.5000 -0.0045
## 140 0.4257 nan 0.5000 -0.0099
## 160 0.4081 nan 0.5000 -0.0067
## 180 0.3650 nan 0.5000 -0.0033
## 200 0.3461 nan 0.5000 -0.0049
## 220 0.3153 nan 0.5000 -0.0025
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1156 nan 0.5000 0.0744
## 2 1.0355 nan 0.5000 0.0271
## 3 0.9806 nan 0.5000 0.0132
## 4 0.9548 nan 0.5000 0.0017
## 5 0.9246 nan 0.5000 0.0005
## 6 0.8993 nan 0.5000 -0.0068
## 7 0.8850 nan 0.5000 -0.0140
## 8 0.8726 nan 0.5000 -0.0061
## 9 0.8631 nan 0.5000 -0.0084
## 10 0.8480 nan 0.5000 0.0015
## 20 0.7711 nan 0.5000 -0.0056
## 40 0.7061 nan 0.5000 -0.0031
## 60 0.6504 nan 0.5000 -0.0121
## 80 0.5640 nan 0.5000 -0.0060
## 100 0.5115 nan 0.5000 -0.0016
## 120 0.4813 nan 0.5000 -0.0012
## 140 0.4381 nan 0.5000 -0.0038
## 160 0.4033 nan 0.5000 -0.0081
## 180 0.3569 nan 0.5000 -0.0064
## 200 0.3136 nan 0.5000 -0.0059
## 220 0.2774 nan 0.5000 -0.0013
## 240 0.2514 nan 0.5000 -0.0026
## 260 0.2279 nan 0.5000 -0.0026
## 280 0.2107 nan 0.5000 -0.0011
## 300 0.1979 nan 0.5000 -0.0052
## 320 0.1812 nan 0.5000 -0.0016
## 340 0.1619 nan 0.5000 -0.0002
## 360 0.1538 nan 0.5000 -0.0018
## 380 0.1414 nan 0.5000 -0.0011
## 400 0.1311 nan 0.5000 -0.0023
## 420 0.1203 nan 0.5000 -0.0002
## 440 0.1119 nan 0.5000 -0.0002
## 460 0.1055 nan 0.5000 -0.0021
## 480 0.0976 nan 0.5000 -0.0014
## 500 0.0906 nan 0.5000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1050 nan 0.5000 0.0810
## 2 1.0201 nan 0.5000 0.0235
## 3 0.9660 nan 0.5000 0.0098
## 4 0.9488 nan 0.5000 -0.0099
## 5 0.9241 nan 0.5000 -0.0102
## 6 0.8928 nan 0.5000 0.0005
## 7 0.8831 nan 0.5000 -0.0091
## 8 0.8781 nan 0.5000 -0.0179
## 9 0.8619 nan 0.5000 -0.0071
## 10 0.8503 nan 0.5000 -0.0063
## 20 0.7652 nan 0.5000 -0.0347
## 40 0.6160 nan 0.5000 -0.0068
## 60 0.5264 nan 0.5000 -0.0204
## 80 0.5828 nan 0.5000 -0.0002
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0931 nan 0.5000 0.0840
## 2 1.0154 nan 0.5000 0.0267
## 3 0.9564 nan 0.5000 0.0121
## 4 0.9149 nan 0.5000 0.0031
## 5 0.8810 nan 0.5000 -0.0038
## 6 0.8630 nan 0.5000 -0.0062
## 7 0.8431 nan 0.5000 -0.0036
## 8 0.8314 nan 0.5000 -0.0132
## 9 0.8116 nan 0.5000 -0.0013
## 10 0.7988 nan 0.5000 -0.0083
## 20 0.7208 nan 0.5000 -0.0139
## 40 0.5989 nan 0.5000 -0.0145
## 60 0.4847 nan 0.5000 -0.0026
## 80 0.4327 nan 0.5000 -0.0159
## 100 0.3763 nan 0.5000 -0.0033
## 120 0.3079 nan 0.5000 -0.0037
## 140 0.2573 nan 0.5000 -0.0008
## 160 0.2077 nan 0.5000 -0.0018
## 180 0.1710 nan 0.5000 -0.0032
## 200 0.1438 nan 0.5000 -0.0008
## 220 0.1254 nan 0.5000 -0.0009
## 240 0.1070 nan 0.5000 -0.0004
## 260 0.0939 nan 0.5000 -0.0013
## 280 0.0815 nan 0.5000 -0.0011
## 300 0.0689 nan 0.5000 -0.0009
## 320 0.0627 nan 0.5000 -0.0004
## 340 0.0546 nan 0.5000 -0.0009
## 360 0.0500 nan 0.5000 -0.0003
## 380 0.0445 nan 0.5000 -0.0002
## 400 0.0393 nan 0.5000 -0.0003
## 420 0.0347 nan 0.5000 -0.0002
## 440 0.0310 nan 0.5000 -0.0003
## 460 0.0276 nan 0.5000 -0.0001
## 480 0.0245 nan 0.5000 -0.0001
## 500 0.0223 nan 0.5000 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0932 nan 0.5000 0.0780
## 2 1.0120 nan 0.5000 0.0249
## 3 0.9812 nan 0.5000 -0.0073
## 4 0.9307 nan 0.5000 0.0068
## 5 0.9090 nan 0.5000 0.0000
## 6 0.8713 nan 0.5000 0.0079
## 7 0.8532 nan 0.5000 -0.0045
## 8 0.8321 nan 0.5000 0.0040
## 9 0.8277 nan 0.5000 -0.0108
## 10 0.8230 nan 0.5000 -0.0127
## 20 0.7599 nan 0.5000 -0.0186
## 40 0.6000 nan 0.5000 -0.0095
## 60 0.4873 nan 0.5000 -0.0010
## 80 0.4147 nan 0.5000 -0.0075
## 100 0.3523 nan 0.5000 -0.0062
## 120 0.2874 nan 0.5000 -0.0044
## 140 0.2574 nan 0.5000 -0.0010
## 160 0.2177 nan 0.5000 -0.0023
## 180 0.1802 nan 0.5000 -0.0015
## 200 0.1516 nan 0.5000 -0.0017
## 220 0.1314 nan 0.5000 -0.0023
## 240 0.1157 nan 0.5000 -0.0021
## 260 0.1006 nan 0.5000 -0.0017
## 280 0.0859 nan 0.5000 -0.0003
## 300 0.0778 nan 0.5000 -0.0021
## 320 0.0685 nan 0.5000 -0.0023
## 340 0.0605 nan 0.5000 -0.0013
## 360 0.0540 nan 0.5000 -0.0008
## 380 0.0463 nan 0.5000 -0.0005
## 400 0.0412 nan 0.5000 -0.0003
## 420 0.0366 nan 0.5000 -0.0005
## 440 0.0331 nan 0.5000 -0.0003
## 460 0.0295 nan 0.5000 -0.0003
## 480 0.0258 nan 0.5000 -0.0004
## 500 0.0236 nan 0.5000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1287 nan 1.0000 0.0494
## 2 1.0668 nan 1.0000 0.0179
## 3 1.0106 nan 1.0000 0.0143
## 4 1.0023 nan 1.0000 -0.0058
## 5 0.9831 nan 1.0000 0.0061
## 6 0.9859 nan 1.0000 -0.0281
## 7 0.9819 nan 1.0000 -0.0051
## 8 0.9921 nan 1.0000 -0.0256
## 9 1.0257 nan 1.0000 -0.0590
## 10 0.9614 nan 1.0000 0.0343
## 20 0.8856 nan 1.0000 0.0449
## 40 1094509.5811 nan 1.0000 -0.0009
## 60 1094509.5383 nan 1.0000 0.0008
## 80 1094509.5348 nan 1.0000 0.0004
## 100 1094509.5390 nan 1.0000 0.0008
## 120 1094509.7372 nan 1.0000 -0.0036
## 140 16076000.7384 nan 1.0000 -0.0009
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1333 nan 1.0000 0.0391
## 2 1.0601 nan 1.0000 -0.0007
## 3 1.0413 nan 1.0000 -0.0195
## 4 0.9997 nan 1.0000 0.0239
## 5 0.9970 nan 1.0000 -0.0227
## 6 0.9715 nan 1.0000 0.0081
## 7 0.9668 nan 1.0000 -0.0111
## 8 0.9512 nan 1.0000 0.0007
## 9 0.9634 nan 1.0000 -0.0342
## 10 1.0059 nan 1.0000 -0.0635
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1380 nan 1.0000 0.0792
## 2 1.0665 nan 1.0000 0.0196
## 3 1.0049 nan 1.0000 0.0194
## 4 0.9749 nan 1.0000 0.0083
## 5 0.9889 nan 1.0000 -0.0384
## 6 0.9761 nan 1.0000 -0.0079
## 7 0.9758 nan 1.0000 -0.0330
## 8 0.9578 nan 1.0000 -0.0028
## 9 0.9828 nan 1.0000 -0.0395
## 10 0.9456 nan 1.0000 0.0186
## 20 1.0953 nan 1.0000 -0.1321
## 40 2218295.2123 nan 1.0000 -0.0123
## 60 2218295.1634 nan 1.0000 -0.0086
## 80 inf nan 1.0000 -inf
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0715 nan 1.0000 0.0962
## 2 0.9911 nan 1.0000 0.0021
## 3 0.9845 nan 1.0000 -0.0333
## 4 0.9260 nan 1.0000 0.0188
## 5 0.9444 nan 1.0000 -0.0453
## 6 0.8899 nan 1.0000 0.0128
## 7 0.8801 nan 1.0000 -0.0126
## 8 0.8672 nan 1.0000 -0.0067
## 9 0.8792 nan 1.0000 -0.0342
## 10 0.8701 nan 1.0000 -0.0140
## 20 0.8512 nan 1.0000 -0.0423
## 40 1.6950 nan 1.0000 -0.0187
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0667 nan 1.0000 0.0654
## 2 0.9865 nan 1.0000 0.0263
## 3 0.9662 nan 1.0000 -0.0150
## 4 0.9433 nan 1.0000 -0.0215
## 5 0.9423 nan 1.0000 -0.0432
## 6 0.9556 nan 1.0000 -0.0306
## 7 0.9289 nan 1.0000 0.0006
## 8 0.9398 nan 1.0000 -0.0417
## 9 0.9303 nan 1.0000 -0.0371
## 10 51.5332 nan 1.0000 -25.3041
## 20 811941.2650 nan 1.0000 -0.0540
## 40 811941.1825 nan 1.0000 -0.0030
## 60 811941.2057 nan 1.0000 -0.0360
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0643 nan 1.0000 0.1110
## 2 0.9831 nan 1.0000 0.0256
## 3 0.9665 nan 1.0000 -0.0139
## 4 0.9402 nan 1.0000 0.0046
## 5 0.9319 nan 1.0000 -0.0135
## 6 0.9292 nan 1.0000 -0.0218
## 7 0.9318 nan 1.0000 -0.0291
## 8 0.8942 nan 1.0000 0.0071
## 9 0.8801 nan 1.0000 -0.0045
## 10 0.8617 nan 1.0000 -0.0239
## 20 0.9150 nan 1.0000 -0.0245
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0588 nan 1.0000 0.0532
## 2 0.9933 nan 1.0000 0.0052
## 3 0.9738 nan 1.0000 -0.0165
## 4 0.9180 nan 1.0000 0.0009
## 5 0.8976 nan 1.0000 -0.0213
## 6 0.8912 nan 1.0000 -0.0188
## 7 0.8701 nan 1.0000 -0.0164
## 8 0.8799 nan 1.0000 -0.0364
## 9 0.8689 nan 1.0000 -0.0252
## 10 0.8866 nan 1.0000 -0.0462
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1219 nan 1.0000 -0.0318
## 2 1.0535 nan 1.0000 0.0042
## 3 0.9900 nan 1.0000 0.0031
## 4 0.9788 nan 1.0000 -0.0237
## 5 1.0803 nan 1.0000 -0.1086
## 6 1.1023 nan 1.0000 -0.0708
## 7 1.1784 nan 1.0000 -0.1156
## 8 1.1877 nan 1.0000 -0.0603
## 9 1.2133 nan 1.0000 -0.0890
## 10 1.3639 nan 1.0000 -0.2459
## 20 1.4180 nan 1.0000 -0.0525
## 40 7914568.2503 nan 1.0000 0.0013
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0626 nan 1.0000 0.0713
## 2 0.9704 nan 1.0000 0.0216
## 3 0.9749 nan 1.0000 -0.0424
## 4 0.9081 nan 1.0000 0.0139
## 5 0.8993 nan 1.0000 -0.0187
## 6 0.9980 nan 1.0000 -0.1545
## 7 0.9970 nan 1.0000 -0.0330
## 8 3.3058 nan 1.0000 -2.3891
## 9 3.3085 nan 1.0000 -0.0402
## 10 3.3481 nan 1.0000 -0.0904
## 20 5.0314 nan 1.0000 -0.3085
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
## 2 1.2945 nan 0.0000 0.0000
## 3 1.2945 nan 0.0000 0.0000
## 4 1.2945 nan 0.0000 0.0000
## 5 1.2945 nan 0.0000 0.0000
## 6 1.2945 nan 0.0000 0.0000
## 7 1.2945 nan 0.0000 0.0000
## 8 1.2945 nan 0.0000 0.0000
## 9 1.2945 nan 0.0000 0.0000
## 10 1.2945 nan 0.0000 0.0000
## 20 1.2945 nan 0.0000 0.0000
## 40 1.2945 nan 0.0000 0.0000
## 60 1.2945 nan 0.0000 0.0000
## 80 1.2945 nan 0.0000 0.0000
## 100 1.2945 nan 0.0000 0.0000
## 120 1.2945 nan 0.0000 0.0000
## 140 1.2945 nan 0.0000 0.0000
## 160 1.2945 nan 0.0000 0.0000
## 180 1.2945 nan 0.0000 0.0000
## 200 1.2945 nan 0.0000 0.0000
## 220 1.2945 nan 0.0000 0.0000
## 240 1.2945 nan 0.0000 0.0000
## 260 1.2945 nan 0.0000 0.0000
## 280 1.2945 nan 0.0000 0.0000
## 300 1.2945 nan 0.0000 0.0000
## 320 1.2945 nan 0.0000 0.0000
## 340 1.2945 nan 0.0000 0.0000
## 360 1.2945 nan 0.0000 0.0000
## 380 1.2945 nan 0.0000 0.0000
## 400 1.2945 nan 0.0000 0.0000
## 420 1.2945 nan 0.0000 0.0000
## 440 1.2945 nan 0.0000 0.0000
## 460 1.2945 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
## 2 1.2945 nan 0.0000 0.0000
## 3 1.2945 nan 0.0000 0.0000
## 4 1.2945 nan 0.0000 0.0000
## 5 1.2945 nan 0.0000 0.0000
## 6 1.2945 nan 0.0000 0.0000
## 7 1.2945 nan 0.0000 0.0000
## 8 1.2945 nan 0.0000 0.0000
## 9 1.2945 nan 0.0000 0.0000
## 10 1.2945 nan 0.0000 0.0000
## 20 1.2945 nan 0.0000 0.0000
## 40 1.2945 nan 0.0000 0.0000
## 60 1.2945 nan 0.0000 0.0000
## 80 1.2945 nan 0.0000 0.0000
## 100 1.2945 nan 0.0000 0.0000
## 120 1.2945 nan 0.0000 0.0000
## 140 1.2945 nan 0.0000 0.0000
## 160 1.2945 nan 0.0000 0.0000
## 180 1.2945 nan 0.0000 0.0000
## 200 1.2945 nan 0.0000 0.0000
## 220 1.2945 nan 0.0000 0.0000
## 240 1.2945 nan 0.0000 0.0000
## 260 1.2945 nan 0.0000 0.0000
## 280 1.2945 nan 0.0000 0.0000
## 300 1.2945 nan 0.0000 0.0000
## 320 1.2945 nan 0.0000 0.0000
## 340 1.2945 nan 0.0000 0.0000
## 360 1.2945 nan 0.0000 0.0000
## 380 1.2945 nan 0.0000 0.0000
## 400 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2942 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2939 nan 0.0010 0.0002
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## 500 1.0998 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0003
## 2 1.2935 nan 0.0010 0.0003
## 3 1.2930 nan 0.0010 0.0002
## 4 1.2924 nan 0.0010 0.0002
## 5 1.2919 nan 0.0010 0.0002
## 6 1.2914 nan 0.0010 0.0003
## 7 1.2908 nan 0.0010 0.0003
## 8 1.2903 nan 0.0010 0.0002
## 9 1.2897 nan 0.0010 0.0002
## 10 1.2892 nan 0.0010 0.0002
## 20 1.2837 nan 0.0010 0.0003
## 40 1.2729 nan 0.0010 0.0002
## 60 1.2627 nan 0.0010 0.0002
## 80 1.2527 nan 0.0010 0.0002
## 100 1.2432 nan 0.0010 0.0002
## 120 1.2338 nan 0.0010 0.0002
## 140 1.2249 nan 0.0010 0.0001
## 160 1.2163 nan 0.0010 0.0002
## 180 1.2079 nan 0.0010 0.0002
## 200 1.1995 nan 0.0010 0.0002
## 220 1.1918 nan 0.0010 0.0002
## 240 1.1840 nan 0.0010 0.0002
## 260 1.1764 nan 0.0010 0.0001
## 280 1.1691 nan 0.0010 0.0001
## 300 1.1619 nan 0.0010 0.0002
## 320 1.1550 nan 0.0010 0.0002
## 340 1.1482 nan 0.0010 0.0002
## 360 1.1415 nan 0.0010 0.0002
## 380 1.1352 nan 0.0010 0.0001
## 400 1.1289 nan 0.0010 0.0001
## 420 1.1227 nan 0.0010 0.0001
## 440 1.1167 nan 0.0010 0.0001
## 460 1.1109 nan 0.0010 0.0001
## 480 1.1054 nan 0.0010 0.0001
## 500 1.1000 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2603 nan 0.1000 0.0151
## 2 1.2299 nan 0.1000 0.0132
## 3 1.2058 nan 0.1000 0.0107
## 4 1.1846 nan 0.1000 0.0082
## 5 1.1660 nan 0.1000 0.0064
## 6 1.1480 nan 0.1000 0.0066
## 7 1.1328 nan 0.1000 0.0063
## 8 1.1160 nan 0.1000 0.0052
## 9 1.1025 nan 0.1000 0.0056
## 10 1.0922 nan 0.1000 0.0042
## 20 1.0031 nan 0.1000 0.0032
## 40 0.9187 nan 0.1000 0.0017
## 60 0.8847 nan 0.1000 0.0001
## 80 0.8610 nan 0.1000 -0.0008
## 100 0.8423 nan 0.1000 -0.0011
## 120 0.8273 nan 0.1000 -0.0004
## 140 0.8166 nan 0.1000 -0.0003
## 160 0.8049 nan 0.1000 -0.0000
## 180 0.7975 nan 0.1000 -0.0012
## 200 0.7921 nan 0.1000 -0.0008
## 220 0.7840 nan 0.1000 -0.0010
## 240 0.7760 nan 0.1000 -0.0005
## 260 0.7687 nan 0.1000 -0.0011
## 280 0.7607 nan 0.1000 -0.0012
## 300 0.7536 nan 0.1000 -0.0003
## 320 0.7468 nan 0.1000 -0.0014
## 340 0.7405 nan 0.1000 -0.0007
## 360 0.7347 nan 0.1000 -0.0005
## 380 0.7280 nan 0.1000 -0.0004
## 400 0.7261 nan 0.1000 -0.0002
## 420 0.7224 nan 0.1000 -0.0006
## 440 0.7188 nan 0.1000 -0.0012
## 460 0.7143 nan 0.1000 -0.0016
## 480 0.7090 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2612 nan 0.1000 0.0166
## 2 1.2355 nan 0.1000 0.0138
## 3 1.2077 nan 0.1000 0.0113
## 4 1.1878 nan 0.1000 0.0085
## 5 1.1717 nan 0.1000 0.0058
## 6 1.1565 nan 0.1000 0.0076
## 7 1.1384 nan 0.1000 0.0072
## 8 1.1221 nan 0.1000 0.0066
## 9 1.1092 nan 0.1000 0.0049
## 10 1.0964 nan 0.1000 0.0043
## 20 1.0037 nan 0.1000 0.0014
## 40 0.9268 nan 0.1000 0.0018
## 60 0.8839 nan 0.1000 -0.0009
## 80 0.8596 nan 0.1000 -0.0004
## 100 0.8404 nan 0.1000 -0.0015
## 120 0.8289 nan 0.1000 0.0000
## 140 0.8178 nan 0.1000 -0.0014
## 160 0.8057 nan 0.1000 -0.0003
## 180 0.7983 nan 0.1000 -0.0015
## 200 0.7902 nan 0.1000 -0.0018
## 220 0.7829 nan 0.1000 -0.0009
## 240 0.7752 nan 0.1000 -0.0005
## 260 0.7687 nan 0.1000 -0.0011
## 280 0.7622 nan 0.1000 -0.0012
## 300 0.7549 nan 0.1000 -0.0013
## 320 0.7471 nan 0.1000 -0.0013
## 340 0.7392 nan 0.1000 -0.0004
## 360 0.7342 nan 0.1000 -0.0018
## 380 0.7279 nan 0.1000 -0.0018
## 400 0.7235 nan 0.1000 -0.0004
## 420 0.7168 nan 0.1000 -0.0005
## 440 0.7097 nan 0.1000 -0.0007
## 460 0.7055 nan 0.1000 -0.0011
## 480 0.7028 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2592 nan 0.1000 0.0171
## 2 1.2324 nan 0.1000 0.0132
## 3 1.2086 nan 0.1000 0.0105
## 4 1.1892 nan 0.1000 0.0046
## 5 1.1667 nan 0.1000 0.0107
## 6 1.1488 nan 0.1000 0.0074
## 7 1.1343 nan 0.1000 0.0059
## 8 1.1196 nan 0.1000 0.0049
## 9 1.1079 nan 0.1000 0.0052
## 10 1.0942 nan 0.1000 0.0034
## 20 1.0021 nan 0.1000 0.0001
## 40 0.9234 nan 0.1000 0.0006
## 60 0.8846 nan 0.1000 0.0002
## 80 0.8591 nan 0.1000 -0.0014
## 100 0.8397 nan 0.1000 -0.0007
## 120 0.8297 nan 0.1000 -0.0013
## 140 0.8165 nan 0.1000 -0.0010
## 160 0.8076 nan 0.1000 -0.0005
## 180 0.7968 nan 0.1000 -0.0005
## 200 0.7884 nan 0.1000 -0.0010
## 220 0.7797 nan 0.1000 -0.0008
## 240 0.7730 nan 0.1000 -0.0012
## 260 0.7671 nan 0.1000 -0.0007
## 280 0.7598 nan 0.1000 -0.0006
## 300 0.7521 nan 0.1000 -0.0012
## 320 0.7456 nan 0.1000 -0.0011
## 340 0.7411 nan 0.1000 -0.0013
## 360 0.7348 nan 0.1000 -0.0005
## 380 0.7287 nan 0.1000 -0.0008
## 400 0.7238 nan 0.1000 -0.0016
## 420 0.7166 nan 0.1000 -0.0002
## 440 0.7117 nan 0.1000 -0.0011
## 460 0.7092 nan 0.1000 -0.0004
## 480 0.7041 nan 0.1000 -0.0004
## 500 0.6970 nan 0.1000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2499 nan 0.1000 0.0221
## 2 1.2095 nan 0.1000 0.0179
## 3 1.1830 nan 0.1000 0.0111
## 4 1.1528 nan 0.1000 0.0136
## 5 1.1292 nan 0.1000 0.0110
## 6 1.1078 nan 0.1000 0.0085
## 7 1.0860 nan 0.1000 0.0092
## 8 1.0733 nan 0.1000 0.0051
## 9 1.0559 nan 0.1000 0.0047
## 10 1.0379 nan 0.1000 0.0082
## 20 0.9409 nan 0.1000 0.0011
## 40 0.8595 nan 0.1000 -0.0005
## 60 0.8104 nan 0.1000 -0.0024
## 80 0.7759 nan 0.1000 -0.0012
## 100 0.7471 nan 0.1000 0.0000
## 120 0.7244 nan 0.1000 -0.0008
## 140 0.7059 nan 0.1000 0.0000
## 160 0.6822 nan 0.1000 -0.0011
## 180 0.6636 nan 0.1000 -0.0009
## 200 0.6444 nan 0.1000 -0.0003
## 220 0.6251 nan 0.1000 -0.0013
## 240 0.6067 nan 0.1000 -0.0008
## 260 0.5895 nan 0.1000 -0.0014
## 280 0.5704 nan 0.1000 -0.0008
## 300 0.5547 nan 0.1000 -0.0000
## 320 0.5410 nan 0.1000 -0.0011
## 340 0.5262 nan 0.1000 -0.0022
## 360 0.5105 nan 0.1000 -0.0010
## 380 0.4978 nan 0.1000 -0.0016
## 400 0.4907 nan 0.1000 -0.0009
## 420 0.4809 nan 0.1000 -0.0014
## 440 0.4696 nan 0.1000 -0.0014
## 460 0.4580 nan 0.1000 -0.0003
## 480 0.4479 nan 0.1000 -0.0010
## 500 0.4385 nan 0.1000 -0.0024
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2482 nan 0.1000 0.0190
## 2 1.2137 nan 0.1000 0.0180
## 3 1.1783 nan 0.1000 0.0148
## 4 1.1497 nan 0.1000 0.0114
## 5 1.1213 nan 0.1000 0.0076
## 6 1.0985 nan 0.1000 0.0098
## 7 1.0782 nan 0.1000 0.0079
## 8 1.0619 nan 0.1000 0.0051
## 9 1.0484 nan 0.1000 0.0033
## 10 1.0311 nan 0.1000 0.0069
## 20 0.9401 nan 0.1000 0.0020
## 40 0.8604 nan 0.1000 -0.0009
## 60 0.8086 nan 0.1000 -0.0011
## 80 0.7747 nan 0.1000 -0.0012
## 100 0.7445 nan 0.1000 -0.0024
## 120 0.7219 nan 0.1000 0.0000
## 140 0.6984 nan 0.1000 -0.0019
## 160 0.6782 nan 0.1000 -0.0014
## 180 0.6601 nan 0.1000 -0.0011
## 200 0.6418 nan 0.1000 -0.0013
## 220 0.6264 nan 0.1000 -0.0019
## 240 0.6087 nan 0.1000 -0.0009
## 260 0.5925 nan 0.1000 -0.0009
## 280 0.5790 nan 0.1000 -0.0007
## 300 0.5679 nan 0.1000 -0.0003
## 320 0.5539 nan 0.1000 -0.0012
## 340 0.5377 nan 0.1000 -0.0009
## 360 0.5249 nan 0.1000 -0.0005
## 380 0.5100 nan 0.1000 -0.0012
## 400 0.4997 nan 0.1000 -0.0012
## 420 0.4897 nan 0.1000 -0.0011
## 440 0.4763 nan 0.1000 -0.0017
## 460 0.4627 nan 0.1000 -0.0009
## 480 0.4534 nan 0.1000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2474 nan 0.1000 0.0204
## 2 1.2068 nan 0.1000 0.0155
## 3 1.1718 nan 0.1000 0.0152
## 4 1.1451 nan 0.1000 0.0143
## 5 1.1202 nan 0.1000 0.0099
## 6 1.0979 nan 0.1000 0.0080
## 7 1.0799 nan 0.1000 0.0079
## 8 1.0606 nan 0.1000 0.0078
## 9 1.0438 nan 0.1000 0.0034
## 10 1.0271 nan 0.1000 0.0066
## 20 0.9344 nan 0.1000 0.0019
## 40 0.8555 nan 0.1000 -0.0011
## 60 0.8087 nan 0.1000 -0.0014
## 80 0.7762 nan 0.1000 -0.0010
## 100 0.7510 nan 0.1000 -0.0018
## 120 0.7297 nan 0.1000 -0.0020
## 140 0.7101 nan 0.1000 -0.0020
## 160 0.6885 nan 0.1000 -0.0023
## 180 0.6685 nan 0.1000 -0.0010
## 200 0.6497 nan 0.1000 -0.0008
## 220 0.6300 nan 0.1000 -0.0015
## 240 0.6130 nan 0.1000 -0.0007
## 260 0.5987 nan 0.1000 -0.0014
## 280 0.5841 nan 0.1000 -0.0012
## 300 0.5706 nan 0.1000 -0.0010
## 320 0.5595 nan 0.1000 -0.0007
## 340 0.5450 nan 0.1000 -0.0007
## 360 0.5334 nan 0.1000 -0.0011
## 380 0.5199 nan 0.1000 -0.0007
## 400 0.5082 nan 0.1000 -0.0012
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## 460 0.4723 nan 0.1000 -0.0007
## 480 0.4590 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2437 nan 0.1000 0.0233
## 2 1.2022 nan 0.1000 0.0171
## 3 1.1612 nan 0.1000 0.0148
## 4 1.1291 nan 0.1000 0.0127
## 5 1.1039 nan 0.1000 0.0061
## 6 1.0756 nan 0.1000 0.0105
## 7 1.0588 nan 0.1000 0.0054
## 8 1.0371 nan 0.1000 0.0080
## 9 1.0211 nan 0.1000 0.0050
## 10 1.0082 nan 0.1000 0.0052
## 20 0.9041 nan 0.1000 0.0006
## 40 0.8097 nan 0.1000 -0.0003
## 60 0.7529 nan 0.1000 -0.0017
## 80 0.7102 nan 0.1000 -0.0010
## 100 0.6763 nan 0.1000 -0.0018
## 120 0.6362 nan 0.1000 -0.0019
## 140 0.6029 nan 0.1000 -0.0009
## 160 0.5717 nan 0.1000 -0.0018
## 180 0.5466 nan 0.1000 -0.0000
## 200 0.5210 nan 0.1000 -0.0006
## 220 0.4974 nan 0.1000 -0.0022
## 240 0.4726 nan 0.1000 -0.0012
## 260 0.4529 nan 0.1000 -0.0001
## 280 0.4344 nan 0.1000 -0.0003
## 300 0.4197 nan 0.1000 -0.0010
## 320 0.4034 nan 0.1000 -0.0010
## 340 0.3857 nan 0.1000 -0.0005
## 360 0.3702 nan 0.1000 -0.0011
## 380 0.3557 nan 0.1000 -0.0000
## 400 0.3419 nan 0.1000 -0.0008
## 420 0.3279 nan 0.1000 -0.0005
## 440 0.3146 nan 0.1000 -0.0005
## 460 0.3021 nan 0.1000 -0.0014
## 480 0.2907 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2437 nan 0.1000 0.0237
## 2 1.1979 nan 0.1000 0.0172
## 3 1.1534 nan 0.1000 0.0190
## 4 1.1187 nan 0.1000 0.0147
## 5 1.0952 nan 0.1000 0.0091
## 6 1.0703 nan 0.1000 0.0085
## 7 1.0473 nan 0.1000 0.0090
## 8 1.0263 nan 0.1000 0.0088
## 9 1.0074 nan 0.1000 0.0056
## 10 0.9948 nan 0.1000 0.0036
## 20 0.8935 nan 0.1000 -0.0019
## 40 0.8005 nan 0.1000 -0.0013
## 60 0.7512 nan 0.1000 -0.0019
## 80 0.7116 nan 0.1000 -0.0007
## 100 0.6761 nan 0.1000 -0.0010
## 120 0.6400 nan 0.1000 -0.0016
## 140 0.6092 nan 0.1000 -0.0020
## 160 0.5808 nan 0.1000 -0.0009
## 180 0.5585 nan 0.1000 -0.0012
## 200 0.5367 nan 0.1000 -0.0018
## 220 0.5095 nan 0.1000 -0.0017
## 240 0.4858 nan 0.1000 -0.0009
## 260 0.4684 nan 0.1000 -0.0014
## 280 0.4498 nan 0.1000 -0.0016
## 300 0.4298 nan 0.1000 -0.0003
## 320 0.4132 nan 0.1000 -0.0004
## 340 0.3944 nan 0.1000 -0.0013
## 360 0.3792 nan 0.1000 -0.0008
## 380 0.3651 nan 0.1000 -0.0001
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## 460 0.3141 nan 0.1000 -0.0009
## 480 0.3026 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2359 nan 0.1000 0.0247
## 2 1.1973 nan 0.1000 0.0146
## 3 1.1614 nan 0.1000 0.0159
## 4 1.1285 nan 0.1000 0.0139
## 5 1.0977 nan 0.1000 0.0144
## 6 1.0726 nan 0.1000 0.0100
## 7 1.0527 nan 0.1000 0.0078
## 8 1.0352 nan 0.1000 0.0052
## 9 1.0134 nan 0.1000 0.0091
## 10 0.9943 nan 0.1000 0.0047
## 20 0.8969 nan 0.1000 0.0007
## 40 0.8081 nan 0.1000 -0.0007
## 60 0.7537 nan 0.1000 -0.0007
## 80 0.7178 nan 0.1000 -0.0028
## 100 0.6802 nan 0.1000 -0.0006
## 120 0.6459 nan 0.1000 -0.0030
## 140 0.6105 nan 0.1000 -0.0015
## 160 0.5795 nan 0.1000 -0.0014
## 180 0.5519 nan 0.1000 -0.0013
## 200 0.5269 nan 0.1000 -0.0004
## 220 0.5047 nan 0.1000 -0.0005
## 240 0.4875 nan 0.1000 -0.0012
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## 280 0.4429 nan 0.1000 -0.0012
## 300 0.4233 nan 0.1000 -0.0009
## 320 0.4079 nan 0.1000 -0.0011
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## 380 0.3620 nan 0.1000 -0.0006
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## 460 0.3054 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2291 nan 0.2000 0.0304
## 2 1.1919 nan 0.2000 0.0155
## 3 1.1563 nan 0.2000 0.0159
## 4 1.1235 nan 0.2000 0.0137
## 5 1.1010 nan 0.2000 0.0089
## 6 1.0765 nan 0.2000 0.0098
## 7 1.0631 nan 0.2000 0.0022
## 8 1.0431 nan 0.2000 0.0079
## 9 1.0226 nan 0.2000 0.0080
## 10 1.0131 nan 0.2000 0.0046
## 20 0.9315 nan 0.2000 -0.0003
## 40 0.8701 nan 0.2000 -0.0026
## 60 0.8358 nan 0.2000 -0.0016
## 80 0.8122 nan 0.2000 -0.0012
## 100 0.7914 nan 0.2000 -0.0025
## 120 0.7755 nan 0.2000 -0.0023
## 140 0.7617 nan 0.2000 -0.0018
## 160 0.7478 nan 0.2000 -0.0009
## 180 0.7412 nan 0.2000 -0.0007
## 200 0.7330 nan 0.2000 -0.0009
## 220 0.7178 nan 0.2000 -0.0001
## 240 0.7130 nan 0.2000 -0.0010
## 260 0.7057 nan 0.2000 -0.0021
## 280 0.6982 nan 0.2000 -0.0032
## 300 0.6878 nan 0.2000 -0.0019
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## 460 0.6392 nan 0.2000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2248 nan 0.2000 0.0273
## 2 1.1789 nan 0.2000 0.0184
## 3 1.1471 nan 0.2000 0.0144
## 4 1.1128 nan 0.2000 0.0141
## 5 1.0899 nan 0.2000 0.0100
## 6 1.0682 nan 0.2000 0.0088
## 7 1.0477 nan 0.2000 0.0054
## 8 1.0302 nan 0.2000 0.0068
## 9 1.0200 nan 0.2000 0.0031
## 10 1.0063 nan 0.2000 0.0040
## 20 0.9252 nan 0.2000 -0.0010
## 40 0.8687 nan 0.2000 -0.0022
## 60 0.8377 nan 0.2000 -0.0006
## 80 0.8126 nan 0.2000 -0.0021
## 100 0.7959 nan 0.2000 -0.0011
## 120 0.7778 nan 0.2000 -0.0030
## 140 0.7622 nan 0.2000 -0.0011
## 160 0.7490 nan 0.2000 -0.0007
## 180 0.7367 nan 0.2000 -0.0020
## 200 0.7284 nan 0.2000 -0.0014
## 220 0.7193 nan 0.2000 -0.0006
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## 260 0.6997 nan 0.2000 -0.0003
## 280 0.6920 nan 0.2000 -0.0022
## 300 0.6846 nan 0.2000 -0.0013
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2302 nan 0.2000 0.0286
## 2 1.1861 nan 0.2000 0.0131
## 3 1.1541 nan 0.2000 0.0143
## 4 1.1192 nan 0.2000 0.0147
## 5 1.0951 nan 0.2000 0.0110
## 6 1.0714 nan 0.2000 0.0080
## 7 1.0491 nan 0.2000 0.0079
## 8 1.0336 nan 0.2000 0.0025
## 9 1.0160 nan 0.2000 0.0052
## 10 1.0015 nan 0.2000 0.0057
## 20 0.9323 nan 0.2000 -0.0001
## 40 0.8653 nan 0.2000 0.0007
## 60 0.8369 nan 0.2000 -0.0013
## 80 0.8115 nan 0.2000 -0.0010
## 100 0.7916 nan 0.2000 -0.0020
## 120 0.7769 nan 0.2000 -0.0012
## 140 0.7596 nan 0.2000 -0.0018
## 160 0.7476 nan 0.2000 -0.0028
## 180 0.7366 nan 0.2000 -0.0016
## 200 0.7287 nan 0.2000 -0.0012
## 220 0.7223 nan 0.2000 -0.0021
## 240 0.7107 nan 0.2000 -0.0002
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## 380 0.6549 nan 0.2000 -0.0011
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## 460 0.6271 nan 0.2000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2072 nan 0.2000 0.0359
## 2 1.1400 nan 0.2000 0.0326
## 3 1.0897 nan 0.2000 0.0182
## 4 1.0491 nan 0.2000 0.0179
## 5 1.0191 nan 0.2000 0.0105
## 6 0.9972 nan 0.2000 0.0081
## 7 0.9808 nan 0.2000 0.0009
## 8 0.9669 nan 0.2000 0.0011
## 9 0.9579 nan 0.2000 0.0004
## 10 0.9461 nan 0.2000 -0.0001
## 20 0.8593 nan 0.2000 -0.0031
## 40 0.7708 nan 0.2000 -0.0009
## 60 0.7231 nan 0.2000 -0.0009
## 80 0.6777 nan 0.2000 -0.0072
## 100 0.6361 nan 0.2000 -0.0025
## 120 0.6023 nan 0.2000 -0.0026
## 140 0.5740 nan 0.2000 -0.0029
## 160 0.5442 nan 0.2000 -0.0024
## 180 0.5237 nan 0.2000 -0.0019
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2030 nan 0.2000 0.0381
## 2 1.1397 nan 0.2000 0.0290
## 3 1.0979 nan 0.2000 0.0200
## 4 1.0580 nan 0.2000 0.0146
## 5 1.0247 nan 0.2000 0.0085
## 6 0.9977 nan 0.2000 0.0091
## 7 0.9786 nan 0.2000 0.0063
## 8 0.9602 nan 0.2000 0.0055
## 9 0.9478 nan 0.2000 0.0002
## 10 0.9344 nan 0.2000 0.0019
## 20 0.8572 nan 0.2000 0.0022
## 40 0.7849 nan 0.2000 -0.0028
## 60 0.7366 nan 0.2000 -0.0037
## 80 0.6915 nan 0.2000 -0.0022
## 100 0.6463 nan 0.2000 -0.0017
## 120 0.6182 nan 0.2000 -0.0024
## 140 0.5870 nan 0.2000 -0.0025
## 160 0.5591 nan 0.2000 -0.0030
## 180 0.5311 nan 0.2000 -0.0015
## 200 0.5099 nan 0.2000 -0.0015
## 220 0.4873 nan 0.2000 -0.0033
## 240 0.4684 nan 0.2000 -0.0032
## 260 0.4459 nan 0.2000 -0.0017
## 280 0.4236 nan 0.2000 -0.0010
## 300 0.4045 nan 0.2000 -0.0035
## 320 0.3872 nan 0.2000 -0.0003
## 340 0.3751 nan 0.2000 -0.0029
## 360 0.3546 nan 0.2000 -0.0003
## 380 0.3419 nan 0.2000 -0.0007
## 400 0.3289 nan 0.2000 -0.0005
## 420 0.3138 nan 0.2000 -0.0010
## 440 0.3035 nan 0.2000 -0.0013
## 460 0.2902 nan 0.2000 -0.0010
## 480 0.2793 nan 0.2000 -0.0009
## 500 0.2713 nan 0.2000 -0.0023
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2134 nan 0.2000 0.0414
## 2 1.1475 nan 0.2000 0.0279
## 3 1.0974 nan 0.2000 0.0185
## 4 1.0577 nan 0.2000 0.0137
## 5 1.0243 nan 0.2000 0.0075
## 6 0.9991 nan 0.2000 0.0059
## 7 0.9829 nan 0.2000 0.0042
## 8 0.9648 nan 0.2000 0.0051
## 9 0.9450 nan 0.2000 0.0059
## 10 0.9293 nan 0.2000 0.0052
## 20 0.8535 nan 0.2000 -0.0016
## 40 0.7836 nan 0.2000 -0.0023
## 60 0.7268 nan 0.2000 -0.0009
## 80 0.6820 nan 0.2000 -0.0031
## 100 0.6481 nan 0.2000 -0.0016
## 120 0.6164 nan 0.2000 -0.0047
## 140 0.5800 nan 0.2000 -0.0014
## 160 0.5435 nan 0.2000 0.0007
## 180 0.5179 nan 0.2000 -0.0014
## 200 0.4960 nan 0.2000 -0.0024
## 220 0.4754 nan 0.2000 -0.0009
## 240 0.4590 nan 0.2000 -0.0014
## 260 0.4399 nan 0.2000 -0.0021
## 280 0.4147 nan 0.2000 -0.0004
## 300 0.4063 nan 0.2000 -0.0012
## 320 0.3909 nan 0.2000 -0.0021
## 340 0.3702 nan 0.2000 -0.0009
## 360 0.3573 nan 0.2000 -0.0013
## 380 0.3449 nan 0.2000 -0.0032
## 400 0.3324 nan 0.2000 -0.0005
## 420 0.3197 nan 0.2000 -0.0008
## 440 0.3083 nan 0.2000 -0.0016
## 460 0.2973 nan 0.2000 -0.0012
## 480 0.2853 nan 0.2000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2005 nan 0.2000 0.0452
## 2 1.1340 nan 0.2000 0.0301
## 3 1.0762 nan 0.2000 0.0237
## 4 1.0319 nan 0.2000 0.0148
## 5 0.9997 nan 0.2000 0.0101
## 6 0.9675 nan 0.2000 0.0111
## 7 0.9435 nan 0.2000 0.0069
## 8 0.9235 nan 0.2000 0.0054
## 9 0.9069 nan 0.2000 -0.0002
## 10 0.8919 nan 0.2000 0.0028
## 20 0.8139 nan 0.2000 -0.0034
## 40 0.7131 nan 0.2000 -0.0007
## 60 0.6373 nan 0.2000 -0.0016
## 80 0.5790 nan 0.2000 -0.0022
## 100 0.5365 nan 0.2000 -0.0034
## 120 0.4985 nan 0.2000 -0.0036
## 140 0.4592 nan 0.2000 -0.0034
## 160 0.4206 nan 0.2000 -0.0022
## 180 0.3853 nan 0.2000 -0.0026
## 200 0.3563 nan 0.2000 -0.0010
## 220 0.3328 nan 0.2000 -0.0025
## 240 0.3068 nan 0.2000 -0.0008
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## 280 0.2666 nan 0.2000 -0.0024
## 300 0.2474 nan 0.2000 -0.0006
## 320 0.2274 nan 0.2000 -0.0000
## 340 0.2152 nan 0.2000 -0.0019
## 360 0.1991 nan 0.2000 -0.0014
## 380 0.1883 nan 0.2000 -0.0020
## 400 0.1758 nan 0.2000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1992 nan 0.2000 0.0425
## 2 1.1208 nan 0.2000 0.0368
## 3 1.0640 nan 0.2000 0.0288
## 4 1.0268 nan 0.2000 0.0153
## 5 0.9953 nan 0.2000 0.0139
## 6 0.9804 nan 0.2000 0.0014
## 7 0.9663 nan 0.2000 -0.0012
## 8 0.9452 nan 0.2000 0.0055
## 9 0.9273 nan 0.2000 -0.0012
## 10 0.9107 nan 0.2000 0.0007
## 20 0.8217 nan 0.2000 -0.0023
## 40 0.7124 nan 0.2000 -0.0030
## 60 0.6491 nan 0.2000 -0.0027
## 80 0.5864 nan 0.2000 -0.0015
## 100 0.5251 nan 0.2000 -0.0003
## 120 0.4810 nan 0.2000 -0.0031
## 140 0.4324 nan 0.2000 -0.0009
## 160 0.3931 nan 0.2000 -0.0005
## 180 0.3600 nan 0.2000 -0.0003
## 200 0.3305 nan 0.2000 -0.0023
## 220 0.3108 nan 0.2000 -0.0014
## 240 0.2847 nan 0.2000 -0.0020
## 260 0.2668 nan 0.2000 -0.0011
## 280 0.2467 nan 0.2000 -0.0019
## 300 0.2307 nan 0.2000 -0.0012
## 320 0.2174 nan 0.2000 -0.0007
## 340 0.2004 nan 0.2000 -0.0011
## 360 0.1919 nan 0.2000 -0.0017
## 380 0.1799 nan 0.2000 -0.0018
## 400 0.1677 nan 0.2000 -0.0010
## 420 0.1576 nan 0.2000 -0.0002
## 440 0.1479 nan 0.2000 -0.0009
## 460 0.1403 nan 0.2000 -0.0013
## 480 0.1321 nan 0.2000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1873 nan 0.2000 0.0529
## 2 1.1245 nan 0.2000 0.0252
## 3 1.0770 nan 0.2000 0.0177
## 4 1.0364 nan 0.2000 0.0138
## 5 1.0000 nan 0.2000 0.0094
## 6 0.9700 nan 0.2000 0.0078
## 7 0.9474 nan 0.2000 0.0078
## 8 0.9260 nan 0.2000 0.0067
## 9 0.9104 nan 0.2000 0.0009
## 10 0.8967 nan 0.2000 0.0025
## 20 0.7951 nan 0.2000 -0.0010
## 40 0.6893 nan 0.2000 -0.0039
## 60 0.6149 nan 0.2000 -0.0022
## 80 0.5554 nan 0.2000 -0.0072
## 100 0.5153 nan 0.2000 -0.0019
## 120 0.4668 nan 0.2000 -0.0053
## 140 0.4283 nan 0.2000 -0.0022
## 160 0.3927 nan 0.2000 -0.0012
## 180 0.3667 nan 0.2000 -0.0023
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## 220 0.3138 nan 0.2000 -0.0013
## 240 0.2946 nan 0.2000 -0.0025
## 260 0.2747 nan 0.2000 -0.0009
## 280 0.2574 nan 0.2000 -0.0018
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## 320 0.2234 nan 0.2000 -0.0012
## 340 0.2083 nan 0.2000 -0.0005
## 360 0.1949 nan 0.2000 -0.0005
## 380 0.1840 nan 0.2000 -0.0003
## 400 0.1732 nan 0.2000 -0.0014
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## 460 0.1430 nan 0.2000 -0.0007
## 480 0.1344 nan 0.2000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2075 nan 0.3000 0.0455
## 2 1.1512 nan 0.3000 0.0275
## 3 1.1095 nan 0.3000 0.0126
## 4 1.0829 nan 0.3000 0.0053
## 5 1.0410 nan 0.3000 0.0214
## 6 1.0173 nan 0.3000 0.0103
## 7 0.9980 nan 0.3000 0.0062
## 8 0.9824 nan 0.3000 0.0057
## 9 0.9650 nan 0.3000 0.0074
## 10 0.9558 nan 0.3000 -0.0001
## 20 0.8963 nan 0.3000 -0.0013
## 40 0.8342 nan 0.3000 -0.0040
## 60 0.7955 nan 0.3000 -0.0002
## 80 0.7671 nan 0.3000 -0.0030
## 100 0.7504 nan 0.3000 -0.0043
## 120 0.7436 nan 0.3000 -0.0078
## 140 0.7311 nan 0.3000 -0.0016
## 160 0.7178 nan 0.3000 -0.0020
## 180 0.7035 nan 0.3000 -0.0038
## 200 0.6938 nan 0.3000 -0.0053
## 220 0.6824 nan 0.3000 -0.0016
## 240 0.6669 nan 0.3000 -0.0020
## 260 0.6606 nan 0.3000 -0.0039
## 280 0.6480 nan 0.3000 -0.0007
## 300 0.6330 nan 0.3000 -0.0023
## 320 0.6218 nan 0.3000 -0.0037
## 340 0.6135 nan 0.3000 -0.0010
## 360 0.6060 nan 0.3000 -0.0018
## 380 0.5968 nan 0.3000 -0.0034
## 400 0.5916 nan 0.3000 -0.0026
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## 460 0.5653 nan 0.3000 -0.0017
## 480 0.5560 nan 0.3000 -0.0017
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2022 nan 0.3000 0.0383
## 2 1.1468 nan 0.3000 0.0184
## 3 1.0830 nan 0.3000 0.0209
## 4 1.0475 nan 0.3000 0.0120
## 5 1.0288 nan 0.3000 0.0053
## 6 1.0021 nan 0.3000 0.0061
## 7 0.9895 nan 0.3000 0.0034
## 8 0.9774 nan 0.3000 0.0042
## 9 0.9675 nan 0.3000 0.0026
## 10 0.9577 nan 0.3000 0.0011
## 20 0.8958 nan 0.3000 -0.0033
## 40 0.8543 nan 0.3000 -0.0026
## 60 0.8191 nan 0.3000 -0.0054
## 80 0.7859 nan 0.3000 -0.0033
## 100 0.7611 nan 0.3000 -0.0043
## 120 0.7433 nan 0.3000 -0.0039
## 140 0.7296 nan 0.3000 -0.0033
## 160 0.7159 nan 0.3000 -0.0005
## 180 0.7080 nan 0.3000 -0.0041
## 200 0.6960 nan 0.3000 -0.0027
## 220 0.6863 nan 0.3000 -0.0054
## 240 0.6749 nan 0.3000 -0.0014
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## 280 0.6529 nan 0.3000 -0.0019
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## 320 0.6324 nan 0.3000 -0.0026
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## 380 0.6008 nan 0.3000 -0.0009
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## 440 0.5808 nan 0.3000 -0.0021
## 460 0.5709 nan 0.3000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2029 nan 0.3000 0.0425
## 2 1.1540 nan 0.3000 0.0207
## 3 1.1150 nan 0.3000 0.0199
## 4 1.0742 nan 0.3000 0.0195
## 5 1.0487 nan 0.3000 0.0032
## 6 1.0152 nan 0.3000 0.0111
## 7 0.9942 nan 0.3000 0.0075
## 8 0.9790 nan 0.3000 0.0034
## 9 0.9625 nan 0.3000 0.0062
## 10 0.9533 nan 0.3000 -0.0002
## 20 0.8916 nan 0.3000 -0.0072
## 40 0.8410 nan 0.3000 -0.0035
## 60 0.8123 nan 0.3000 0.0002
## 80 0.7842 nan 0.3000 -0.0012
## 100 0.7727 nan 0.3000 -0.0046
## 120 0.7557 nan 0.3000 -0.0025
## 140 0.7371 nan 0.3000 -0.0025
## 160 0.7216 nan 0.3000 -0.0025
## 180 0.7015 nan 0.3000 -0.0024
## 200 0.6931 nan 0.3000 -0.0029
## 220 0.6771 nan 0.3000 -0.0024
## 240 0.6652 nan 0.3000 -0.0036
## 260 0.6518 nan 0.3000 -0.0006
## 280 0.6419 nan 0.3000 -0.0035
## 300 0.6353 nan 0.3000 -0.0044
## 320 0.6298 nan 0.3000 -0.0031
## 340 0.6234 nan 0.3000 -0.0056
## 360 0.6179 nan 0.3000 -0.0009
## 380 0.6074 nan 0.3000 -0.0036
## 400 0.5985 nan 0.3000 -0.0027
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1747 nan 0.3000 0.0587
## 2 1.0993 nan 0.3000 0.0381
## 3 1.0529 nan 0.3000 0.0105
## 4 1.0104 nan 0.3000 0.0184
## 5 0.9683 nan 0.3000 0.0140
## 6 0.9444 nan 0.3000 0.0053
## 7 0.9246 nan 0.3000 0.0014
## 8 0.9117 nan 0.3000 0.0032
## 9 0.9000 nan 0.3000 0.0012
## 10 0.8866 nan 0.3000 0.0034
## 20 0.8041 nan 0.3000 -0.0054
## 40 0.7379 nan 0.3000 -0.0061
## 60 0.6870 nan 0.3000 -0.0032
## 80 0.6361 nan 0.3000 -0.0018
## 100 0.5913 nan 0.3000 -0.0022
## 120 0.5533 nan 0.3000 -0.0011
## 140 0.5167 nan 0.3000 -0.0045
## 160 0.4861 nan 0.3000 -0.0033
## 180 0.4565 nan 0.3000 -0.0023
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## 220 0.3959 nan 0.3000 -0.0012
## 240 0.3686 nan 0.3000 -0.0006
## 260 0.3486 nan 0.3000 -0.0013
## 280 0.3288 nan 0.3000 -0.0024
## 300 0.3122 nan 0.3000 -0.0017
## 320 0.2929 nan 0.3000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1784 nan 0.3000 0.0525
## 2 1.1178 nan 0.3000 0.0209
## 3 1.0500 nan 0.3000 0.0289
## 4 1.0145 nan 0.3000 0.0125
## 5 0.9825 nan 0.3000 0.0102
## 6 0.9546 nan 0.3000 0.0053
## 7 0.9341 nan 0.3000 0.0049
## 8 0.9129 nan 0.3000 0.0073
## 9 0.9024 nan 0.3000 -0.0019
## 10 0.8930 nan 0.3000 0.0016
## 20 0.8171 nan 0.3000 0.0010
## 40 0.7297 nan 0.3000 -0.0068
## 60 0.6712 nan 0.3000 -0.0042
## 80 0.6134 nan 0.3000 -0.0023
## 100 0.5664 nan 0.3000 -0.0028
## 120 0.5169 nan 0.3000 -0.0033
## 140 0.4936 nan 0.3000 -0.0043
## 160 0.4661 nan 0.3000 -0.0048
## 180 0.4341 nan 0.3000 -0.0033
## 200 0.4049 nan 0.3000 -0.0042
## 220 0.3799 nan 0.3000 -0.0023
## 240 0.3530 nan 0.3000 -0.0030
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## 280 0.3208 nan 0.3000 -0.0041
## 300 0.3020 nan 0.3000 -0.0043
## 320 0.2855 nan 0.3000 -0.0030
## 340 0.2675 nan 0.3000 -0.0019
## 360 0.2536 nan 0.3000 -0.0007
## 380 0.2417 nan 0.3000 -0.0023
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## 460 0.1955 nan 0.3000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1669 nan 0.3000 0.0447
## 2 1.0854 nan 0.3000 0.0377
## 3 1.0371 nan 0.3000 0.0164
## 4 1.0069 nan 0.3000 0.0103
## 5 0.9698 nan 0.3000 0.0166
## 6 0.9500 nan 0.3000 0.0055
## 7 0.9297 nan 0.3000 0.0057
## 8 0.9152 nan 0.3000 0.0046
## 9 0.9071 nan 0.3000 -0.0051
## 10 0.8902 nan 0.3000 0.0018
## 20 0.8317 nan 0.3000 -0.0028
## 40 0.7433 nan 0.3000 -0.0034
## 60 0.6825 nan 0.3000 -0.0026
## 80 0.6150 nan 0.3000 -0.0050
## 100 0.5776 nan 0.3000 -0.0049
## 120 0.5333 nan 0.3000 -0.0057
## 140 0.4929 nan 0.3000 -0.0011
## 160 0.4616 nan 0.3000 -0.0038
## 180 0.4384 nan 0.3000 -0.0030
## 200 0.4072 nan 0.3000 -0.0019
## 220 0.3739 nan 0.3000 0.0001
## 240 0.3559 nan 0.3000 -0.0021
## 260 0.3308 nan 0.3000 -0.0017
## 280 0.3145 nan 0.3000 -0.0021
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## 320 0.2726 nan 0.3000 -0.0042
## 340 0.2571 nan 0.3000 -0.0011
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## 380 0.2308 nan 0.3000 -0.0018
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1463 nan 0.3000 0.0759
## 2 1.0702 nan 0.3000 0.0323
## 3 1.0261 nan 0.3000 0.0041
## 4 0.9689 nan 0.3000 0.0232
## 5 0.9313 nan 0.3000 0.0086
## 6 0.9116 nan 0.3000 0.0018
## 7 0.8905 nan 0.3000 -0.0004
## 8 0.8805 nan 0.3000 -0.0042
## 9 0.8564 nan 0.3000 0.0060
## 10 0.8414 nan 0.3000 -0.0013
## 20 0.7490 nan 0.3000 -0.0054
## 40 0.6407 nan 0.3000 -0.0010
## 60 0.5728 nan 0.3000 -0.0109
## 80 0.5055 nan 0.3000 -0.0087
## 100 0.4232 nan 0.3000 -0.0013
## 120 0.3781 nan 0.3000 -0.0012
## 140 0.3408 nan 0.3000 -0.0032
## 160 0.3020 nan 0.3000 -0.0024
## 180 0.2733 nan 0.3000 -0.0031
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## 220 0.2206 nan 0.3000 -0.0005
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## 280 0.1655 nan 0.3000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1473 nan 0.3000 0.0614
## 2 1.0589 nan 0.3000 0.0282
## 3 0.9924 nan 0.3000 0.0268
## 4 0.9584 nan 0.3000 0.0085
## 5 0.9321 nan 0.3000 0.0034
## 6 0.9130 nan 0.3000 -0.0001
## 7 0.8952 nan 0.3000 -0.0013
## 8 0.8837 nan 0.3000 -0.0014
## 9 0.8726 nan 0.3000 -0.0021
## 10 0.8588 nan 0.3000 -0.0001
## 20 0.7720 nan 0.3000 -0.0105
## 40 0.6569 nan 0.3000 -0.0038
## 60 0.5673 nan 0.3000 -0.0069
## 80 0.5062 nan 0.3000 -0.0073
## 100 0.4461 nan 0.3000 -0.0068
## 120 0.3924 nan 0.3000 -0.0040
## 140 0.3518 nan 0.3000 -0.0027
## 160 0.3136 nan 0.3000 -0.0017
## 180 0.2826 nan 0.3000 -0.0026
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## 220 0.2228 nan 0.3000 -0.0018
## 240 0.2054 nan 0.3000 -0.0019
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1471 nan 0.3000 0.0588
## 2 1.0751 nan 0.3000 0.0282
## 3 1.0243 nan 0.3000 0.0193
## 4 0.9835 nan 0.3000 0.0131
## 5 0.9450 nan 0.3000 0.0084
## 6 0.9224 nan 0.3000 0.0019
## 7 0.9063 nan 0.3000 0.0014
## 8 0.8856 nan 0.3000 -0.0003
## 9 0.8699 nan 0.3000 -0.0015
## 10 0.8544 nan 0.3000 -0.0017
## 20 0.7754 nan 0.3000 -0.0104
## 40 0.6828 nan 0.3000 -0.0043
## 60 0.5827 nan 0.3000 -0.0090
## 80 0.5111 nan 0.3000 -0.0055
## 100 0.4516 nan 0.3000 -0.0028
## 120 0.4021 nan 0.3000 -0.0048
## 140 0.3570 nan 0.3000 -0.0039
## 160 0.3219 nan 0.3000 0.0002
## 180 0.2942 nan 0.3000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1710 nan 0.5000 0.0485
## 2 1.0951 nan 0.5000 0.0313
## 3 1.0462 nan 0.5000 0.0165
## 4 0.9888 nan 0.5000 0.0194
## 5 0.9618 nan 0.5000 0.0122
## 6 0.9436 nan 0.5000 0.0038
## 7 0.9296 nan 0.5000 -0.0021
## 8 0.9156 nan 0.5000 0.0041
## 9 0.9057 nan 0.5000 0.0031
## 10 0.9012 nan 0.5000 -0.0062
## 20 0.8565 nan 0.5000 0.0018
## 40 0.8041 nan 0.5000 -0.0046
## 60 2.0564 nan 0.5000 -0.0076
## 80 2.0228 nan 0.5000 -0.0020
## 100 2.0211 nan 0.5000 -0.0005
## 120 2.0114 nan 0.5000 -0.0001
## 140 1.9957 nan 0.5000 -0.0031
## 160 1.9941 nan 0.5000 -0.0006
## 180 1.9848 nan 0.5000 0.0003
## 200 1.9704 nan 0.5000 0.0021
## 220 1.9625 nan 0.5000 -0.0099
## 240 1.9519 nan 0.5000 -0.0028
## 260 1.9459 nan 0.5000 0.0006
## 280 1.9394 nan 0.5000 -0.0001
## 300 1.9301 nan 0.5000 0.0006
## 320 1.9247 nan 0.5000 -0.0000
## 340 1.9223 nan 0.5000 0.0001
## 360 1.9227 nan 0.5000 0.0004
## 380 1.9108 nan 0.5000 0.0002
## 400 1.9089 nan 0.5000 -0.0018
## 420 1.9048 nan 0.5000 -0.0004
## 440 1.9000 nan 0.5000 0.0002
## 460 1.8952 nan 0.5000 -0.0013
## 480 1.8856 nan 0.5000 -0.0002
## 500 1.8757 nan 0.5000 -0.0022
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1522 nan 0.5000 0.0565
## 2 1.0921 nan 0.5000 0.0231
## 3 1.0420 nan 0.5000 0.0222
## 4 1.0127 nan 0.5000 0.0117
## 5 0.9912 nan 0.5000 0.0076
## 6 0.9741 nan 0.5000 0.0018
## 7 0.9590 nan 0.5000 -0.0025
## 8 0.9485 nan 0.5000 -0.0044
## 9 0.9372 nan 0.5000 -0.0002
## 10 0.9181 nan 0.5000 0.0069
## 20 0.8734 nan 0.5000 -0.0028
## 40 0.8228 nan 0.5000 0.0031
## 60 0.7829 nan 0.5000 -0.0036
## 80 0.7386 nan 0.5000 -0.0016
## 100 0.7175 nan 0.5000 -0.0035
## 120 0.7004 nan 0.5000 -0.0090
## 140 0.6751 nan 0.5000 -0.0076
## 160 0.6697 nan 0.5000 -0.0053
## 180 0.6535 nan 0.5000 -0.0037
## 200 0.6431 nan 0.5000 -0.0026
## 220 0.6263 nan 0.5000 -0.0022
## 240 0.6094 nan 0.5000 -0.0102
## 260 0.5916 nan 0.5000 0.0000
## 280 0.5828 nan 0.5000 -0.0084
## 300 0.5732 nan 0.5000 -0.0053
## 320 0.5687 nan 0.5000 -0.0048
## 340 0.5584 nan 0.5000 -0.0083
## 360 0.5566 nan 0.5000 -0.0087
## 380 0.5411 nan 0.5000 -0.0034
## 400 0.5274 nan 0.5000 -0.0077
## 420 0.5146 nan 0.5000 -0.0051
## 440 0.5087 nan 0.5000 -0.0032
## 460 0.4985 nan 0.5000 -0.0062
## 480 0.4880 nan 0.5000 -0.0055
## 500 0.4812 nan 0.5000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1682 nan 0.5000 0.0670
## 2 1.0964 nan 0.5000 0.0222
## 3 1.0396 nan 0.5000 0.0237
## 4 0.9984 nan 0.5000 0.0136
## 5 0.9667 nan 0.5000 0.0121
## 6 0.9445 nan 0.5000 0.0077
## 7 0.9374 nan 0.5000 -0.0021
## 8 0.9301 nan 0.5000 -0.0094
## 9 0.9159 nan 0.5000 0.0032
## 10 0.9062 nan 0.5000 0.0042
## 20 0.8683 nan 0.5000 -0.0036
## 40 0.8099 nan 0.5000 -0.0039
## 60 0.7688 nan 0.5000 -0.0031
## 80 0.7452 nan 0.5000 -0.0045
## 100 0.7263 nan 0.5000 -0.0092
## 120 0.7038 nan 0.5000 -0.0036
## 140 0.7906 nan 0.5000 -0.0007
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1192 nan 0.5000 0.0736
## 2 1.0411 nan 0.5000 0.0186
## 3 0.9880 nan 0.5000 0.0169
## 4 0.9645 nan 0.5000 -0.0067
## 5 0.9337 nan 0.5000 0.0064
## 6 0.9089 nan 0.5000 0.0008
## 7 0.8906 nan 0.5000 -0.0000
## 8 0.8742 nan 0.5000 0.0025
## 9 0.8689 nan 0.5000 -0.0045
## 10 0.8640 nan 0.5000 -0.0042
## 20 0.7757 nan 0.5000 -0.0023
## 40 0.6850 nan 0.5000 -0.0097
## 60 0.6303 nan 0.5000 -0.0078
## 80 0.5686 nan 0.5000 -0.0047
## 100 0.5130 nan 0.5000 -0.0065
## 120 0.4798 nan 0.5000 -0.0092
## 140 0.7864 nan 0.5000 -0.0099
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1221 nan 0.5000 0.0619
## 2 1.0238 nan 0.5000 0.0404
## 3 0.9756 nan 0.5000 0.0082
## 4 0.9498 nan 0.5000 0.0073
## 5 0.9302 nan 0.5000 0.0020
## 6 0.9183 nan 0.5000 -0.0135
## 7 0.9026 nan 0.5000 -0.0067
## 8 0.8800 nan 0.5000 -0.0002
## 9 0.8692 nan 0.5000 -0.0029
## 10 0.8674 nan 0.5000 -0.0141
## 20 0.7942 nan 0.5000 -0.0087
## 40 0.7189 nan 0.5000 -0.0153
## 60 0.6610 nan 0.5000 -0.0109
## 80 0.5680 nan 0.5000 -0.0086
## 100 0.5293 nan 0.5000 -0.0033
## 120 0.4820 nan 0.5000 -0.0098
## 140 0.4093 nan 0.5000 -0.0075
## 160 0.3754 nan 0.5000 -0.0040
## 180 0.3349 nan 0.5000 -0.0053
## 200 0.2904 nan 0.5000 -0.0031
## 220 0.2601 nan 0.5000 -0.0042
## 240 0.2379 nan 0.5000 -0.0029
## 260 0.2127 nan 0.5000 -0.0014
## 280 0.1970 nan 0.5000 -0.0010
## 300 0.1742 nan 0.5000 -0.0010
## 320 0.1611 nan 0.5000 -0.0035
## 340 0.1470 nan 0.5000 -0.0012
## 360 0.1451 nan 0.5000 -0.0023
## 380 0.1340 nan 0.5000 -0.0018
## 400 0.1161 nan 0.5000 -0.0009
## 420 0.1042 nan 0.5000 -0.0017
## 440 0.0948 nan 0.5000 -0.0005
## 460 0.0897 nan 0.5000 -0.0016
## 480 0.0825 nan 0.5000 -0.0022
## 500 0.0782 nan 0.5000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1195 nan 0.5000 0.0730
## 2 1.0288 nan 0.5000 0.0349
## 3 0.9716 nan 0.5000 0.0225
## 4 0.9409 nan 0.5000 0.0033
## 5 0.9317 nan 0.5000 -0.0110
## 6 0.9064 nan 0.5000 -0.0021
## 7 0.8937 nan 0.5000 0.0009
## 8 0.8884 nan 0.5000 -0.0129
## 9 0.8693 nan 0.5000 -0.0009
## 10 0.8585 nan 0.5000 -0.0052
## 20 0.7966 nan 0.5000 -0.0053
## 40 0.7055 nan 0.5000 -0.0141
## 60 0.6576 nan 0.5000 -0.0126
## 80 0.5744 nan 0.5000 -0.0038
## 100 0.4981 nan 0.5000 -0.0038
## 120 0.4504 nan 0.5000 -0.0135
## 140 0.3979 nan 0.5000 -0.0058
## 160 0.3592 nan 0.5000 -0.0029
## 180 0.3334 nan 0.5000 -0.0031
## 200 0.2936 nan 0.5000 -0.0031
## 220 0.2594 nan 0.5000 -0.0014
## 240 0.2366 nan 0.5000 -0.0040
## 260 0.2177 nan 0.5000 -0.0024
## 280 0.2015 nan 0.5000 -0.0030
## 300 0.1861 nan 0.5000 -0.0013
## 320 0.1679 nan 0.5000 -0.0048
## 340 0.1550 nan 0.5000 -0.0020
## 360 0.1415 nan 0.5000 -0.0011
## 380 0.1264 nan 0.5000 -0.0023
## 400 0.1116 nan 0.5000 -0.0005
## 420 0.1063 nan 0.5000 -0.0018
## 440 0.1000 nan 0.5000 -0.0006
## 460 0.0948 nan 0.5000 -0.0012
## 480 0.0878 nan 0.5000 -0.0014
## 500 0.0809 nan 0.5000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0901 nan 0.5000 0.0955
## 2 0.9834 nan 0.5000 0.0418
## 3 0.9382 nan 0.5000 0.0057
## 4 0.9014 nan 0.5000 0.0099
## 5 0.8886 nan 0.5000 -0.0175
## 6 0.8750 nan 0.5000 -0.0187
## 7 0.8603 nan 0.5000 -0.0101
## 8 0.8424 nan 0.5000 -0.0090
## 9 0.8259 nan 0.5000 -0.0000
## 10 0.8167 nan 0.5000 -0.0069
## 20 0.7540 nan 0.5000 0.0068
## 40 0.6025 nan 0.5000 -0.0053
## 60 0.5664 nan 0.5000 -0.0817
## 80 0.4150 nan 0.5000 -0.0045
## 100 0.3249 nan 0.5000 -0.0033
## 120 0.2645 nan 0.5000 -0.0036
## 140 0.2123 nan 0.5000 -0.0004
## 160 0.1798 nan 0.5000 -0.0030
## 180 0.1534 nan 0.5000 -0.0004
## 200 0.1320 nan 0.5000 -0.0021
## 220 0.1129 nan 0.5000 -0.0009
## 240 0.0983 nan 0.5000 -0.0017
## 260 0.0837 nan 0.5000 -0.0018
## 280 0.0763 nan 0.5000 -0.0018
## 300 0.0653 nan 0.5000 -0.0015
## 320 0.0572 nan 0.5000 -0.0004
## 340 0.0498 nan 0.5000 -0.0009
## 360 0.0441 nan 0.5000 -0.0005
## 380 0.0389 nan 0.5000 -0.0004
## 400 0.0345 nan 0.5000 -0.0004
## 420 0.0303 nan 0.5000 -0.0007
## 440 0.0276 nan 0.5000 -0.0000
## 460 0.0253 nan 0.5000 -0.0003
## 480 0.0225 nan 0.5000 -0.0006
## 500 0.0204 nan 0.5000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0986 nan 0.5000 0.1028
## 2 0.9880 nan 0.5000 0.0413
## 3 0.9513 nan 0.5000 -0.0022
## 4 0.9311 nan 0.5000 -0.0073
## 5 0.8997 nan 0.5000 -0.0002
## 6 0.8785 nan 0.5000 -0.0042
## 7 0.8578 nan 0.5000 -0.0048
## 8 0.8412 nan 0.5000 -0.0040
## 9 0.8308 nan 0.5000 -0.0134
## 10 0.8212 nan 0.5000 -0.0126
## 20 0.7138 nan 0.5000 -0.0005
## 40 0.5579 nan 0.5000 -0.0046
## 60 0.4566 nan 0.5000 0.0012
## 80 0.3976 nan 0.5000 -0.0070
## 100 0.3212 nan 0.5000 -0.0006
## 120 0.2628 nan 0.5000 -0.0031
## 140 0.2137 nan 0.5000 -0.0034
## 160 0.1802 nan 0.5000 -0.0029
## 180 0.1556 nan 0.5000 -0.0019
## 200 0.1334 nan 0.5000 -0.0021
## 220 0.1191 nan 0.5000 -0.0036
## 240 0.1041 nan 0.5000 -0.0004
## 260 0.0910 nan 0.5000 -0.0015
## 280 0.0793 nan 0.5000 -0.0016
## 300 0.0688 nan 0.5000 -0.0013
## 320 0.0624 nan 0.5000 -0.0003
## 340 0.0552 nan 0.5000 -0.0012
## 360 0.0496 nan 0.5000 -0.0010
## 380 0.0425 nan 0.5000 -0.0007
## 400 0.0371 nan 0.5000 -0.0002
## 420 0.0328 nan 0.5000 -0.0002
## 440 0.0281 nan 0.5000 -0.0000
## 460 0.0259 nan 0.5000 -0.0005
## 480 0.0233 nan 0.5000 -0.0001
## 500 0.0206 nan 0.5000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0964 nan 0.5000 0.0644
## 2 0.9911 nan 0.5000 0.0386
## 3 0.9418 nan 0.5000 0.0068
## 4 0.8993 nan 0.5000 0.0111
## 5 0.8862 nan 0.5000 -0.0183
## 6 0.8551 nan 0.5000 0.0015
## 7 0.8397 nan 0.5000 -0.0038
## 8 0.8244 nan 0.5000 -0.0073
## 9 0.8108 nan 0.5000 -0.0065
## 10 0.8059 nan 0.5000 -0.0068
## 20 0.7262 nan 0.5000 -0.0109
## 40 0.6202 nan 0.5000 -0.0056
## 60 0.5056 nan 0.5000 -0.0055
## 80 0.4096 nan 0.5000 -0.0088
## 100 0.3324 nan 0.5000 -0.0029
## 120 0.2587 nan 0.5000 -0.0002
## 140 0.2131 nan 0.5000 -0.0037
## 160 0.1820 nan 0.5000 -0.0035
## 180 0.1541 nan 0.5000 -0.0021
## 200 0.1369 nan 0.5000 -0.0015
## 220 0.1177 nan 0.5000 -0.0023
## 240 0.0985 nan 0.5000 -0.0022
## 260 0.0850 nan 0.5000 -0.0011
## 280 0.0731 nan 0.5000 0.0000
## 300 0.0636 nan 0.5000 -0.0012
## 320 0.0564 nan 0.5000 -0.0007
## 340 0.0515 nan 0.5000 -0.0004
## 360 0.0465 nan 0.5000 -0.0006
## 380 0.0413 nan 0.5000 -0.0003
## 400 0.0360 nan 0.5000 -0.0005
## 420 0.0319 nan 0.5000 -0.0003
## 440 0.0281 nan 0.5000 -0.0002
## 460 0.0240 nan 0.5000 -0.0003
## 480 0.0213 nan 0.5000 -0.0005
## 500 0.0192 nan 0.5000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1365 nan 1.0000 0.0458
## 2 1.0480 nan 1.0000 0.0309
## 3 1.0218 nan 1.0000 -0.0043
## 4 0.9830 nan 1.0000 0.0087
## 5 0.9648 nan 1.0000 0.0018
## 6 0.9655 nan 1.0000 -0.0185
## 7 0.9652 nan 1.0000 -0.0210
## 8 0.9555 nan 1.0000 -0.0102
## 9 1.0261 nan 1.0000 -0.0902
## 10 1.0386 nan 1.0000 -0.0252
## 20 36.3092 nan 1.0000 -0.0007
## 40 36.2980 nan 1.0000 -0.0343
## 60 36.6792 nan 1.0000 0.0036
## 80 37649.1222 nan 1.0000 -0.0013
## 100 37649.0938 nan 1.0000 0.0006
## 120 37649.0398 nan 1.0000 -0.0013
## 140 816676.4265 nan 1.0000 -0.0002
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1278 nan 1.0000 0.0655
## 2 1.0817 nan 1.0000 0.0106
## 3 1.0540 nan 1.0000 -0.0297
## 4 1.0351 nan 1.0000 -0.0245
## 5 1.0163 nan 1.0000 -0.0050
## 6 1.0152 nan 1.0000 -0.0308
## 7 0.9993 nan 1.0000 -0.0023
## 8 0.9775 nan 1.0000 -0.0108
## 9 0.9921 nan 1.0000 -0.0377
## 10 0.9652 nan 1.0000 -0.0099
## 20 0.9753 nan 1.0000 -0.0147
## 40 2.8221 nan 1.0000 0.0062
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 -0.0194
## 160 2995.3187 nan 1.0000 0.0005
## 180 3389.0624 nan 1.0000 -0.0040
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1375 nan 1.0000 0.0708
## 2 1.0896 nan 1.0000 0.0013
## 3 1.0181 nan 1.0000 0.0417
## 4 0.9913 nan 1.0000 0.0006
## 5 0.9867 nan 1.0000 -0.0242
## 6 1.0111 nan 1.0000 -0.0347
## 7 1.0029 nan 1.0000 -0.0065
## 8 0.9940 nan 1.0000 -0.0158
## 9 0.9719 nan 1.0000 0.0041
## 10 0.9829 nan 1.0000 -0.0287
## 20 0.8916 nan 1.0000 -0.0063
## 40 163.8002 nan 1.0000 -0.0483
## 60 164.7028 nan 1.0000 0.0050
## 80 164.7520 nan 1.0000 0.0138
## 100 174.2501 nan 1.0000 -0.0005
## 120 174.2137 nan 1.0000 -0.0020
## 140 174.1954 nan 1.0000 -0.0065
## 160 174.1781 nan 1.0000 -0.0067
## 180 173.9277 nan 1.0000 0.0021
## 200 173.9052 nan 1.0000 -0.0023
## 220 173.7962 nan 1.0000 0.0190
## 240 173.7537 nan 1.0000 -0.0040
## 260 173.6956 nan 1.0000 0.0174
## 280 173.6691 nan 1.0000 -0.0000
## 300 173.5915 nan 1.0000 0.0072
## 320 173.5599 nan 1.0000 -0.0023
## 340 173.4880 nan 1.0000 -0.0000
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0755 nan 1.0000 0.0691
## 2 0.9830 nan 1.0000 0.0268
## 3 0.9686 nan 1.0000 -0.0199
## 4 0.9692 nan 1.0000 -0.0235
## 5 0.9841 nan 1.0000 -0.0407
## 6 0.9626 nan 1.0000 -0.0110
## 7 0.9487 nan 1.0000 -0.0136
## 8 0.9535 nan 1.0000 -0.0381
## 9 0.9640 nan 1.0000 -0.0471
## 10 0.9484 nan 1.0000 -0.0237
## 20 0.9341 nan 1.0000 -0.0285
## 40 14.1276 nan 1.0000 -7.7784
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0644 nan 1.0000 0.0944
## 2 0.9965 nan 1.0000 0.0115
## 3 0.9450 nan 1.0000 0.0132
## 4 0.9524 nan 1.0000 -0.0391
## 5 0.9419 nan 1.0000 -0.0132
## 6 1.2691 nan 1.0000 -0.3626
## 7 1.2764 nan 1.0000 -0.0551
## 8 1.2291 nan 1.0000 0.0164
## 9 1114478821.4872 nan 1.0000 -557239410.2070
## 10 1114478821.4777 nan 1.0000 -0.0116
## 20 1114478821.4788 nan 1.0000 -0.0614
## 40 1114478821.3185 nan 1.0000 -0.0433
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## 360 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0685 nan 1.0000 0.0988
## 2 1.0128 nan 1.0000 0.0074
## 3 0.9975 nan 1.0000 -0.0298
## 4 0.9792 nan 1.0000 -0.0284
## 5 0.9852 nan 1.0000 -0.0501
## 6 1.0977 nan 1.0000 -0.1047
## 7 1.1714 nan 1.0000 -0.1644
## 8 inf nan 1.0000 -inf
## 9 inf nan 1.0000 nan
## 10 inf nan 1.0000 nan
## 20 inf nan 1.0000 -0.0078
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0148 nan 1.0000 0.1035
## 2 0.9669 nan 1.0000 -0.0045
## 3 0.9533 nan 1.0000 -0.0423
## 4 0.9436 nan 1.0000 -0.0392
## 5 0.9279 nan 1.0000 -0.0210
## 6 1.0522 nan 1.0000 -0.1711
## 7 0.9779 nan 1.0000 -0.0190
## 8 0.9504 nan 1.0000 -0.0288
## 9 0.9987 nan 1.0000 -0.0959
## 10 0.9713 nan 1.0000 -0.0450
## 20 0.9327 nan 1.0000 -0.0713
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
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## 300 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0419 nan 1.0000 0.1137
## 2 0.9745 nan 1.0000 0.0049
## 3 0.9600 nan 1.0000 -0.0482
## 4 0.9150 nan 1.0000 -0.0105
## 5 0.8893 nan 1.0000 -0.0186
## 6 0.8974 nan 1.0000 -0.0564
## 7 0.8918 nan 1.0000 -0.0280
## 8 0.8706 nan 1.0000 -0.0292
## 9 0.8461 nan 1.0000 -0.0261
## 10 0.9082 nan 1.0000 -0.0997
## 20 163902.6421 nan 1.0000 -163901.7564
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0603 nan 1.0000 0.0999
## 2 0.9657 nan 1.0000 0.0221
## 3 0.9547 nan 1.0000 -0.0362
## 4 0.9437 nan 1.0000 -0.0320
## 5 0.9648 nan 1.0000 -0.0803
## 6 0.9240 nan 1.0000 -0.0155
## 7 0.9186 nan 1.0000 -0.0305
## 8 0.9079 nan 1.0000 -0.0186
## 9 0.9323 nan 1.0000 -0.0526
## 10 1.0034 nan 1.0000 -0.1166
## 20 1.0640 nan 1.0000 -0.1789
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## 3 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2900 nan 0.0010 0.0002
## 10 1.2896 nan 0.0010 0.0001
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2791 nan 0.0010 0.0002
## 60 1.2724 nan 0.0010 0.0001
## 80 1.2659 nan 0.0010 0.0001
## 100 1.2595 nan 0.0010 0.0001
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## 140 1.2476 nan 0.0010 0.0001
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## 180 1.2364 nan 0.0010 0.0001
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## 220 1.2256 nan 0.0010 0.0001
## 240 1.2206 nan 0.0010 0.0001
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## 320 1.2019 nan 0.0010 0.0001
## 340 1.1976 nan 0.0010 0.0001
## 360 1.1934 nan 0.0010 0.0001
## 380 1.1892 nan 0.0010 0.0001
## 400 1.1851 nan 0.0010 0.0001
## 420 1.1812 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2909 nan 0.0010 0.0001
## 8 1.2905 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2860 nan 0.0010 0.0001
## 40 1.2791 nan 0.0010 0.0001
## 60 1.2725 nan 0.0010 0.0002
## 80 1.2660 nan 0.0010 0.0001
## 100 1.2597 nan 0.0010 0.0001
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## 140 1.2480 nan 0.0010 0.0001
## 160 1.2423 nan 0.0010 0.0001
## 180 1.2367 nan 0.0010 0.0001
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## 340 1.1978 nan 0.0010 0.0001
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## 380 1.1892 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2931 nan 0.0010 0.0002
## 2 1.2927 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
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## 10 1.2897 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2791 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2911 nan 0.0010 0.0002
## 6 1.2906 nan 0.0010 0.0002
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## 8 1.2897 nan 0.0010 0.0002
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## 20 1.2840 nan 0.0010 0.0002
## 40 1.2749 nan 0.0010 0.0002
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## 140 1.2334 nan 0.0010 0.0002
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## 180 1.2187 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0003
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
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## 40 1.2746 nan 0.0010 0.0002
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## 300 1.1772 nan 0.0010 0.0001
## 320 1.1710 nan 0.0010 0.0001
## 340 1.1650 nan 0.0010 0.0001
## 360 1.1593 nan 0.0010 0.0001
## 380 1.1537 nan 0.0010 0.0001
## 400 1.1482 nan 0.0010 0.0001
## 420 1.1427 nan 0.0010 0.0001
## 440 1.1374 nan 0.0010 0.0001
## 460 1.1326 nan 0.0010 0.0001
## 480 1.1274 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2895 nan 0.0010 0.0002
## 9 1.2891 nan 0.0010 0.0002
## 10 1.2886 nan 0.0010 0.0002
## 20 1.2840 nan 0.0010 0.0002
## 40 1.2749 nan 0.0010 0.0002
## 60 1.2661 nan 0.0010 0.0002
## 80 1.2573 nan 0.0010 0.0002
## 100 1.2490 nan 0.0010 0.0001
## 120 1.2411 nan 0.0010 0.0001
## 140 1.2334 nan 0.0010 0.0002
## 160 1.2255 nan 0.0010 0.0002
## 180 1.2181 nan 0.0010 0.0002
## 200 1.2110 nan 0.0010 0.0002
## 220 1.2039 nan 0.0010 0.0002
## 240 1.1971 nan 0.0010 0.0001
## 260 1.1905 nan 0.0010 0.0002
## 280 1.1841 nan 0.0010 0.0001
## 300 1.1776 nan 0.0010 0.0001
## 320 1.1715 nan 0.0010 0.0001
## 340 1.1658 nan 0.0010 0.0001
## 360 1.1602 nan 0.0010 0.0001
## 380 1.1547 nan 0.0010 0.0001
## 400 1.1492 nan 0.0010 0.0001
## 420 1.1439 nan 0.0010 0.0001
## 440 1.1386 nan 0.0010 0.0001
## 460 1.1336 nan 0.0010 0.0001
## 480 1.1286 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0003
## 4 1.2911 nan 0.0010 0.0002
## 5 1.2905 nan 0.0010 0.0003
## 6 1.2899 nan 0.0010 0.0002
## 7 1.2893 nan 0.0010 0.0002
## 8 1.2887 nan 0.0010 0.0002
## 9 1.2882 nan 0.0010 0.0002
## 10 1.2876 nan 0.0010 0.0003
## 20 1.2822 nan 0.0010 0.0002
## 40 1.2716 nan 0.0010 0.0002
## 60 1.2612 nan 0.0010 0.0002
## 80 1.2512 nan 0.0010 0.0002
## 100 1.2412 nan 0.0010 0.0002
## 120 1.2320 nan 0.0010 0.0002
## 140 1.2227 nan 0.0010 0.0002
## 160 1.2137 nan 0.0010 0.0002
## 180 1.2054 nan 0.0010 0.0002
## 200 1.1971 nan 0.0010 0.0002
## 220 1.1889 nan 0.0010 0.0002
## 240 1.1812 nan 0.0010 0.0002
## 260 1.1736 nan 0.0010 0.0002
## 280 1.1662 nan 0.0010 0.0002
## 300 1.1589 nan 0.0010 0.0001
## 320 1.1519 nan 0.0010 0.0002
## 340 1.1448 nan 0.0010 0.0002
## 360 1.1381 nan 0.0010 0.0001
## 380 1.1316 nan 0.0010 0.0001
## 400 1.1253 nan 0.0010 0.0001
## 420 1.1191 nan 0.0010 0.0001
## 440 1.1133 nan 0.0010 0.0001
## 460 1.1075 nan 0.0010 0.0001
## 480 1.1019 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2900 nan 0.0010 0.0002
## 7 1.2894 nan 0.0010 0.0003
## 8 1.2889 nan 0.0010 0.0002
## 9 1.2883 nan 0.0010 0.0003
## 10 1.2878 nan 0.0010 0.0003
## 20 1.2824 nan 0.0010 0.0002
## 40 1.2717 nan 0.0010 0.0002
## 60 1.2613 nan 0.0010 0.0002
## 80 1.2514 nan 0.0010 0.0002
## 100 1.2416 nan 0.0010 0.0002
## 120 1.2323 nan 0.0010 0.0002
## 140 1.2231 nan 0.0010 0.0002
## 160 1.2145 nan 0.0010 0.0002
## 180 1.2059 nan 0.0010 0.0002
## 200 1.1976 nan 0.0010 0.0002
## 220 1.1894 nan 0.0010 0.0002
## 240 1.1815 nan 0.0010 0.0001
## 260 1.1740 nan 0.0010 0.0001
## 280 1.1668 nan 0.0010 0.0002
## 300 1.1596 nan 0.0010 0.0001
## 320 1.1528 nan 0.0010 0.0001
## 340 1.1461 nan 0.0010 0.0001
## 360 1.1395 nan 0.0010 0.0001
## 380 1.1330 nan 0.0010 0.0001
## 400 1.1265 nan 0.0010 0.0001
## 420 1.1205 nan 0.0010 0.0001
## 440 1.1142 nan 0.0010 0.0001
## 460 1.1085 nan 0.0010 0.0001
## 480 1.1028 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0003
## 3 1.2917 nan 0.0010 0.0003
## 4 1.2911 nan 0.0010 0.0003
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2900 nan 0.0010 0.0002
## 7 1.2894 nan 0.0010 0.0002
## 8 1.2889 nan 0.0010 0.0002
## 9 1.2884 nan 0.0010 0.0003
## 10 1.2879 nan 0.0010 0.0002
## 20 1.2824 nan 0.0010 0.0002
## 40 1.2719 nan 0.0010 0.0002
## 60 1.2616 nan 0.0010 0.0002
## 80 1.2518 nan 0.0010 0.0002
## 100 1.2421 nan 0.0010 0.0002
## 120 1.2327 nan 0.0010 0.0002
## 140 1.2238 nan 0.0010 0.0002
## 160 1.2150 nan 0.0010 0.0002
## 180 1.2065 nan 0.0010 0.0002
## 200 1.1981 nan 0.0010 0.0002
## 220 1.1901 nan 0.0010 0.0002
## 240 1.1823 nan 0.0010 0.0002
## 260 1.1744 nan 0.0010 0.0002
## 280 1.1670 nan 0.0010 0.0001
## 300 1.1597 nan 0.0010 0.0002
## 320 1.1526 nan 0.0010 0.0001
## 340 1.1457 nan 0.0010 0.0002
## 360 1.1391 nan 0.0010 0.0001
## 380 1.1326 nan 0.0010 0.0001
## 400 1.1268 nan 0.0010 0.0001
## 420 1.1205 nan 0.0010 0.0001
## 440 1.1145 nan 0.0010 0.0001
## 460 1.1088 nan 0.0010 0.0001
## 480 1.1033 nan 0.0010 0.0001
## 500 1.0981 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2557 nan 0.1000 0.0163
## 2 1.2347 nan 0.1000 0.0085
## 3 1.2076 nan 0.1000 0.0121
## 4 1.1818 nan 0.1000 0.0107
## 5 1.1630 nan 0.1000 0.0069
## 6 1.1431 nan 0.1000 0.0076
## 7 1.1249 nan 0.1000 0.0088
## 8 1.1112 nan 0.1000 0.0068
## 9 1.0979 nan 0.1000 0.0038
## 10 1.0888 nan 0.1000 0.0029
## 20 0.9983 nan 0.1000 0.0009
## 40 0.9157 nan 0.1000 0.0010
## 60 0.8750 nan 0.1000 -0.0002
## 80 0.8495 nan 0.1000 -0.0011
## 100 0.8301 nan 0.1000 -0.0004
## 120 0.8150 nan 0.1000 -0.0003
## 140 0.8026 nan 0.1000 -0.0006
## 160 0.7952 nan 0.1000 -0.0014
## 180 0.7839 nan 0.1000 -0.0008
## 200 0.7759 nan 0.1000 -0.0010
## 220 0.7669 nan 0.1000 -0.0010
## 240 0.7617 nan 0.1000 -0.0005
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## 280 0.7497 nan 0.1000 -0.0011
## 300 0.7432 nan 0.1000 -0.0009
## 320 0.7362 nan 0.1000 -0.0008
## 340 0.7299 nan 0.1000 -0.0004
## 360 0.7244 nan 0.1000 -0.0007
## 380 0.7205 nan 0.1000 -0.0011
## 400 0.7163 nan 0.1000 -0.0007
## 420 0.7093 nan 0.1000 -0.0006
## 440 0.7048 nan 0.1000 -0.0010
## 460 0.7007 nan 0.1000 -0.0002
## 480 0.6951 nan 0.1000 -0.0007
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2602 nan 0.1000 0.0158
## 2 1.2294 nan 0.1000 0.0136
## 3 1.2051 nan 0.1000 0.0099
## 4 1.1833 nan 0.1000 0.0104
## 5 1.1643 nan 0.1000 0.0086
## 6 1.1508 nan 0.1000 0.0068
## 7 1.1374 nan 0.1000 0.0052
## 8 1.1242 nan 0.1000 0.0047
## 9 1.1123 nan 0.1000 0.0042
## 10 1.0966 nan 0.1000 0.0072
## 20 1.0007 nan 0.1000 0.0036
## 40 0.9138 nan 0.1000 0.0007
## 60 0.8723 nan 0.1000 -0.0007
## 80 0.8461 nan 0.1000 0.0004
## 100 0.8270 nan 0.1000 -0.0007
## 120 0.8125 nan 0.1000 -0.0010
## 140 0.8015 nan 0.1000 -0.0006
## 160 0.7896 nan 0.1000 -0.0006
## 180 0.7804 nan 0.1000 -0.0009
## 200 0.7687 nan 0.1000 -0.0009
## 220 0.7616 nan 0.1000 -0.0005
## 240 0.7553 nan 0.1000 -0.0010
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## 280 0.7433 nan 0.1000 -0.0007
## 300 0.7375 nan 0.1000 -0.0011
## 320 0.7318 nan 0.1000 -0.0011
## 340 0.7282 nan 0.1000 -0.0018
## 360 0.7203 nan 0.1000 -0.0009
## 380 0.7149 nan 0.1000 -0.0009
## 400 0.7097 nan 0.1000 -0.0003
## 420 0.7049 nan 0.1000 -0.0007
## 440 0.7004 nan 0.1000 -0.0013
## 460 0.6973 nan 0.1000 -0.0009
## 480 0.6909 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2569 nan 0.1000 0.0158
## 2 1.2274 nan 0.1000 0.0164
## 3 1.2002 nan 0.1000 0.0115
## 4 1.1799 nan 0.1000 0.0089
## 5 1.1621 nan 0.1000 0.0072
## 6 1.1452 nan 0.1000 0.0067
## 7 1.1279 nan 0.1000 0.0074
## 8 1.1137 nan 0.1000 0.0068
## 9 1.0985 nan 0.1000 0.0048
## 10 1.0832 nan 0.1000 0.0053
## 20 0.9942 nan 0.1000 0.0026
## 40 0.9133 nan 0.1000 0.0004
## 60 0.8785 nan 0.1000 -0.0008
## 80 0.8521 nan 0.1000 -0.0003
## 100 0.8319 nan 0.1000 -0.0016
## 120 0.8185 nan 0.1000 -0.0012
## 140 0.8048 nan 0.1000 -0.0001
## 160 0.7940 nan 0.1000 -0.0010
## 180 0.7864 nan 0.1000 -0.0012
## 200 0.7759 nan 0.1000 -0.0004
## 220 0.7702 nan 0.1000 -0.0014
## 240 0.7642 nan 0.1000 -0.0008
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## 280 0.7520 nan 0.1000 -0.0009
## 300 0.7471 nan 0.1000 -0.0013
## 320 0.7400 nan 0.1000 -0.0005
## 340 0.7342 nan 0.1000 -0.0003
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## 380 0.7239 nan 0.1000 -0.0013
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## 460 0.7018 nan 0.1000 -0.0008
## 480 0.6992 nan 0.1000 -0.0015
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2490 nan 0.1000 0.0228
## 2 1.2165 nan 0.1000 0.0158
## 3 1.1807 nan 0.1000 0.0140
## 4 1.1507 nan 0.1000 0.0139
## 5 1.1214 nan 0.1000 0.0127
## 6 1.0964 nan 0.1000 0.0104
## 7 1.0753 nan 0.1000 0.0096
## 8 1.0570 nan 0.1000 0.0082
## 9 1.0404 nan 0.1000 0.0069
## 10 1.0262 nan 0.1000 0.0059
## 20 0.9261 nan 0.1000 0.0005
## 40 0.8415 nan 0.1000 -0.0015
## 60 0.7930 nan 0.1000 -0.0010
## 80 0.7591 nan 0.1000 -0.0011
## 100 0.7356 nan 0.1000 -0.0016
## 120 0.7090 nan 0.1000 -0.0010
## 140 0.6899 nan 0.1000 -0.0007
## 160 0.6657 nan 0.1000 -0.0018
## 180 0.6454 nan 0.1000 -0.0007
## 200 0.6267 nan 0.1000 -0.0011
## 220 0.6102 nan 0.1000 -0.0026
## 240 0.5909 nan 0.1000 -0.0006
## 260 0.5715 nan 0.1000 -0.0016
## 280 0.5509 nan 0.1000 -0.0020
## 300 0.5338 nan 0.1000 -0.0012
## 320 0.5202 nan 0.1000 -0.0011
## 340 0.5035 nan 0.1000 -0.0007
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## 380 0.4789 nan 0.1000 -0.0008
## 400 0.4669 nan 0.1000 -0.0005
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## 460 0.4336 nan 0.1000 -0.0013
## 480 0.4229 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2464 nan 0.1000 0.0212
## 2 1.2143 nan 0.1000 0.0148
## 3 1.1797 nan 0.1000 0.0151
## 4 1.1505 nan 0.1000 0.0114
## 5 1.1230 nan 0.1000 0.0112
## 6 1.0990 nan 0.1000 0.0078
## 7 1.0795 nan 0.1000 0.0078
## 8 1.0573 nan 0.1000 0.0101
## 9 1.0402 nan 0.1000 0.0052
## 10 1.0276 nan 0.1000 0.0048
## 20 0.9266 nan 0.1000 0.0011
## 40 0.8371 nan 0.1000 -0.0010
## 60 0.7866 nan 0.1000 -0.0002
## 80 0.7510 nan 0.1000 -0.0008
## 100 0.7204 nan 0.1000 -0.0022
## 120 0.6957 nan 0.1000 -0.0009
## 140 0.6750 nan 0.1000 -0.0016
## 160 0.6518 nan 0.1000 -0.0013
## 180 0.6314 nan 0.1000 -0.0006
## 200 0.6145 nan 0.1000 -0.0015
## 220 0.5951 nan 0.1000 -0.0010
## 240 0.5743 nan 0.1000 -0.0009
## 260 0.5593 nan 0.1000 -0.0016
## 280 0.5438 nan 0.1000 -0.0004
## 300 0.5297 nan 0.1000 -0.0010
## 320 0.5146 nan 0.1000 -0.0007
## 340 0.5030 nan 0.1000 -0.0008
## 360 0.4923 nan 0.1000 -0.0011
## 380 0.4828 nan 0.1000 -0.0010
## 400 0.4709 nan 0.1000 -0.0013
## 420 0.4585 nan 0.1000 -0.0009
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## 460 0.4369 nan 0.1000 -0.0007
## 480 0.4274 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2481 nan 0.1000 0.0196
## 2 1.2097 nan 0.1000 0.0186
## 3 1.1804 nan 0.1000 0.0122
## 4 1.1550 nan 0.1000 0.0124
## 5 1.1280 nan 0.1000 0.0113
## 6 1.1089 nan 0.1000 0.0094
## 7 1.0853 nan 0.1000 0.0089
## 8 1.0650 nan 0.1000 0.0058
## 9 1.0457 nan 0.1000 0.0057
## 10 1.0312 nan 0.1000 0.0054
## 20 0.9328 nan 0.1000 0.0019
## 40 0.8419 nan 0.1000 -0.0010
## 60 0.7884 nan 0.1000 -0.0019
## 80 0.7601 nan 0.1000 -0.0024
## 100 0.7290 nan 0.1000 -0.0005
## 120 0.7059 nan 0.1000 -0.0017
## 140 0.6824 nan 0.1000 -0.0010
## 160 0.6643 nan 0.1000 -0.0007
## 180 0.6427 nan 0.1000 -0.0009
## 200 0.6245 nan 0.1000 -0.0013
## 220 0.6105 nan 0.1000 -0.0009
## 240 0.5943 nan 0.1000 -0.0024
## 260 0.5791 nan 0.1000 -0.0016
## 280 0.5635 nan 0.1000 -0.0009
## 300 0.5467 nan 0.1000 -0.0001
## 320 0.5316 nan 0.1000 -0.0008
## 340 0.5170 nan 0.1000 -0.0015
## 360 0.5037 nan 0.1000 -0.0012
## 380 0.4914 nan 0.1000 -0.0014
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2331 nan 0.1000 0.0275
## 2 1.1828 nan 0.1000 0.0224
## 3 1.1496 nan 0.1000 0.0144
## 4 1.1115 nan 0.1000 0.0172
## 5 1.0802 nan 0.1000 0.0128
## 6 1.0571 nan 0.1000 0.0100
## 7 1.0335 nan 0.1000 0.0083
## 8 1.0078 nan 0.1000 0.0083
## 9 0.9922 nan 0.1000 0.0054
## 10 0.9762 nan 0.1000 0.0056
## 20 0.8713 nan 0.1000 0.0012
## 40 0.7775 nan 0.1000 0.0000
## 60 0.7195 nan 0.1000 -0.0014
## 80 0.6730 nan 0.1000 -0.0011
## 100 0.6277 nan 0.1000 -0.0015
## 120 0.5931 nan 0.1000 -0.0010
## 140 0.5594 nan 0.1000 -0.0013
## 160 0.5333 nan 0.1000 -0.0027
## 180 0.5030 nan 0.1000 -0.0009
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## 220 0.4566 nan 0.1000 -0.0016
## 240 0.4347 nan 0.1000 -0.0010
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## 280 0.3991 nan 0.1000 -0.0007
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## 340 0.3499 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2419 nan 0.1000 0.0245
## 2 1.1925 nan 0.1000 0.0187
## 3 1.1476 nan 0.1000 0.0106
## 4 1.1174 nan 0.1000 0.0122
## 5 1.0886 nan 0.1000 0.0119
## 6 1.0658 nan 0.1000 0.0085
## 7 1.0440 nan 0.1000 0.0063
## 8 1.0225 nan 0.1000 0.0063
## 9 1.0018 nan 0.1000 0.0091
## 10 0.9882 nan 0.1000 0.0052
## 20 0.8804 nan 0.1000 0.0015
## 40 0.7863 nan 0.1000 0.0003
## 60 0.7274 nan 0.1000 -0.0002
## 80 0.6860 nan 0.1000 -0.0026
## 100 0.6526 nan 0.1000 -0.0011
## 120 0.6150 nan 0.1000 -0.0008
## 140 0.5810 nan 0.1000 -0.0006
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## 180 0.5262 nan 0.1000 -0.0010
## 200 0.5032 nan 0.1000 0.0001
## 220 0.4812 nan 0.1000 -0.0007
## 240 0.4586 nan 0.1000 -0.0010
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## 280 0.4256 nan 0.1000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2417 nan 0.1000 0.0251
## 2 1.2023 nan 0.1000 0.0182
## 3 1.1648 nan 0.1000 0.0169
## 4 1.1328 nan 0.1000 0.0130
## 5 1.0954 nan 0.1000 0.0164
## 6 1.0705 nan 0.1000 0.0074
## 7 1.0468 nan 0.1000 0.0088
## 8 1.0306 nan 0.1000 0.0043
## 9 1.0113 nan 0.1000 0.0058
## 10 0.9958 nan 0.1000 0.0051
## 20 0.8838 nan 0.1000 0.0004
## 40 0.7849 nan 0.1000 -0.0017
## 60 0.7251 nan 0.1000 -0.0001
## 80 0.6814 nan 0.1000 -0.0008
## 100 0.6436 nan 0.1000 -0.0012
## 120 0.6102 nan 0.1000 -0.0000
## 140 0.5765 nan 0.1000 -0.0001
## 160 0.5481 nan 0.1000 0.0002
## 180 0.5223 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2220 nan 0.2000 0.0297
## 2 1.1809 nan 0.2000 0.0167
## 3 1.1455 nan 0.2000 0.0175
## 4 1.1121 nan 0.2000 0.0106
## 5 1.0868 nan 0.2000 0.0102
## 6 1.0595 nan 0.2000 0.0114
## 7 1.0412 nan 0.2000 0.0080
## 8 1.0235 nan 0.2000 0.0060
## 9 1.0073 nan 0.2000 0.0066
## 10 0.9922 nan 0.2000 0.0051
## 20 0.9167 nan 0.2000 -0.0003
## 40 0.8442 nan 0.2000 0.0006
## 60 0.8136 nan 0.2000 -0.0012
## 80 0.7883 nan 0.2000 -0.0010
## 100 0.7670 nan 0.2000 -0.0015
## 120 0.7518 nan 0.2000 -0.0006
## 140 0.7401 nan 0.2000 -0.0020
## 160 0.7258 nan 0.2000 -0.0013
## 180 0.7179 nan 0.2000 -0.0020
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## 220 0.7023 nan 0.2000 -0.0021
## 240 0.6926 nan 0.2000 -0.0025
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## 320 0.6607 nan 0.2000 -0.0036
## 340 0.6550 nan 0.2000 -0.0024
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## 420 0.6268 nan 0.2000 -0.0037
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## 460 0.6149 nan 0.2000 -0.0029
## 480 0.6089 nan 0.2000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2255 nan 0.2000 0.0223
## 2 1.1793 nan 0.2000 0.0223
## 3 1.1486 nan 0.2000 0.0146
## 4 1.1188 nan 0.2000 0.0120
## 5 1.0877 nan 0.2000 0.0129
## 6 1.0646 nan 0.2000 0.0095
## 7 1.0443 nan 0.2000 0.0080
## 8 1.0251 nan 0.2000 0.0047
## 9 1.0066 nan 0.2000 0.0054
## 10 0.9933 nan 0.2000 0.0052
## 20 0.9084 nan 0.2000 0.0030
## 40 0.8528 nan 0.2000 -0.0027
## 60 0.8206 nan 0.2000 -0.0024
## 80 0.7969 nan 0.2000 -0.0002
## 100 0.7780 nan 0.2000 -0.0009
## 120 0.7662 nan 0.2000 -0.0015
## 140 0.7559 nan 0.2000 -0.0025
## 160 0.7446 nan 0.2000 -0.0006
## 180 0.7334 nan 0.2000 -0.0009
## 200 0.7197 nan 0.2000 -0.0026
## 220 0.7084 nan 0.2000 -0.0019
## 240 0.6943 nan 0.2000 -0.0012
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## 280 0.6796 nan 0.2000 -0.0020
## 300 0.6729 nan 0.2000 -0.0041
## 320 0.6669 nan 0.2000 -0.0013
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## 360 0.6494 nan 0.2000 -0.0023
## 380 0.6390 nan 0.2000 -0.0012
## 400 0.6335 nan 0.2000 -0.0022
## 420 0.6252 nan 0.2000 -0.0014
## 440 0.6206 nan 0.2000 -0.0013
## 460 0.6147 nan 0.2000 -0.0027
## 480 0.6100 nan 0.2000 -0.0041
## 500 0.6028 nan 0.2000 -0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2266 nan 0.2000 0.0301
## 2 1.1832 nan 0.2000 0.0192
## 3 1.1504 nan 0.2000 0.0131
## 4 1.1193 nan 0.2000 0.0127
## 5 1.0897 nan 0.2000 0.0136
## 6 1.0640 nan 0.2000 0.0134
## 7 1.0424 nan 0.2000 0.0077
## 8 1.0242 nan 0.2000 0.0062
## 9 1.0101 nan 0.2000 0.0026
## 10 0.9961 nan 0.2000 0.0051
## 20 0.9218 nan 0.2000 0.0017
## 40 0.8427 nan 0.2000 -0.0014
## 60 0.8094 nan 0.2000 -0.0015
## 80 0.7889 nan 0.2000 -0.0019
## 100 0.7738 nan 0.2000 -0.0007
## 120 0.7579 nan 0.2000 -0.0017
## 140 0.7473 nan 0.2000 -0.0003
## 160 0.7379 nan 0.2000 -0.0030
## 180 0.7307 nan 0.2000 -0.0013
## 200 0.7219 nan 0.2000 -0.0006
## 220 0.7140 nan 0.2000 -0.0023
## 240 0.7012 nan 0.2000 -0.0017
## 260 0.6925 nan 0.2000 -0.0027
## 280 0.6866 nan 0.2000 -0.0009
## 300 0.6764 nan 0.2000 -0.0034
## 320 0.6655 nan 0.2000 -0.0015
## 340 0.6571 nan 0.2000 -0.0015
## 360 0.6494 nan 0.2000 -0.0034
## 380 0.6408 nan 0.2000 -0.0018
## 400 0.6352 nan 0.2000 -0.0021
## 420 0.6290 nan 0.2000 -0.0005
## 440 0.6223 nan 0.2000 -0.0014
## 460 0.6147 nan 0.2000 -0.0014
## 480 0.6117 nan 0.2000 -0.0011
## 500 0.6079 nan 0.2000 -0.0023
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2102 nan 0.2000 0.0343
## 2 1.1474 nan 0.2000 0.0269
## 3 1.0991 nan 0.2000 0.0215
## 4 1.0634 nan 0.2000 0.0162
## 5 1.0342 nan 0.2000 0.0071
## 6 1.0066 nan 0.2000 0.0092
## 7 0.9836 nan 0.2000 0.0089
## 8 0.9733 nan 0.2000 -0.0002
## 9 0.9584 nan 0.2000 0.0027
## 10 0.9379 nan 0.2000 0.0027
## 20 0.8561 nan 0.2000 0.0005
## 40 0.7679 nan 0.2000 -0.0040
## 60 0.7129 nan 0.2000 -0.0017
## 80 0.6581 nan 0.2000 -0.0013
## 100 0.6208 nan 0.2000 -0.0014
## 120 0.5909 nan 0.2000 -0.0030
## 140 0.5546 nan 0.2000 -0.0016
## 160 0.5200 nan 0.2000 -0.0027
## 180 0.4907 nan 0.2000 -0.0028
## 200 0.4633 nan 0.2000 -0.0026
## 220 0.4428 nan 0.2000 -0.0032
## 240 0.4275 nan 0.2000 -0.0011
## 260 0.4119 nan 0.2000 -0.0023
## 280 0.3931 nan 0.2000 -0.0019
## 300 0.3756 nan 0.2000 -0.0009
## 320 0.3623 nan 0.2000 -0.0002
## 340 0.3466 nan 0.2000 -0.0019
## 360 0.3314 nan 0.2000 -0.0016
## 380 0.3166 nan 0.2000 -0.0004
## 400 0.3033 nan 0.2000 -0.0018
## 420 0.2886 nan 0.2000 -0.0011
## 440 0.2797 nan 0.2000 -0.0008
## 460 0.2661 nan 0.2000 -0.0009
## 480 0.2587 nan 0.2000 -0.0017
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2070 nan 0.2000 0.0379
## 2 1.1516 nan 0.2000 0.0248
## 3 1.1092 nan 0.2000 0.0187
## 4 1.0700 nan 0.2000 0.0160
## 5 1.0316 nan 0.2000 0.0150
## 6 1.0083 nan 0.2000 0.0056
## 7 0.9882 nan 0.2000 0.0076
## 8 0.9665 nan 0.2000 0.0079
## 9 0.9508 nan 0.2000 0.0034
## 10 0.9364 nan 0.2000 0.0017
## 20 0.8534 nan 0.2000 -0.0013
## 40 0.7650 nan 0.2000 -0.0038
## 60 0.7149 nan 0.2000 -0.0023
## 80 0.6748 nan 0.2000 -0.0014
## 100 0.6312 nan 0.2000 -0.0019
## 120 0.5915 nan 0.2000 -0.0021
## 140 0.5672 nan 0.2000 -0.0023
## 160 0.5278 nan 0.2000 -0.0011
## 180 0.4988 nan 0.2000 -0.0010
## 200 0.4740 nan 0.2000 -0.0018
## 220 0.4553 nan 0.2000 -0.0028
## 240 0.4299 nan 0.2000 -0.0005
## 260 0.4148 nan 0.2000 -0.0017
## 280 0.4007 nan 0.2000 -0.0010
## 300 0.3832 nan 0.2000 -0.0002
## 320 0.3706 nan 0.2000 -0.0025
## 340 0.3509 nan 0.2000 -0.0014
## 360 0.3358 nan 0.2000 -0.0022
## 380 0.3234 nan 0.2000 -0.0010
## 400 0.3102 nan 0.2000 -0.0018
## 420 0.2992 nan 0.2000 -0.0027
## 440 0.2890 nan 0.2000 -0.0019
## 460 0.2798 nan 0.2000 -0.0014
## 480 0.2693 nan 0.2000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2118 nan 0.2000 0.0346
## 2 1.1434 nan 0.2000 0.0280
## 3 1.0924 nan 0.2000 0.0234
## 4 1.0475 nan 0.2000 0.0144
## 5 1.0187 nan 0.2000 0.0106
## 6 0.9963 nan 0.2000 0.0072
## 7 0.9709 nan 0.2000 0.0098
## 8 0.9557 nan 0.2000 0.0052
## 9 0.9409 nan 0.2000 0.0041
## 10 0.9263 nan 0.2000 0.0043
## 20 0.8511 nan 0.2000 -0.0027
## 40 0.7763 nan 0.2000 -0.0047
## 60 0.7139 nan 0.2000 -0.0028
## 80 0.6663 nan 0.2000 -0.0017
## 100 0.6277 nan 0.2000 -0.0030
## 120 0.5975 nan 0.2000 -0.0007
## 140 0.5685 nan 0.2000 -0.0004
## 160 0.5383 nan 0.2000 -0.0016
## 180 0.5133 nan 0.2000 -0.0008
## 200 0.4843 nan 0.2000 -0.0017
## 220 0.4676 nan 0.2000 -0.0016
## 240 0.4442 nan 0.2000 -0.0035
## 260 0.4249 nan 0.2000 -0.0020
## 280 0.4041 nan 0.2000 -0.0024
## 300 0.3863 nan 0.2000 -0.0029
## 320 0.3663 nan 0.2000 -0.0013
## 340 0.3506 nan 0.2000 -0.0018
## 360 0.3390 nan 0.2000 -0.0013
## 380 0.3271 nan 0.2000 -0.0016
## 400 0.3100 nan 0.2000 -0.0002
## 420 0.3013 nan 0.2000 -0.0005
## 440 0.2905 nan 0.2000 -0.0031
## 460 0.2779 nan 0.2000 -0.0014
## 480 0.2688 nan 0.2000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1908 nan 0.2000 0.0427
## 2 1.1187 nan 0.2000 0.0328
## 3 1.0650 nan 0.2000 0.0214
## 4 1.0267 nan 0.2000 0.0093
## 5 0.9934 nan 0.2000 0.0112
## 6 0.9601 nan 0.2000 0.0117
## 7 0.9345 nan 0.2000 0.0101
## 8 0.9118 nan 0.2000 0.0051
## 9 0.8879 nan 0.2000 0.0042
## 10 0.8754 nan 0.2000 0.0005
## 20 0.7870 nan 0.2000 -0.0004
## 40 0.6949 nan 0.2000 -0.0029
## 60 0.6296 nan 0.2000 -0.0008
## 80 0.5605 nan 0.2000 -0.0070
## 100 0.5096 nan 0.2000 -0.0014
## 120 0.4610 nan 0.2000 -0.0023
## 140 0.4165 nan 0.2000 -0.0005
## 160 0.3861 nan 0.2000 -0.0020
## 180 0.3549 nan 0.2000 -0.0016
## 200 0.3326 nan 0.2000 -0.0012
## 220 0.3104 nan 0.2000 -0.0007
## 240 0.2874 nan 0.2000 -0.0024
## 260 0.2687 nan 0.2000 -0.0009
## 280 0.2468 nan 0.2000 -0.0001
## 300 0.2314 nan 0.2000 -0.0011
## 320 0.2187 nan 0.2000 -0.0013
## 340 0.2031 nan 0.2000 -0.0009
## 360 0.1881 nan 0.2000 -0.0008
## 380 0.1739 nan 0.2000 -0.0001
## 400 0.1649 nan 0.2000 -0.0010
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## 460 0.1402 nan 0.2000 -0.0006
## 480 0.1312 nan 0.2000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1900 nan 0.2000 0.0409
## 2 1.1268 nan 0.2000 0.0312
## 3 1.0683 nan 0.2000 0.0216
## 4 1.0232 nan 0.2000 0.0134
## 5 1.0006 nan 0.2000 0.0031
## 6 0.9780 nan 0.2000 0.0032
## 7 0.9515 nan 0.2000 0.0063
## 8 0.9309 nan 0.2000 0.0067
## 9 0.9120 nan 0.2000 0.0025
## 10 0.8962 nan 0.2000 0.0042
## 20 0.7978 nan 0.2000 -0.0008
## 40 0.6999 nan 0.2000 -0.0004
## 60 0.6289 nan 0.2000 -0.0044
## 80 0.5685 nan 0.2000 -0.0049
## 100 0.5150 nan 0.2000 -0.0042
## 120 0.4674 nan 0.2000 -0.0015
## 140 0.4313 nan 0.2000 -0.0031
## 160 0.3910 nan 0.2000 -0.0000
## 180 0.3573 nan 0.2000 -0.0008
## 200 0.3318 nan 0.2000 -0.0024
## 220 0.3028 nan 0.2000 -0.0018
## 240 0.2811 nan 0.2000 -0.0014
## 260 0.2596 nan 0.2000 -0.0014
## 280 0.2380 nan 0.2000 -0.0021
## 300 0.2199 nan 0.2000 -0.0016
## 320 0.2029 nan 0.2000 -0.0005
## 340 0.1895 nan 0.2000 -0.0010
## 360 0.1785 nan 0.2000 -0.0014
## 380 0.1692 nan 0.2000 -0.0013
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## 460 0.1337 nan 0.2000 -0.0008
## 480 0.1254 nan 0.2000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1920 nan 0.2000 0.0418
## 2 1.1180 nan 0.2000 0.0225
## 3 1.0644 nan 0.2000 0.0223
## 4 1.0120 nan 0.2000 0.0219
## 5 0.9819 nan 0.2000 0.0075
## 6 0.9536 nan 0.2000 0.0104
## 7 0.9335 nan 0.2000 0.0045
## 8 0.9132 nan 0.2000 0.0001
## 9 0.8984 nan 0.2000 0.0012
## 10 0.8884 nan 0.2000 0.0001
## 20 0.8031 nan 0.2000 0.0004
## 40 0.6966 nan 0.2000 -0.0014
## 60 0.6275 nan 0.2000 -0.0014
## 80 0.5695 nan 0.2000 -0.0021
## 100 0.5205 nan 0.2000 -0.0020
## 120 0.4803 nan 0.2000 -0.0037
## 140 0.4377 nan 0.2000 -0.0023
## 160 0.4066 nan 0.2000 -0.0011
## 180 0.3746 nan 0.2000 -0.0018
## 200 0.3441 nan 0.2000 -0.0006
## 220 0.3166 nan 0.2000 -0.0018
## 240 0.2851 nan 0.2000 -0.0002
## 260 0.2642 nan 0.2000 -0.0013
## 280 0.2455 nan 0.2000 -0.0006
## 300 0.2281 nan 0.2000 -0.0010
## 320 0.2107 nan 0.2000 -0.0018
## 340 0.1967 nan 0.2000 -0.0007
## 360 0.1835 nan 0.2000 -0.0005
## 380 0.1710 nan 0.2000 -0.0017
## 400 0.1605 nan 0.2000 -0.0005
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## 460 0.1309 nan 0.2000 -0.0006
## 480 0.1231 nan 0.2000 -0.0014
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2068 nan 0.3000 0.0403
## 2 1.1444 nan 0.3000 0.0309
## 3 1.1035 nan 0.3000 0.0120
## 4 1.0672 nan 0.3000 0.0128
## 5 1.0325 nan 0.3000 0.0158
## 6 1.0088 nan 0.3000 0.0097
## 7 0.9861 nan 0.3000 0.0095
## 8 0.9720 nan 0.3000 0.0051
## 9 0.9554 nan 0.3000 0.0070
## 10 0.9441 nan 0.3000 0.0049
## 20 0.8742 nan 0.3000 -0.0033
## 40 0.8143 nan 0.3000 -0.0062
## 60 0.7823 nan 0.3000 -0.0028
## 80 0.7689 nan 0.3000 -0.0029
## 100 0.7522 nan 0.3000 -0.0037
## 120 0.7344 nan 0.3000 -0.0031
## 140 0.7177 nan 0.3000 -0.0046
## 160 0.7040 nan 0.3000 -0.0031
## 180 0.6916 nan 0.3000 -0.0022
## 200 0.6739 nan 0.3000 -0.0015
## 220 0.6641 nan 0.3000 -0.0041
## 240 0.6532 nan 0.3000 0.0000
## 260 0.6451 nan 0.3000 -0.0030
## 280 0.6310 nan 0.3000 -0.0007
## 300 0.6250 nan 0.3000 -0.0066
## 320 0.6104 nan 0.3000 -0.0025
## 340 0.6059 nan 0.3000 -0.0034
## 360 0.5933 nan 0.3000 -0.0024
## 380 0.5846 nan 0.3000 -0.0017
## 400 0.5781 nan 0.3000 -0.0034
## 420 0.5738 nan 0.3000 -0.0026
## 440 0.5640 nan 0.3000 -0.0030
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2058 nan 0.3000 0.0397
## 2 1.1563 nan 0.3000 0.0173
## 3 1.1047 nan 0.3000 0.0246
## 4 1.0678 nan 0.3000 0.0177
## 5 1.0338 nan 0.3000 0.0127
## 6 1.0119 nan 0.3000 0.0068
## 7 0.9905 nan 0.3000 0.0043
## 8 0.9741 nan 0.3000 0.0052
## 9 0.9602 nan 0.3000 0.0033
## 10 0.9446 nan 0.3000 0.0037
## 20 0.8796 nan 0.3000 0.0002
## 40 0.8215 nan 0.3000 -0.0025
## 60 0.7848 nan 0.3000 -0.0003
## 80 0.7577 nan 0.3000 0.0005
## 100 0.7353 nan 0.3000 -0.0029
## 120 0.7268 nan 0.3000 -0.0031
## 140 0.7156 nan 0.3000 -0.0051
## 160 0.7009 nan 0.3000 0.0004
## 180 0.6892 nan 0.3000 -0.0047
## 200 0.6739 nan 0.3000 -0.0033
## 220 0.6631 nan 0.3000 -0.0037
## 240 0.6510 nan 0.3000 -0.0021
## 260 0.6480 nan 0.3000 -0.0044
## 280 0.6334 nan 0.3000 -0.0045
## 300 0.6273 nan 0.3000 -0.0044
## 320 0.6187 nan 0.3000 -0.0050
## 340 0.6107 nan 0.3000 -0.0028
## 360 0.6008 nan 0.3000 -0.0013
## 380 0.5921 nan 0.3000 -0.0007
## 400 0.5873 nan 0.3000 -0.0030
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## 440 0.5762 nan 0.3000 -0.0011
## 460 0.5675 nan 0.3000 -0.0027
## 480 0.5610 nan 0.3000 -0.0028
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2010 nan 0.3000 0.0432
## 2 1.1399 nan 0.3000 0.0280
## 3 1.1089 nan 0.3000 0.0152
## 4 1.0723 nan 0.3000 0.0111
## 5 1.0399 nan 0.3000 0.0157
## 6 1.0223 nan 0.3000 0.0032
## 7 0.9976 nan 0.3000 0.0092
## 8 0.9854 nan 0.3000 -0.0001
## 9 0.9674 nan 0.3000 0.0074
## 10 0.9540 nan 0.3000 0.0034
## 20 0.8758 nan 0.3000 0.0006
## 40 0.8224 nan 0.3000 -0.0003
## 60 0.7960 nan 0.3000 -0.0024
## 80 0.7702 nan 0.3000 -0.0002
## 100 0.7478 nan 0.3000 -0.0014
## 120 0.7330 nan 0.3000 -0.0025
## 140 0.7175 nan 0.3000 -0.0018
## 160 0.7062 nan 0.3000 -0.0027
## 180 0.6938 nan 0.3000 -0.0045
## 200 0.6814 nan 0.3000 -0.0047
## 220 0.6709 nan 0.3000 -0.0061
## 240 0.6635 nan 0.3000 -0.0024
## 260 0.6542 nan 0.3000 -0.0037
## 280 0.6428 nan 0.3000 -0.0014
## 300 0.6342 nan 0.3000 -0.0016
## 320 0.6293 nan 0.3000 -0.0022
## 340 0.6209 nan 0.3000 -0.0005
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## 380 0.6010 nan 0.3000 -0.0060
## 400 0.5919 nan 0.3000 -0.0039
## 420 0.5865 nan 0.3000 -0.0036
## 440 0.5819 nan 0.3000 -0.0028
## 460 0.5741 nan 0.3000 -0.0027
## 480 0.5707 nan 0.3000 -0.0025
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1714 nan 0.3000 0.0518
## 2 1.1032 nan 0.3000 0.0255
## 3 1.0461 nan 0.3000 0.0224
## 4 1.0002 nan 0.3000 0.0163
## 5 0.9694 nan 0.3000 0.0018
## 6 0.9624 nan 0.3000 -0.0056
## 7 0.9458 nan 0.3000 -0.0026
## 8 0.9289 nan 0.3000 0.0040
## 9 0.9107 nan 0.3000 0.0049
## 10 0.8931 nan 0.3000 0.0054
## 20 0.8057 nan 0.3000 -0.0013
## 40 0.7316 nan 0.3000 -0.0043
## 60 0.6566 nan 0.3000 0.0004
## 80 0.6003 nan 0.3000 -0.0027
## 100 0.5565 nan 0.3000 -0.0048
## 120 0.5147 nan 0.3000 -0.0012
## 140 0.4695 nan 0.3000 -0.0026
## 160 0.4345 nan 0.3000 -0.0013
## 180 0.4055 nan 0.3000 -0.0018
## 200 0.3726 nan 0.3000 -0.0019
## 220 0.3484 nan 0.3000 -0.0028
## 240 0.3148 nan 0.3000 -0.0031
## 260 0.2941 nan 0.3000 -0.0012
## 280 0.2775 nan 0.3000 -0.0016
## 300 0.2592 nan 0.3000 -0.0012
## 320 0.2486 nan 0.3000 -0.0020
## 340 0.2346 nan 0.3000 -0.0019
## 360 0.2209 nan 0.3000 -0.0037
## 380 0.2069 nan 0.3000 -0.0021
## 400 0.1950 nan 0.3000 -0.0012
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## 480 0.1576 nan 0.3000 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1824 nan 0.3000 0.0530
## 2 1.0948 nan 0.3000 0.0376
## 3 1.0375 nan 0.3000 0.0232
## 4 0.9967 nan 0.3000 0.0148
## 5 0.9601 nan 0.3000 0.0102
## 6 0.9376 nan 0.3000 0.0067
## 7 0.9125 nan 0.3000 0.0040
## 8 0.8949 nan 0.3000 -0.0015
## 9 0.8810 nan 0.3000 0.0022
## 10 0.8737 nan 0.3000 -0.0018
## 20 0.7896 nan 0.3000 -0.0057
## 40 0.7024 nan 0.3000 -0.0057
## 60 0.6399 nan 0.3000 -0.0081
## 80 0.5926 nan 0.3000 -0.0028
## 100 0.5504 nan 0.3000 -0.0029
## 120 0.5160 nan 0.3000 -0.0041
## 140 0.4681 nan 0.3000 -0.0049
## 160 0.4369 nan 0.3000 -0.0017
## 180 0.4158 nan 0.3000 -0.0033
## 200 0.3878 nan 0.3000 -0.0051
## 220 0.3584 nan 0.3000 -0.0017
## 240 0.3362 nan 0.3000 -0.0001
## 260 0.3209 nan 0.3000 0.0004
## 280 0.3040 nan 0.3000 -0.0033
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## 320 0.2633 nan 0.3000 -0.0014
## 340 0.2462 nan 0.3000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1823 nan 0.3000 0.0442
## 2 1.1129 nan 0.3000 0.0285
## 3 1.0515 nan 0.3000 0.0243
## 4 1.0143 nan 0.3000 0.0032
## 5 0.9940 nan 0.3000 -0.0000
## 6 0.9710 nan 0.3000 0.0069
## 7 0.9441 nan 0.3000 0.0065
## 8 0.9361 nan 0.3000 -0.0075
## 9 0.9166 nan 0.3000 0.0057
## 10 0.9088 nan 0.3000 -0.0056
## 20 0.8252 nan 0.3000 -0.0079
## 40 0.7328 nan 0.3000 -0.0013
## 60 0.6713 nan 0.3000 -0.0028
## 80 0.6223 nan 0.3000 -0.0029
## 100 0.5728 nan 0.3000 -0.0030
## 120 0.5332 nan 0.3000 -0.0040
## 140 0.4945 nan 0.3000 -0.0023
## 160 0.4554 nan 0.3000 -0.0031
## 180 0.4174 nan 0.3000 -0.0014
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## 220 0.3609 nan 0.3000 -0.0014
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## 280 0.2995 nan 0.3000 -0.0019
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## 340 0.2515 nan 0.3000 0.0000
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## 380 0.2222 nan 0.3000 -0.0015
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## 460 0.1845 nan 0.3000 -0.0023
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1474 nan 0.3000 0.0740
## 2 1.0741 nan 0.3000 0.0186
## 3 1.0087 nan 0.3000 0.0233
## 4 0.9615 nan 0.3000 0.0142
## 5 0.9339 nan 0.3000 0.0026
## 6 0.8991 nan 0.3000 0.0108
## 7 0.8794 nan 0.3000 0.0033
## 8 0.8563 nan 0.3000 0.0035
## 9 0.8367 nan 0.3000 0.0013
## 10 0.8315 nan 0.3000 -0.0076
## 20 0.7451 nan 0.3000 -0.0053
## 40 0.6242 nan 0.3000 -0.0066
## 60 0.6067 nan 0.3000 -0.0065
## 80 inf nan 0.3000 nan
## 100 inf nan 0.3000 nan
## 120 inf nan 0.3000 nan
## 140 inf nan 0.3000 nan
## 160 inf nan 0.3000 nan
## 180 inf nan 0.3000 nan
## 200 inf nan 0.3000 nan
## 220 inf nan 0.3000 nan
## 240 inf nan 0.3000 nan
## 260 inf nan 0.3000 nan
## 280 inf nan 0.3000 nan
## 300 inf nan 0.3000 nan
## 320 inf nan 0.3000 nan
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## 380 inf nan 0.3000 nan
## 400 inf nan 0.3000 nan
## 420 inf nan 0.3000 nan
## 440 inf nan 0.3000 nan
## 460 inf nan 0.3000 nan
## 480 inf nan 0.3000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1562 nan 0.3000 0.0615
## 2 1.0486 nan 0.3000 0.0437
## 3 0.9957 nan 0.3000 0.0191
## 4 0.9591 nan 0.3000 0.0093
## 5 0.9265 nan 0.3000 0.0042
## 6 0.9007 nan 0.3000 0.0096
## 7 0.8819 nan 0.3000 0.0047
## 8 0.8684 nan 0.3000 -0.0037
## 9 0.8449 nan 0.3000 0.0034
## 10 0.8320 nan 0.3000 0.0009
## 20 0.7565 nan 0.3000 -0.0067
## 40 0.6449 nan 0.3000 -0.0054
## 60 0.5561 nan 0.3000 -0.0065
## 80 0.4738 nan 0.3000 -0.0049
## 100 0.4083 nan 0.3000 -0.0041
## 120 0.3557 nan 0.3000 -0.0019
## 140 0.3016 nan 0.3000 -0.0015
## 160 0.2692 nan 0.3000 -0.0015
## 180 0.2389 nan 0.3000 -0.0034
## 200 0.2188 nan 0.3000 -0.0037
## 220 0.1986 nan 0.3000 -0.0014
## 240 0.1795 nan 0.3000 -0.0037
## 260 0.1638 nan 0.3000 -0.0017
## 280 0.1450 nan 0.3000 -0.0007
## 300 0.1327 nan 0.3000 -0.0008
## 320 0.1231 nan 0.3000 -0.0005
## 340 0.1107 nan 0.3000 -0.0009
## 360 0.0991 nan 0.3000 -0.0005
## 380 0.0912 nan 0.3000 -0.0003
## 400 0.0832 nan 0.3000 -0.0007
## 420 0.0786 nan 0.3000 -0.0005
## 440 0.0713 nan 0.3000 -0.0003
## 460 0.0647 nan 0.3000 -0.0003
## 480 0.0593 nan 0.3000 -0.0006
## 500 0.0549 nan 0.3000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1552 nan 0.3000 0.0557
## 2 1.0707 nan 0.3000 0.0201
## 3 1.0232 nan 0.3000 0.0136
## 4 0.9852 nan 0.3000 0.0100
## 5 0.9412 nan 0.3000 0.0149
## 6 0.9118 nan 0.3000 0.0128
## 7 0.8962 nan 0.3000 0.0002
## 8 0.8807 nan 0.3000 -0.0027
## 9 0.8625 nan 0.3000 0.0015
## 10 0.8501 nan 0.3000 -0.0031
## 20 0.7590 nan 0.3000 -0.0031
## 40 0.6451 nan 0.3000 -0.0044
## 60 0.5640 nan 0.3000 -0.0039
## 80 0.4938 nan 0.3000 -0.0059
## 100 0.4369 nan 0.3000 -0.0015
## 120 0.3833 nan 0.3000 -0.0014
## 140 0.3427 nan 0.3000 -0.0039
## 160 0.3027 nan 0.3000 -0.0009
## 180 0.2767 nan 0.3000 -0.0017
## 200 0.2435 nan 0.3000 -0.0017
## 220 0.2228 nan 0.3000 -0.0012
## 240 0.2033 nan 0.3000 -0.0010
## 260 0.1852 nan 0.3000 -0.0031
## 280 0.1688 nan 0.3000 -0.0013
## 300 0.1538 nan 0.3000 -0.0008
## 320 0.1404 nan 0.3000 -0.0007
## 340 0.1259 nan 0.3000 -0.0008
## 360 0.1139 nan 0.3000 -0.0009
## 380 0.1059 nan 0.3000 -0.0015
## 400 0.0947 nan 0.3000 -0.0003
## 420 0.0882 nan 0.3000 -0.0011
## 440 0.0807 nan 0.3000 -0.0005
## 460 0.0736 nan 0.3000 -0.0008
## 480 0.0672 nan 0.3000 -0.0004
## 500 0.0621 nan 0.3000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1664 nan 0.5000 0.0587
## 2 1.0954 nan 0.5000 0.0324
## 3 1.0381 nan 0.5000 0.0228
## 4 1.0134 nan 0.5000 0.0060
## 5 0.9795 nan 0.5000 0.0132
## 6 0.9616 nan 0.5000 0.0025
## 7 0.9346 nan 0.5000 0.0059
## 8 0.9234 nan 0.5000 0.0004
## 9 0.9078 nan 0.5000 0.0012
## 10 0.8974 nan 0.5000 -0.0027
## 20 0.8410 nan 0.5000 -0.0042
## 40 0.7949 nan 0.5000 -0.0038
## 60 3.5372 nan 0.5000 -0.0010
## 80 3.5128 nan 0.5000 -0.0007
## 100 3.4973 nan 0.5000 0.0000
## 120 3.4873 nan 0.5000 -0.0065
## 140 3.4807 nan 0.5000 -0.0012
## 160 3.4770 nan 0.5000 -0.0000
## 180 3.6933 nan 0.5000 -0.0013
## 200 3.6924 nan 0.5000 -0.0009
## 220 3.6838 nan 0.5000 0.0002
## 240 3.6794 nan 0.5000 0.0006
## 260 3.6703 nan 0.5000 -0.0002
## 280 3.6621 nan 0.5000 -0.0015
## 300 3.6593 nan 0.5000 0.0001
## 320 3.6609 nan 0.5000 -0.0002
## 340 3.6654 nan 0.5000 0.0000
## 360 3.6498 nan 0.5000 0.0000
## 380 3.4139 nan 0.5000 -0.0001
## 400 3.4100 nan 0.5000 -0.0018
## 420 3.4069 nan 0.5000 -0.0001
## 440 3.4127 nan 0.5000 0.0001
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1528 nan 0.5000 0.0606
## 2 1.0907 nan 0.5000 0.0261
## 3 1.0544 nan 0.5000 0.0134
## 4 1.0048 nan 0.5000 0.0102
## 5 0.9847 nan 0.5000 0.0018
## 6 0.9757 nan 0.5000 -0.0032
## 7 0.9623 nan 0.5000 -0.0016
## 8 0.9403 nan 0.5000 0.0008
## 9 0.9362 nan 0.5000 -0.0060
## 10 0.9266 nan 0.5000 -0.0019
## 20 0.8525 nan 0.5000 -0.0058
## 40 0.8044 nan 0.5000 -0.0043
## 60 0.7699 nan 0.5000 -0.0082
## 80 0.7391 nan 0.5000 -0.0066
## 100 0.7130 nan 0.5000 -0.0047
## 120 0.6859 nan 0.5000 -0.0018
## 140 0.6719 nan 0.5000 -0.0019
## 160 0.6505 nan 0.5000 -0.0081
## 180 0.6286 nan 0.5000 -0.0020
## 200 0.6215 nan 0.5000 -0.0082
## 220 0.5975 nan 0.5000 0.0010
## 240 0.5961 nan 0.5000 -0.0101
## 260 0.5741 nan 0.5000 -0.0046
## 280 0.5568 nan 0.5000 -0.0019
## 300 0.5516 nan 0.5000 -0.0046
## 320 0.5333 nan 0.5000 -0.0021
## 340 0.5289 nan 0.5000 -0.0014
## 360 0.5225 nan 0.5000 -0.0168
## 380 0.5045 nan 0.5000 -0.0020
## 400 0.4971 nan 0.5000 -0.0056
## 420 0.4877 nan 0.5000 -0.0016
## 440 0.4809 nan 0.5000 -0.0002
## 460 0.4722 nan 0.5000 -0.0070
## 480 0.4645 nan 0.5000 -0.0015
## 500 0.4612 nan 0.5000 -0.0034
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1559 nan 0.5000 0.0452
## 2 1.0754 nan 0.5000 0.0119
## 3 1.0264 nan 0.5000 0.0216
## 4 0.9938 nan 0.5000 0.0145
## 5 0.9719 nan 0.5000 0.0062
## 6 0.9553 nan 0.5000 0.0024
## 7 0.9428 nan 0.5000 0.0003
## 8 0.9250 nan 0.5000 0.0006
## 9 0.9115 nan 0.5000 0.0059
## 10 0.8988 nan 0.5000 -0.0008
## 20 0.8471 nan 0.5000 -0.0014
## 40 0.8155 nan 0.5000 -0.0086
## 60 0.7774 nan 0.5000 -0.0028
## 80 0.7456 nan 0.5000 -0.0025
## 100 0.7278 nan 0.5000 -0.0021
## 120 0.7050 nan 0.5000 -0.0062
## 140 0.6900 nan 0.5000 -0.0062
## 160 0.6759 nan 0.5000 -0.0054
## 180 0.6543 nan 0.5000 -0.0080
## 200 0.6449 nan 0.5000 -0.0064
## 220 0.6349 nan 0.5000 -0.0040
## 240 0.6178 nan 0.5000 -0.0051
## 260 0.6118 nan 0.5000 -0.0090
## 280 0.5921 nan 0.5000 -0.0054
## 300 0.5833 nan 0.5000 -0.0006
## 320 0.5815 nan 0.5000 -0.0120
## 340 0.5761 nan 0.5000 -0.0006
## 360 0.5649 nan 0.5000 -0.0076
## 380 0.5513 nan 0.5000 -0.0033
## 400 0.5358 nan 0.5000 -0.0017
## 420 0.5285 nan 0.5000 -0.0018
## 440 0.5186 nan 0.5000 -0.0070
## 460 0.5081 nan 0.5000 -0.0023
## 480 0.5046 nan 0.5000 -0.0049
## 500 0.4900 nan 0.5000 -0.0053
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1357 nan 0.5000 0.0665
## 2 1.0259 nan 0.5000 0.0308
## 3 0.9820 nan 0.5000 0.0143
## 4 0.9357 nan 0.5000 0.0102
## 5 0.9146 nan 0.5000 -0.0014
## 6 0.9037 nan 0.5000 -0.0108
## 7 0.8857 nan 0.5000 -0.0082
## 8 0.8783 nan 0.5000 -0.0065
## 9 0.8707 nan 0.5000 -0.0071
## 10 0.8500 nan 0.5000 -0.0017
## 20 0.7926 nan 0.5000 -0.0181
## 40 0.6990 nan 0.5000 -0.0035
## 60 0.6084 nan 0.5000 -0.0109
## 80 0.5411 nan 0.5000 -0.0046
## 100 1.4444 nan 0.5000 -0.0061
## 120 1.3870 nan 0.5000 -0.0003
## 140 1.2943 nan 0.5000 -0.0000
## 160 1.2197 nan 0.5000 -0.0046
## 180 1.4743 nan 0.5000 -0.0056
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1284 nan 0.5000 0.0781
## 2 1.0407 nan 0.5000 0.0349
## 3 0.9910 nan 0.5000 0.0187
## 4 0.9392 nan 0.5000 0.0128
## 5 0.9265 nan 0.5000 -0.0069
## 6 0.9138 nan 0.5000 -0.0091
## 7 0.8939 nan 0.5000 -0.0010
## 8 0.8853 nan 0.5000 -0.0075
## 9 0.8626 nan 0.5000 -0.0027
## 10 0.8627 nan 0.5000 -0.0232
## 20 0.7961 nan 0.5000 -0.0123
## 40 0.6952 nan 0.5000 -0.0088
## 60 0.6221 nan 0.5000 -0.0065
## 80 171.2534 nan 0.5000 -0.0031
## 100 62762966852.1521 nan 0.5000 -0.0124
## 120 62762966852.0974 nan 0.5000 -0.0057
## 140 62762966852.0733 nan 0.5000 -0.0059
## 160 62762975095.3167 nan 0.5000 -0.0034
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1291 nan 0.5000 0.0722
## 2 1.0396 nan 0.5000 0.0401
## 3 0.9887 nan 0.5000 0.0156
## 4 0.9610 nan 0.5000 -0.0014
## 5 0.9384 nan 0.5000 0.0036
## 6 0.9195 nan 0.5000 0.0015
## 7 0.8943 nan 0.5000 0.0018
## 8 0.8878 nan 0.5000 -0.0064
## 9 0.8606 nan 0.5000 0.0122
## 10 0.8570 nan 0.5000 -0.0089
## 20 0.7676 nan 0.5000 -0.0127
## 40 0.6707 nan 0.5000 -0.0084
## 60 0.6075 nan 0.5000 -0.0035
## 80 0.5596 nan 0.5000 -0.0094
## 100 0.5128 nan 0.5000 -0.0104
## 120 0.4629 nan 0.5000 -0.0079
## 140 0.4078 nan 0.5000 -0.0059
## 160 0.3591 nan 0.5000 -0.0048
## 180 0.3216 nan 0.5000 -0.0044
## 200 0.3025 nan 0.5000 -0.0057
## 220 0.2831 nan 0.5000 -0.0058
## 240 0.2510 nan 0.5000 -0.0020
## 260 0.2349 nan 0.5000 -0.0045
## 280 0.2070 nan 0.5000 -0.0017
## 300 0.1821 nan 0.5000 -0.0024
## 320 0.1649 nan 0.5000 -0.0011
## 340 0.1499 nan 0.5000 -0.0027
## 360 0.1373 nan 0.5000 -0.0023
## 380 0.1279 nan 0.5000 -0.0031
## 400 0.1135 nan 0.5000 -0.0004
## 420 0.1054 nan 0.5000 -0.0017
## 440 0.0981 nan 0.5000 -0.0013
## 460 0.0904 nan 0.5000 -0.0004
## 480 0.0839 nan 0.5000 -0.0020
## 500 0.0762 nan 0.5000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0728 nan 0.5000 0.1029
## 2 0.9650 nan 0.5000 0.0291
## 3 0.9332 nan 0.5000 -0.0044
## 4 0.8901 nan 0.5000 0.0035
## 5 0.8657 nan 0.5000 -0.0010
## 6 0.8478 nan 0.5000 -0.0037
## 7 0.8254 nan 0.5000 -0.0015
## 8 0.8204 nan 0.5000 -0.0234
## 9 0.8103 nan 0.5000 -0.0081
## 10 0.7951 nan 0.5000 -0.0192
## 20 0.8923 nan 0.5000 -0.0096
## 40 inf nan 0.5000 nan
## 60 inf nan 0.5000 nan
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 0.0001
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0855 nan 0.5000 0.0954
## 2 1.0182 nan 0.5000 0.0163
## 3 0.9519 nan 0.5000 0.0269
## 4 0.9181 nan 0.5000 -0.0047
## 5 0.8857 nan 0.5000 0.0039
## 6 0.8595 nan 0.5000 -0.0018
## 7 0.8414 nan 0.5000 -0.0175
## 8 0.8443 nan 0.5000 -0.0289
## 9 0.8271 nan 0.5000 -0.0084
## 10 0.8103 nan 0.5000 -0.0096
## 20 0.6887 nan 0.5000 -0.0086
## 40 0.5535 nan 0.5000 -0.0085
## 60 0.4389 nan 0.5000 -0.0086
## 80 0.3564 nan 0.5000 -0.0127
## 100 0.2935 nan 0.5000 -0.0044
## 120 0.2299 nan 0.5000 -0.0028
## 140 0.1900 nan 0.5000 -0.0033
## 160 0.1549 nan 0.5000 -0.0018
## 180 0.1323 nan 0.5000 -0.0006
## 200 0.1091 nan 0.5000 -0.0012
## 220 0.0946 nan 0.5000 -0.0008
## 240 0.0830 nan 0.5000 -0.0008
## 260 0.0723 nan 0.5000 -0.0003
## 280 0.0636 nan 0.5000 -0.0013
## 300 0.0566 nan 0.5000 -0.0003
## 320 0.0514 nan 0.5000 -0.0010
## 340 0.0451 nan 0.5000 -0.0010
## 360 0.0393 nan 0.5000 -0.0016
## 380 0.0349 nan 0.5000 -0.0004
## 400 0.0299 nan 0.5000 -0.0002
## 420 0.0271 nan 0.5000 -0.0006
## 440 0.0239 nan 0.5000 -0.0002
## 460 0.0217 nan 0.5000 -0.0004
## 480 0.0196 nan 0.5000 -0.0004
## 500 0.0174 nan 0.5000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0779 nan 0.5000 0.0950
## 2 0.9666 nan 0.5000 0.0478
## 3 0.9289 nan 0.5000 0.0067
## 4 0.8984 nan 0.5000 0.0056
## 5 0.8787 nan 0.5000 -0.0058
## 6 0.8548 nan 0.5000 -0.0045
## 7 0.8320 nan 0.5000 -0.0009
## 8 0.8161 nan 0.5000 -0.0007
## 9 0.7992 nan 0.5000 0.0035
## 10 0.7872 nan 0.5000 -0.0051
## 20 0.6924 nan 0.5000 -0.0082
## 40 0.5611 nan 0.5000 -0.0070
## 60 0.4552 nan 0.5000 -0.0028
## 80 0.3742 nan 0.5000 -0.0062
## 100 0.3155 nan 0.5000 -0.0061
## 120 0.2629 nan 0.5000 -0.0028
## 140 0.2076 nan 0.5000 -0.0016
## 160 0.1753 nan 0.5000 -0.0015
## 180 0.1444 nan 0.5000 -0.0001
## 200 0.1209 nan 0.5000 -0.0016
## 220 0.1062 nan 0.5000 -0.0019
## 240 0.0879 nan 0.5000 -0.0012
## 260 0.0752 nan 0.5000 -0.0001
## 280 0.0655 nan 0.5000 -0.0004
## 300 0.0583 nan 0.5000 -0.0008
## 320 0.0505 nan 0.5000 -0.0007
## 340 0.0423 nan 0.5000 -0.0010
## 360 0.0371 nan 0.5000 -0.0004
## 380 0.0325 nan 0.5000 -0.0002
## 400 0.0294 nan 0.5000 -0.0002
## 420 0.0274 nan 0.5000 -0.0002
## 440 0.0240 nan 0.5000 -0.0006
## 460 0.0206 nan 0.5000 -0.0002
## 480 0.0183 nan 0.5000 -0.0001
## 500 0.0167 nan 0.5000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1252 nan 1.0000 0.0759
## 2 1.0839 nan 1.0000 0.0048
## 3 1.0214 nan 1.0000 0.0238
## 4 1.0131 nan 1.0000 -0.0148
## 5 0.9855 nan 1.0000 -0.0132
## 6 0.9775 nan 1.0000 -0.0091
## 7 0.9785 nan 1.0000 -0.0135
## 8 0.9850 nan 1.0000 -0.0218
## 9 0.9549 nan 1.0000 0.0146
## 10 0.9593 nan 1.0000 -0.0161
## 20 0.9170 nan 1.0000 -0.0108
## 40 0.8270 nan 1.0000 -0.0139
## 60 0.8037 nan 1.0000 -0.0177
## 80 2.6195 nan 1.0000 0.0003
## 100 2.6011 nan 1.0000 -0.0041
## 120 2.5644 nan 1.0000 -0.0030
## 140 5.8721 nan 1.0000 0.0014
## 160 5.8955 nan 1.0000 -0.0445
## 180 5.8961 nan 1.0000 0.0056
## 200 5.8798 nan 1.0000 0.0023
## 220 5.8826 nan 1.0000 -0.0019
## 240 5.8829 nan 1.0000 -0.0041
## 260 5.8723 nan 1.0000 0.0006
## 280 5.8874 nan 1.0000 -0.0206
## 300 5.8725 nan 1.0000 -0.0004
## 320 5.8749 nan 1.0000 -0.0053
## 340 5.8525 nan 1.0000 -0.0007
## 360 5.8522 nan 1.0000 -0.0000
## 380 5.8525 nan 1.0000 -0.0002
## 400 5.8626 nan 1.0000 -0.0012
## 420 5.8420 nan 1.0000 -0.0084
## 440 5.8378 nan 1.0000 0.0007
## 460 5.8394 nan 1.0000 -0.0046
## 480 5.8426 nan 1.0000 -0.0096
## 500 5.8245 nan 1.0000 -0.0074
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1142 nan 1.0000 0.0706
## 2 1.0332 nan 1.0000 0.0375
## 3 0.9829 nan 1.0000 0.0100
## 4 0.9747 nan 1.0000 -0.0120
## 5 0.9510 nan 1.0000 0.0030
## 6 0.9440 nan 1.0000 -0.0040
## 7 0.9383 nan 1.0000 -0.0017
## 8 0.9375 nan 1.0000 -0.0161
## 9 0.9155 nan 1.0000 0.0111
## 10 0.9075 nan 1.0000 -0.0019
## 20 0.8959 nan 1.0000 -0.0220
## 40 0.8547 nan 1.0000 -0.0185
## 60 1214.8926 nan 1.0000 -0.0123
## 80 1215.2222 nan 1.0000 -0.0034
## 100 1215.2735 nan 1.0000 0.0344
## 120 1215.2386 nan 1.0000 -0.0004
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1181 nan 1.0000 0.0449
## 2 1.0354 nan 1.0000 0.0403
## 3 0.9951 nan 1.0000 0.0024
## 4 0.9941 nan 1.0000 -0.0196
## 5 0.9659 nan 1.0000 0.0150
## 6 0.9585 nan 1.0000 -0.0064
## 7 0.9310 nan 1.0000 0.0109
## 8 0.9419 nan 1.0000 -0.0199
## 9 0.9267 nan 1.0000 0.0044
## 10 0.9043 nan 1.0000 0.0116
## 20 1.9020 nan 1.0000 0.0005
## 40 1.8518 nan 1.0000 -0.0255
## 60 1.7922 nan 1.0000 0.0005
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0608 nan 1.0000 0.0992
## 2 0.9368 nan 1.0000 0.0401
## 3 0.9120 nan 1.0000 -0.0064
## 4 0.8978 nan 1.0000 -0.0039
## 5 0.8971 nan 1.0000 -0.0315
## 6 0.9016 nan 1.0000 -0.0471
## 7 0.8923 nan 1.0000 -0.0344
## 8 0.9003 nan 1.0000 -0.0158
## 9 0.9251 nan 1.0000 -0.0524
## 10 0.9042 nan 1.0000 -0.0111
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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## 340 inf nan 1.0000 nan
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## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0495 nan 1.0000 0.1020
## 2 0.9848 nan 1.0000 0.0041
## 3 0.9687 nan 1.0000 -0.0193
## 4 0.9401 nan 1.0000 -0.0120
## 5 0.9204 nan 1.0000 -0.0189
## 6 0.9293 nan 1.0000 -0.0438
## 7 0.9040 nan 1.0000 -0.0050
## 8 0.9585 nan 1.0000 -0.0801
## 9 1.3293 nan 1.0000 -0.4286
## 10 1.3682 nan 1.0000 -0.0711
## 20 1.5033 nan 1.0000 -0.1508
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
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## 220 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0912 nan 1.0000 0.0999
## 2 0.9630 nan 1.0000 0.0491
## 3 0.9670 nan 1.0000 -0.0498
## 4 0.9627 nan 1.0000 -0.0537
## 5 0.9280 nan 1.0000 -0.0246
## 6 0.9602 nan 1.0000 -0.0624
## 7 0.9620 nan 1.0000 -0.0340
## 8 0.9113 nan 1.0000 0.0130
## 9 0.8892 nan 1.0000 -0.0080
## 10 0.8987 nan 1.0000 -0.0316
## 20 0.9085 nan 1.0000 -0.0371
## 40 0.8190 nan 1.0000 0.0376
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 5792165582463.2637 nan 1.0000 inf
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0389 nan 1.0000 0.0746
## 2 0.9580 nan 1.0000 0.0129
## 3 0.9224 nan 1.0000 -0.0190
## 4 0.8916 nan 1.0000 -0.0213
## 5 0.8946 nan 1.0000 -0.0444
## 6 0.9223 nan 1.0000 -0.0750
## 7 1.0956 nan 1.0000 -0.2297
## 8 inf nan 1.0000 -inf
## 9 inf nan 1.0000 -0.0788
## 10 inf nan 1.0000 -0.0148
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0280 nan 1.0000 0.1068
## 2 0.9460 nan 1.0000 0.0042
## 3 0.9304 nan 1.0000 -0.0335
## 4 0.8782 nan 1.0000 -0.0003
## 5 0.8594 nan 1.0000 -0.0161
## 6 0.8460 nan 1.0000 -0.0175
## 7 0.8209 nan 1.0000 -0.0143
## 8 0.9540 nan 1.0000 -0.1705
## 9 0.9601 nan 1.0000 -0.0546
## 10 0.9629 nan 1.0000 -0.0307
## 20 0.8806 nan 1.0000 -0.0299
## 40 2.0977 nan 1.0000 -0.1409
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0037 nan 1.0000 0.0884
## 2 0.9525 nan 1.0000 -0.0136
## 3 0.9550 nan 1.0000 -0.0464
## 4 0.9750 nan 1.0000 -0.0635
## 5 0.9232 nan 1.0000 -0.0019
## 6 0.8851 nan 1.0000 -0.0167
## 7 0.8907 nan 1.0000 -0.0377
## 8 0.9736 nan 1.0000 -0.0473
## 9 0.9621 nan 1.0000 -0.0196
## 10 1.0457 nan 1.0000 -0.1380
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
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## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
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## 280 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
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## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
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## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
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## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2914 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0001
## 10 1.2897 nan 0.0010 0.0002
## 20 1.2860 nan 0.0010 0.0002
## 40 1.2787 nan 0.0010 0.0002
## 60 1.2715 nan 0.0010 0.0002
## 80 1.2646 nan 0.0010 0.0002
## 100 1.2582 nan 0.0010 0.0001
## 120 1.2520 nan 0.0010 0.0001
## 140 1.2461 nan 0.0010 0.0001
## 160 1.2402 nan 0.0010 0.0001
## 180 1.2346 nan 0.0010 0.0001
## 200 1.2292 nan 0.0010 0.0001
## 220 1.2239 nan 0.0010 0.0001
## 240 1.2186 nan 0.0010 0.0001
## 260 1.2136 nan 0.0010 0.0001
## 280 1.2088 nan 0.0010 0.0001
## 300 1.2040 nan 0.0010 0.0001
## 320 1.1993 nan 0.0010 0.0001
## 340 1.1950 nan 0.0010 0.0001
## 360 1.1907 nan 0.0010 0.0001
## 380 1.1864 nan 0.0010 0.0001
## 400 1.1823 nan 0.0010 0.0001
## 420 1.1785 nan 0.0010 0.0001
## 440 1.1747 nan 0.0010 0.0001
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## 480 1.1669 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2921 nan 0.0010 0.0002
## 4 1.2917 nan 0.0010 0.0002
## 5 1.2914 nan 0.0010 0.0002
## 6 1.2910 nan 0.0010 0.0002
## 7 1.2906 nan 0.0010 0.0002
## 8 1.2902 nan 0.0010 0.0002
## 9 1.2900 nan 0.0010 0.0001
## 10 1.2896 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2787 nan 0.0010 0.0002
## 60 1.2717 nan 0.0010 0.0002
## 80 1.2649 nan 0.0010 0.0002
## 100 1.2581 nan 0.0010 0.0001
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## 140 1.2454 nan 0.0010 0.0002
## 160 1.2397 nan 0.0010 0.0001
## 180 1.2340 nan 0.0010 0.0001
## 200 1.2287 nan 0.0010 0.0001
## 220 1.2235 nan 0.0010 0.0001
## 240 1.2182 nan 0.0010 0.0001
## 260 1.2134 nan 0.0010 0.0001
## 280 1.2084 nan 0.0010 0.0001
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## 320 1.1991 nan 0.0010 0.0001
## 340 1.1946 nan 0.0010 0.0001
## 360 1.1902 nan 0.0010 0.0001
## 380 1.1857 nan 0.0010 0.0001
## 400 1.1817 nan 0.0010 0.0001
## 420 1.1778 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0001
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2903 nan 0.0010 0.0002
## 9 1.2899 nan 0.0010 0.0002
## 10 1.2896 nan 0.0010 0.0002
## 20 1.2857 nan 0.0010 0.0002
## 40 1.2786 nan 0.0010 0.0002
## 60 1.2715 nan 0.0010 0.0002
## 80 1.2647 nan 0.0010 0.0002
## 100 1.2582 nan 0.0010 0.0001
## 120 1.2519 nan 0.0010 0.0001
## 140 1.2460 nan 0.0010 0.0001
## 160 1.2401 nan 0.0010 0.0001
## 180 1.2345 nan 0.0010 0.0001
## 200 1.2291 nan 0.0010 0.0001
## 220 1.2237 nan 0.0010 0.0001
## 240 1.2182 nan 0.0010 0.0001
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## 400 1.1818 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2894 nan 0.0010 0.0002
## 9 1.2890 nan 0.0010 0.0002
## 10 1.2885 nan 0.0010 0.0003
## 20 1.2838 nan 0.0010 0.0002
## 40 1.2744 nan 0.0010 0.0002
## 60 1.2653 nan 0.0010 0.0002
## 80 1.2566 nan 0.0010 0.0002
## 100 1.2481 nan 0.0010 0.0002
## 120 1.2400 nan 0.0010 0.0002
## 140 1.2320 nan 0.0010 0.0002
## 160 1.2242 nan 0.0010 0.0002
## 180 1.2167 nan 0.0010 0.0002
## 200 1.2096 nan 0.0010 0.0002
## 220 1.2025 nan 0.0010 0.0002
## 240 1.1955 nan 0.0010 0.0002
## 260 1.1889 nan 0.0010 0.0001
## 280 1.1826 nan 0.0010 0.0001
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## 320 1.1698 nan 0.0010 0.0001
## 340 1.1641 nan 0.0010 0.0001
## 360 1.1582 nan 0.0010 0.0001
## 380 1.1526 nan 0.0010 0.0001
## 400 1.1470 nan 0.0010 0.0001
## 420 1.1416 nan 0.0010 0.0001
## 440 1.1364 nan 0.0010 0.0001
## 460 1.1314 nan 0.0010 0.0001
## 480 1.1264 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2899 nan 0.0010 0.0002
## 8 1.2895 nan 0.0010 0.0002
## 9 1.2890 nan 0.0010 0.0002
## 10 1.2884 nan 0.0010 0.0003
## 20 1.2835 nan 0.0010 0.0002
## 40 1.2743 nan 0.0010 0.0002
## 60 1.2652 nan 0.0010 0.0002
## 80 1.2564 nan 0.0010 0.0002
## 100 1.2478 nan 0.0010 0.0002
## 120 1.2394 nan 0.0010 0.0002
## 140 1.2313 nan 0.0010 0.0002
## 160 1.2236 nan 0.0010 0.0002
## 180 1.2161 nan 0.0010 0.0002
## 200 1.2087 nan 0.0010 0.0002
## 220 1.2017 nan 0.0010 0.0001
## 240 1.1949 nan 0.0010 0.0001
## 260 1.1882 nan 0.0010 0.0001
## 280 1.1816 nan 0.0010 0.0002
## 300 1.1754 nan 0.0010 0.0001
## 320 1.1692 nan 0.0010 0.0001
## 340 1.1632 nan 0.0010 0.0001
## 360 1.1576 nan 0.0010 0.0001
## 380 1.1519 nan 0.0010 0.0001
## 400 1.1467 nan 0.0010 0.0001
## 420 1.1411 nan 0.0010 0.0001
## 440 1.1360 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0003
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
## 9 1.2890 nan 0.0010 0.0002
## 10 1.2886 nan 0.0010 0.0002
## 20 1.2837 nan 0.0010 0.0002
## 40 1.2746 nan 0.0010 0.0002
## 60 1.2652 nan 0.0010 0.0002
## 80 1.2562 nan 0.0010 0.0002
## 100 1.2477 nan 0.0010 0.0002
## 120 1.2394 nan 0.0010 0.0002
## 140 1.2314 nan 0.0010 0.0002
## 160 1.2237 nan 0.0010 0.0001
## 180 1.2162 nan 0.0010 0.0001
## 200 1.2089 nan 0.0010 0.0001
## 220 1.2019 nan 0.0010 0.0002
## 240 1.1952 nan 0.0010 0.0002
## 260 1.1885 nan 0.0010 0.0001
## 280 1.1821 nan 0.0010 0.0002
## 300 1.1757 nan 0.0010 0.0001
## 320 1.1697 nan 0.0010 0.0001
## 340 1.1636 nan 0.0010 0.0001
## 360 1.1579 nan 0.0010 0.0001
## 380 1.1525 nan 0.0010 0.0001
## 400 1.1469 nan 0.0010 0.0001
## 420 1.1416 nan 0.0010 0.0001
## 440 1.1364 nan 0.0010 0.0001
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## 480 1.1263 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2916 nan 0.0010 0.0003
## 4 1.2910 nan 0.0010 0.0002
## 5 1.2905 nan 0.0010 0.0003
## 6 1.2900 nan 0.0010 0.0002
## 7 1.2893 nan 0.0010 0.0002
## 8 1.2887 nan 0.0010 0.0003
## 9 1.2881 nan 0.0010 0.0003
## 10 1.2877 nan 0.0010 0.0002
## 20 1.2821 nan 0.0010 0.0002
## 40 1.2714 nan 0.0010 0.0002
## 60 1.2609 nan 0.0010 0.0002
## 80 1.2508 nan 0.0010 0.0002
## 100 1.2413 nan 0.0010 0.0002
## 120 1.2318 nan 0.0010 0.0002
## 140 1.2227 nan 0.0010 0.0002
## 160 1.2139 nan 0.0010 0.0002
## 180 1.2053 nan 0.0010 0.0002
## 200 1.1971 nan 0.0010 0.0001
## 220 1.1888 nan 0.0010 0.0002
## 240 1.1808 nan 0.0010 0.0002
## 260 1.1731 nan 0.0010 0.0002
## 280 1.1658 nan 0.0010 0.0002
## 300 1.1583 nan 0.0010 0.0001
## 320 1.1510 nan 0.0010 0.0001
## 340 1.1442 nan 0.0010 0.0001
## 360 1.1376 nan 0.0010 0.0001
## 380 1.1311 nan 0.0010 0.0002
## 400 1.1248 nan 0.0010 0.0001
## 420 1.1188 nan 0.0010 0.0001
## 440 1.1129 nan 0.0010 0.0001
## 460 1.1072 nan 0.0010 0.0002
## 480 1.1014 nan 0.0010 0.0001
## 500 1.0959 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0002
## 3 1.2916 nan 0.0010 0.0003
## 4 1.2911 nan 0.0010 0.0003
## 5 1.2905 nan 0.0010 0.0002
## 6 1.2899 nan 0.0010 0.0002
## 7 1.2893 nan 0.0010 0.0002
## 8 1.2888 nan 0.0010 0.0002
## 9 1.2883 nan 0.0010 0.0002
## 10 1.2877 nan 0.0010 0.0003
## 20 1.2824 nan 0.0010 0.0003
## 40 1.2714 nan 0.0010 0.0002
## 60 1.2607 nan 0.0010 0.0002
## 80 1.2507 nan 0.0010 0.0002
## 100 1.2410 nan 0.0010 0.0002
## 120 1.2315 nan 0.0010 0.0002
## 140 1.2223 nan 0.0010 0.0002
## 160 1.2134 nan 0.0010 0.0002
## 180 1.2050 nan 0.0010 0.0002
## 200 1.1971 nan 0.0010 0.0002
## 220 1.1893 nan 0.0010 0.0002
## 240 1.1815 nan 0.0010 0.0002
## 260 1.1738 nan 0.0010 0.0002
## 280 1.1664 nan 0.0010 0.0001
## 300 1.1594 nan 0.0010 0.0001
## 320 1.1523 nan 0.0010 0.0001
## 340 1.1454 nan 0.0010 0.0002
## 360 1.1388 nan 0.0010 0.0001
## 380 1.1323 nan 0.0010 0.0001
## 400 1.1259 nan 0.0010 0.0001
## 420 1.1197 nan 0.0010 0.0001
## 440 1.1136 nan 0.0010 0.0002
## 460 1.1078 nan 0.0010 0.0001
## 480 1.1020 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2927 nan 0.0010 0.0003
## 2 1.2921 nan 0.0010 0.0002
## 3 1.2916 nan 0.0010 0.0002
## 4 1.2910 nan 0.0010 0.0002
## 5 1.2904 nan 0.0010 0.0003
## 6 1.2898 nan 0.0010 0.0003
## 7 1.2893 nan 0.0010 0.0003
## 8 1.2887 nan 0.0010 0.0002
## 9 1.2881 nan 0.0010 0.0003
## 10 1.2876 nan 0.0010 0.0002
## 20 1.2820 nan 0.0010 0.0002
## 40 1.2712 nan 0.0010 0.0002
## 60 1.2608 nan 0.0010 0.0002
## 80 1.2508 nan 0.0010 0.0002
## 100 1.2412 nan 0.0010 0.0002
## 120 1.2319 nan 0.0010 0.0002
## 140 1.2229 nan 0.0010 0.0002
## 160 1.2140 nan 0.0010 0.0002
## 180 1.2053 nan 0.0010 0.0002
## 200 1.1968 nan 0.0010 0.0002
## 220 1.1887 nan 0.0010 0.0001
## 240 1.1810 nan 0.0010 0.0002
## 260 1.1732 nan 0.0010 0.0001
## 280 1.1659 nan 0.0010 0.0002
## 300 1.1586 nan 0.0010 0.0002
## 320 1.1516 nan 0.0010 0.0001
## 340 1.1448 nan 0.0010 0.0001
## 360 1.1382 nan 0.0010 0.0001
## 380 1.1318 nan 0.0010 0.0001
## 400 1.1256 nan 0.0010 0.0002
## 420 1.1193 nan 0.0010 0.0001
## 440 1.1134 nan 0.0010 0.0001
## 460 1.1075 nan 0.0010 0.0001
## 480 1.1017 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2574 nan 0.1000 0.0167
## 2 1.2260 nan 0.1000 0.0147
## 3 1.2058 nan 0.1000 0.0086
## 4 1.1798 nan 0.1000 0.0056
## 5 1.1595 nan 0.1000 0.0097
## 6 1.1435 nan 0.1000 0.0062
## 7 1.1291 nan 0.1000 0.0063
## 8 1.1155 nan 0.1000 0.0052
## 9 1.1002 nan 0.1000 0.0063
## 10 1.0883 nan 0.1000 0.0044
## 20 0.9956 nan 0.1000 0.0030
## 40 0.9169 nan 0.1000 -0.0001
## 60 0.8787 nan 0.1000 -0.0009
## 80 0.8483 nan 0.1000 -0.0001
## 100 0.8294 nan 0.1000 -0.0015
## 120 0.8142 nan 0.1000 -0.0020
## 140 0.8017 nan 0.1000 -0.0007
## 160 0.7920 nan 0.1000 -0.0011
## 180 0.7804 nan 0.1000 -0.0005
## 200 0.7695 nan 0.1000 -0.0006
## 220 0.7609 nan 0.1000 -0.0004
## 240 0.7545 nan 0.1000 -0.0011
## 260 0.7467 nan 0.1000 -0.0006
## 280 0.7399 nan 0.1000 -0.0012
## 300 0.7328 nan 0.1000 -0.0011
## 320 0.7265 nan 0.1000 -0.0005
## 340 0.7210 nan 0.1000 -0.0007
## 360 0.7158 nan 0.1000 -0.0003
## 380 0.7096 nan 0.1000 -0.0009
## 400 0.7065 nan 0.1000 -0.0006
## 420 0.7018 nan 0.1000 -0.0010
## 440 0.6969 nan 0.1000 -0.0007
## 460 0.6925 nan 0.1000 -0.0011
## 480 0.6884 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2574 nan 0.1000 0.0180
## 2 1.2291 nan 0.1000 0.0137
## 3 1.2016 nan 0.1000 0.0118
## 4 1.1778 nan 0.1000 0.0097
## 5 1.1608 nan 0.1000 0.0075
## 6 1.1417 nan 0.1000 0.0081
## 7 1.1282 nan 0.1000 0.0057
## 8 1.1147 nan 0.1000 0.0049
## 9 1.1023 nan 0.1000 0.0047
## 10 1.0912 nan 0.1000 0.0051
## 20 0.9989 nan 0.1000 0.0013
## 40 0.9131 nan 0.1000 0.0007
## 60 0.8728 nan 0.1000 -0.0012
## 80 0.8468 nan 0.1000 -0.0003
## 100 0.8226 nan 0.1000 -0.0001
## 120 0.8072 nan 0.1000 -0.0005
## 140 0.7946 nan 0.1000 -0.0014
## 160 0.7835 nan 0.1000 -0.0010
## 180 0.7731 nan 0.1000 -0.0006
## 200 0.7658 nan 0.1000 -0.0005
## 220 0.7564 nan 0.1000 -0.0006
## 240 0.7496 nan 0.1000 -0.0015
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## 280 0.7376 nan 0.1000 -0.0007
## 300 0.7317 nan 0.1000 -0.0008
## 320 0.7284 nan 0.1000 -0.0008
## 340 0.7226 nan 0.1000 -0.0001
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## 380 0.7098 nan 0.1000 -0.0003
## 400 0.7031 nan 0.1000 -0.0007
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## 480 0.6858 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2540 nan 0.1000 0.0183
## 2 1.2198 nan 0.1000 0.0135
## 3 1.1933 nan 0.1000 0.0117
## 4 1.1715 nan 0.1000 0.0094
## 5 1.1516 nan 0.1000 0.0078
## 6 1.1340 nan 0.1000 0.0073
## 7 1.1206 nan 0.1000 0.0052
## 8 1.1089 nan 0.1000 0.0048
## 9 1.0932 nan 0.1000 0.0060
## 10 1.0805 nan 0.1000 0.0068
## 20 0.9963 nan 0.1000 0.0027
## 40 0.9111 nan 0.1000 -0.0012
## 60 0.8663 nan 0.1000 -0.0012
## 80 0.8433 nan 0.1000 0.0001
## 100 0.8267 nan 0.1000 0.0001
## 120 0.8104 nan 0.1000 -0.0004
## 140 0.8015 nan 0.1000 -0.0013
## 160 0.7881 nan 0.1000 -0.0014
## 180 0.7796 nan 0.1000 -0.0001
## 200 0.7710 nan 0.1000 -0.0008
## 220 0.7612 nan 0.1000 -0.0007
## 240 0.7540 nan 0.1000 -0.0001
## 260 0.7487 nan 0.1000 -0.0014
## 280 0.7430 nan 0.1000 -0.0019
## 300 0.7354 nan 0.1000 -0.0016
## 320 0.7292 nan 0.1000 -0.0010
## 340 0.7214 nan 0.1000 -0.0014
## 360 0.7152 nan 0.1000 -0.0006
## 380 0.7091 nan 0.1000 -0.0019
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## 460 0.6902 nan 0.1000 -0.0009
## 480 0.6864 nan 0.1000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2496 nan 0.1000 0.0225
## 2 1.2087 nan 0.1000 0.0153
## 3 1.1745 nan 0.1000 0.0161
## 4 1.1475 nan 0.1000 0.0120
## 5 1.1258 nan 0.1000 0.0092
## 6 1.1053 nan 0.1000 0.0090
## 7 1.0880 nan 0.1000 0.0060
## 8 1.0687 nan 0.1000 0.0077
## 9 1.0554 nan 0.1000 0.0046
## 10 1.0405 nan 0.1000 0.0037
## 20 0.9352 nan 0.1000 0.0011
## 40 0.8446 nan 0.1000 -0.0010
## 60 0.7931 nan 0.1000 -0.0020
## 80 0.7573 nan 0.1000 -0.0009
## 100 0.7290 nan 0.1000 -0.0006
## 120 0.7085 nan 0.1000 -0.0010
## 140 0.6831 nan 0.1000 -0.0004
## 160 0.6583 nan 0.1000 -0.0011
## 180 0.6397 nan 0.1000 -0.0019
## 200 0.6190 nan 0.1000 -0.0006
## 220 0.6045 nan 0.1000 -0.0013
## 240 0.5857 nan 0.1000 -0.0013
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## 280 0.5594 nan 0.1000 -0.0020
## 300 0.5446 nan 0.1000 -0.0025
## 320 0.5318 nan 0.1000 -0.0007
## 340 0.5212 nan 0.1000 -0.0014
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## 380 0.4963 nan 0.1000 -0.0008
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## 460 0.4435 nan 0.1000 -0.0013
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2468 nan 0.1000 0.0195
## 2 1.2075 nan 0.1000 0.0199
## 3 1.1683 nan 0.1000 0.0157
## 4 1.1392 nan 0.1000 0.0137
## 5 1.1125 nan 0.1000 0.0117
## 6 1.0917 nan 0.1000 0.0090
## 7 1.0696 nan 0.1000 0.0084
## 8 1.0524 nan 0.1000 0.0065
## 9 1.0340 nan 0.1000 0.0066
## 10 1.0190 nan 0.1000 0.0042
## 20 0.9230 nan 0.1000 0.0026
## 40 0.8410 nan 0.1000 0.0007
## 60 0.7960 nan 0.1000 -0.0004
## 80 0.7569 nan 0.1000 -0.0015
## 100 0.7255 nan 0.1000 0.0000
## 120 0.7006 nan 0.1000 -0.0005
## 140 0.6741 nan 0.1000 -0.0012
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## 180 0.6388 nan 0.1000 -0.0016
## 200 0.6134 nan 0.1000 -0.0001
## 220 0.5965 nan 0.1000 -0.0003
## 240 0.5784 nan 0.1000 -0.0018
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## 280 0.5510 nan 0.1000 -0.0017
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## 320 0.5234 nan 0.1000 -0.0004
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## 380 0.4809 nan 0.1000 -0.0019
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2371 nan 0.1000 0.0236
## 2 1.1978 nan 0.1000 0.0151
## 3 1.1653 nan 0.1000 0.0164
## 4 1.1424 nan 0.1000 0.0087
## 5 1.1145 nan 0.1000 0.0101
## 6 1.0944 nan 0.1000 0.0068
## 7 1.0747 nan 0.1000 0.0075
## 8 1.0571 nan 0.1000 0.0061
## 9 1.0387 nan 0.1000 0.0063
## 10 1.0251 nan 0.1000 0.0036
## 20 0.9301 nan 0.1000 -0.0004
## 40 0.8430 nan 0.1000 -0.0001
## 60 0.7963 nan 0.1000 -0.0016
## 80 0.7633 nan 0.1000 -0.0015
## 100 0.7376 nan 0.1000 -0.0007
## 120 0.7086 nan 0.1000 -0.0021
## 140 0.6867 nan 0.1000 -0.0014
## 160 0.6602 nan 0.1000 -0.0014
## 180 0.6448 nan 0.1000 -0.0018
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## 220 0.6082 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2377 nan 0.1000 0.0252
## 2 1.1967 nan 0.1000 0.0178
## 3 1.1537 nan 0.1000 0.0180
## 4 1.1194 nan 0.1000 0.0149
## 5 1.0921 nan 0.1000 0.0118
## 6 1.0638 nan 0.1000 0.0099
## 7 1.0387 nan 0.1000 0.0100
## 8 1.0168 nan 0.1000 0.0076
## 9 1.0000 nan 0.1000 0.0054
## 10 0.9816 nan 0.1000 0.0051
## 20 0.8898 nan 0.1000 -0.0003
## 40 0.7955 nan 0.1000 -0.0010
## 60 0.7385 nan 0.1000 -0.0006
## 80 0.6923 nan 0.1000 -0.0020
## 100 0.6488 nan 0.1000 -0.0016
## 120 0.6139 nan 0.1000 -0.0018
## 140 0.5792 nan 0.1000 -0.0024
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## 200 0.5030 nan 0.1000 -0.0013
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## 280 0.4198 nan 0.1000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2429 nan 0.1000 0.0247
## 2 1.1981 nan 0.1000 0.0202
## 3 1.1535 nan 0.1000 0.0179
## 4 1.1190 nan 0.1000 0.0112
## 5 1.0890 nan 0.1000 0.0127
## 6 1.0698 nan 0.1000 0.0081
## 7 1.0477 nan 0.1000 0.0089
## 8 1.0291 nan 0.1000 0.0057
## 9 1.0128 nan 0.1000 0.0044
## 10 0.9951 nan 0.1000 0.0069
## 20 0.8904 nan 0.1000 0.0020
## 40 0.7967 nan 0.1000 -0.0003
## 60 0.7392 nan 0.1000 -0.0030
## 80 0.6937 nan 0.1000 -0.0016
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## 120 0.6231 nan 0.1000 -0.0007
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## 180 0.5300 nan 0.1000 -0.0008
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## 260 0.4369 nan 0.1000 -0.0013
## 280 0.4162 nan 0.1000 -0.0005
## 300 0.4012 nan 0.1000 -0.0009
## 320 0.3828 nan 0.1000 -0.0013
## 340 0.3681 nan 0.1000 -0.0003
## 360 0.3532 nan 0.1000 -0.0009
## 380 0.3391 nan 0.1000 -0.0005
## 400 0.3238 nan 0.1000 -0.0006
## 420 0.3103 nan 0.1000 -0.0004
## 440 0.2995 nan 0.1000 -0.0004
## 460 0.2895 nan 0.1000 -0.0007
## 480 0.2804 nan 0.1000 -0.0007
## 500 0.2705 nan 0.1000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2425 nan 0.1000 0.0183
## 2 1.1976 nan 0.1000 0.0169
## 3 1.1576 nan 0.1000 0.0157
## 4 1.1216 nan 0.1000 0.0152
## 5 1.0887 nan 0.1000 0.0144
## 6 1.0628 nan 0.1000 0.0098
## 7 1.0410 nan 0.1000 0.0071
## 8 1.0201 nan 0.1000 0.0072
## 9 1.0018 nan 0.1000 0.0069
## 10 0.9851 nan 0.1000 0.0054
## 20 0.8805 nan 0.1000 0.0011
## 40 0.7818 nan 0.1000 -0.0006
## 60 0.7208 nan 0.1000 0.0001
## 80 0.6762 nan 0.1000 -0.0000
## 100 0.6389 nan 0.1000 -0.0009
## 120 0.6040 nan 0.1000 -0.0014
## 140 0.5758 nan 0.1000 -0.0014
## 160 0.5475 nan 0.1000 -0.0007
## 180 0.5223 nan 0.1000 -0.0006
## 200 0.4971 nan 0.1000 -0.0015
## 220 0.4739 nan 0.1000 -0.0004
## 240 0.4549 nan 0.1000 -0.0011
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## 280 0.4159 nan 0.1000 -0.0009
## 300 0.3979 nan 0.1000 -0.0012
## 320 0.3817 nan 0.1000 -0.0006
## 340 0.3632 nan 0.1000 -0.0015
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## 380 0.3355 nan 0.1000 -0.0009
## 400 0.3231 nan 0.1000 -0.0013
## 420 0.3087 nan 0.1000 -0.0007
## 440 0.2970 nan 0.1000 -0.0012
## 460 0.2853 nan 0.1000 -0.0017
## 480 0.2759 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2342 nan 0.2000 0.0303
## 2 1.1889 nan 0.2000 0.0219
## 3 1.1481 nan 0.2000 0.0166
## 4 1.1128 nan 0.2000 0.0140
## 5 1.0821 nan 0.2000 0.0110
## 6 1.0625 nan 0.2000 0.0088
## 7 1.0380 nan 0.2000 0.0069
## 8 1.0206 nan 0.2000 0.0055
## 9 1.0067 nan 0.2000 0.0059
## 10 0.9948 nan 0.2000 0.0026
## 20 0.9186 nan 0.2000 -0.0007
## 40 0.8544 nan 0.2000 0.0017
## 60 0.8230 nan 0.2000 -0.0052
## 80 0.8051 nan 0.2000 -0.0004
## 100 0.7782 nan 0.2000 -0.0024
## 120 0.7639 nan 0.2000 -0.0020
## 140 0.7498 nan 0.2000 -0.0014
## 160 0.7382 nan 0.2000 -0.0019
## 180 0.7231 nan 0.2000 -0.0019
## 200 0.7091 nan 0.2000 -0.0032
## 220 0.6973 nan 0.2000 -0.0031
## 240 0.6883 nan 0.2000 -0.0014
## 260 0.6805 nan 0.2000 -0.0017
## 280 0.6703 nan 0.2000 -0.0014
## 300 0.6610 nan 0.2000 -0.0016
## 320 0.6545 nan 0.2000 -0.0004
## 340 0.6472 nan 0.2000 -0.0022
## 360 0.6413 nan 0.2000 -0.0014
## 380 0.6353 nan 0.2000 -0.0020
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2271 nan 0.2000 0.0330
## 2 1.1735 nan 0.2000 0.0232
## 3 1.1437 nan 0.2000 0.0142
## 4 1.1133 nan 0.2000 0.0122
## 5 1.0862 nan 0.2000 0.0108
## 6 1.0645 nan 0.2000 0.0084
## 7 1.0440 nan 0.2000 0.0082
## 8 1.0217 nan 0.2000 0.0065
## 9 1.0065 nan 0.2000 0.0041
## 10 0.9956 nan 0.2000 -0.0001
## 20 0.9141 nan 0.2000 -0.0001
## 40 0.8418 nan 0.2000 -0.0011
## 60 0.8051 nan 0.2000 -0.0014
## 80 0.7852 nan 0.2000 -0.0025
## 100 0.7699 nan 0.2000 -0.0019
## 120 0.7558 nan 0.2000 -0.0025
## 140 0.7436 nan 0.2000 -0.0013
## 160 0.7316 nan 0.2000 -0.0019
## 180 0.7161 nan 0.2000 -0.0019
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## 220 0.6951 nan 0.2000 -0.0018
## 240 0.6834 nan 0.2000 -0.0002
## 260 0.6742 nan 0.2000 -0.0010
## 280 0.6651 nan 0.2000 -0.0014
## 300 0.6596 nan 0.2000 -0.0021
## 320 0.6524 nan 0.2000 -0.0012
## 340 0.6422 nan 0.2000 -0.0002
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## 380 0.6299 nan 0.2000 -0.0010
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## 440 0.6141 nan 0.2000 -0.0038
## 460 0.6071 nan 0.2000 -0.0029
## 480 0.6029 nan 0.2000 -0.0018
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2261 nan 0.2000 0.0313
## 2 1.1728 nan 0.2000 0.0236
## 3 1.1374 nan 0.2000 0.0171
## 4 1.1105 nan 0.2000 0.0144
## 5 1.0864 nan 0.2000 0.0088
## 6 1.0657 nan 0.2000 0.0076
## 7 1.0461 nan 0.2000 0.0060
## 8 1.0275 nan 0.2000 0.0053
## 9 1.0172 nan 0.2000 0.0015
## 10 1.0026 nan 0.2000 0.0066
## 20 0.9243 nan 0.2000 0.0017
## 40 0.8515 nan 0.2000 -0.0025
## 60 0.8163 nan 0.2000 -0.0015
## 80 0.7917 nan 0.2000 -0.0006
## 100 0.7732 nan 0.2000 -0.0012
## 120 0.7565 nan 0.2000 -0.0015
## 140 0.7444 nan 0.2000 -0.0016
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## 180 0.7244 nan 0.2000 -0.0020
## 200 0.7077 nan 0.2000 -0.0010
## 220 0.6962 nan 0.2000 -0.0014
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## 280 0.6668 nan 0.2000 -0.0039
## 300 0.6595 nan 0.2000 -0.0016
## 320 0.6516 nan 0.2000 -0.0027
## 340 0.6450 nan 0.2000 -0.0014
## 360 0.6358 nan 0.2000 0.0002
## 380 0.6310 nan 0.2000 -0.0012
## 400 0.6228 nan 0.2000 -0.0010
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## 440 0.6113 nan 0.2000 -0.0004
## 460 0.6050 nan 0.2000 -0.0017
## 480 0.5968 nan 0.2000 -0.0018
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2056 nan 0.2000 0.0404
## 2 1.1417 nan 0.2000 0.0279
## 3 1.0956 nan 0.2000 0.0192
## 4 1.0511 nan 0.2000 0.0173
## 5 1.0207 nan 0.2000 0.0126
## 6 0.9988 nan 0.2000 0.0067
## 7 0.9794 nan 0.2000 0.0045
## 8 0.9593 nan 0.2000 0.0045
## 9 0.9440 nan 0.2000 0.0017
## 10 0.9277 nan 0.2000 0.0044
## 20 0.8449 nan 0.2000 -0.0001
## 40 0.7563 nan 0.2000 -0.0018
## 60 0.7028 nan 0.2000 -0.0013
## 80 0.6657 nan 0.2000 -0.0020
## 100 0.6219 nan 0.2000 -0.0034
## 120 0.5809 nan 0.2000 -0.0010
## 140 0.5547 nan 0.2000 -0.0035
## 160 0.5246 nan 0.2000 -0.0035
## 180 0.4958 nan 0.2000 -0.0008
## 200 0.4695 nan 0.2000 -0.0008
## 220 0.4470 nan 0.2000 -0.0022
## 240 0.4396 nan 0.2000 -0.0003
## 260 0.4190 nan 0.2000 -0.0040
## 280 0.3994 nan 0.2000 -0.0004
## 300 0.3845 nan 0.2000 -0.0014
## 320 0.3697 nan 0.2000 -0.0030
## 340 0.3532 nan 0.2000 -0.0007
## 360 0.3395 nan 0.2000 -0.0015
## 380 0.3254 nan 0.2000 -0.0007
## 400 0.3147 nan 0.2000 -0.0017
## 420 0.3041 nan 0.2000 -0.0008
## 440 0.2912 nan 0.2000 -0.0006
## 460 0.2791 nan 0.2000 -0.0023
## 480 0.2680 nan 0.2000 -0.0017
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2022 nan 0.2000 0.0404
## 2 1.1346 nan 0.2000 0.0298
## 3 1.0872 nan 0.2000 0.0206
## 4 1.0559 nan 0.2000 0.0080
## 5 1.0254 nan 0.2000 0.0136
## 6 0.9968 nan 0.2000 0.0084
## 7 0.9744 nan 0.2000 0.0053
## 8 0.9612 nan 0.2000 0.0037
## 9 0.9440 nan 0.2000 0.0036
## 10 0.9294 nan 0.2000 0.0036
## 20 0.8431 nan 0.2000 -0.0005
## 40 0.7631 nan 0.2000 -0.0025
## 60 0.7175 nan 0.2000 -0.0008
## 80 0.6628 nan 0.2000 -0.0031
## 100 0.6316 nan 0.2000 -0.0032
## 120 0.5892 nan 0.2000 -0.0014
## 140 0.5538 nan 0.2000 -0.0025
## 160 0.5331 nan 0.2000 -0.0036
## 180 0.5117 nan 0.2000 -0.0014
## 200 0.4872 nan 0.2000 -0.0017
## 220 0.4668 nan 0.2000 -0.0024
## 240 0.4487 nan 0.2000 -0.0020
## 260 0.4313 nan 0.2000 -0.0007
## 280 0.4079 nan 0.2000 -0.0006
## 300 0.3984 nan 0.2000 -0.0016
## 320 0.3729 nan 0.2000 -0.0000
## 340 0.3566 nan 0.2000 -0.0002
## 360 0.3451 nan 0.2000 -0.0008
## 380 0.3289 nan 0.2000 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2021 nan 0.2000 0.0368
## 2 1.1449 nan 0.2000 0.0259
## 3 1.0885 nan 0.2000 0.0296
## 4 1.0574 nan 0.2000 0.0123
## 5 1.0240 nan 0.2000 0.0121
## 6 0.9980 nan 0.2000 0.0106
## 7 0.9817 nan 0.2000 0.0036
## 8 0.9629 nan 0.2000 0.0072
## 9 0.9488 nan 0.2000 -0.0005
## 10 0.9322 nan 0.2000 0.0060
## 20 0.8384 nan 0.2000 -0.0008
## 40 0.7546 nan 0.2000 -0.0010
## 60 0.7046 nan 0.2000 -0.0006
## 80 0.6562 nan 0.2000 -0.0020
## 100 0.6215 nan 0.2000 -0.0020
## 120 0.5862 nan 0.2000 -0.0023
## 140 0.5502 nan 0.2000 -0.0013
## 160 0.5213 nan 0.2000 -0.0058
## 180 0.4955 nan 0.2000 -0.0036
## 200 0.4715 nan 0.2000 -0.0019
## 220 0.4548 nan 0.2000 -0.0018
## 240 0.4336 nan 0.2000 -0.0017
## 260 0.4145 nan 0.2000 -0.0015
## 280 0.3979 nan 0.2000 -0.0024
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## 320 0.3627 nan 0.2000 -0.0018
## 340 0.3474 nan 0.2000 -0.0023
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## 380 0.3184 nan 0.2000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2047 nan 0.2000 0.0381
## 2 1.1292 nan 0.2000 0.0325
## 3 1.0778 nan 0.2000 0.0218
## 4 1.0293 nan 0.2000 0.0195
## 5 0.9941 nan 0.2000 0.0144
## 6 0.9695 nan 0.2000 0.0065
## 7 0.9414 nan 0.2000 0.0088
## 8 0.9254 nan 0.2000 -0.0021
## 9 0.9099 nan 0.2000 0.0028
## 10 0.8945 nan 0.2000 0.0036
## 20 0.8065 nan 0.2000 -0.0020
## 40 0.7178 nan 0.2000 -0.0025
## 60 0.6430 nan 0.2000 -0.0016
## 80 0.5879 nan 0.2000 -0.0016
## 100 0.5324 nan 0.2000 -0.0033
## 120 0.4856 nan 0.2000 -0.0018
## 140 0.4368 nan 0.2000 -0.0027
## 160 0.3976 nan 0.2000 -0.0018
## 180 0.3682 nan 0.2000 -0.0026
## 200 0.3419 nan 0.2000 -0.0015
## 220 0.3183 nan 0.2000 -0.0019
## 240 0.2997 nan 0.2000 -0.0031
## 260 0.2783 nan 0.2000 -0.0008
## 280 0.2591 nan 0.2000 -0.0021
## 300 0.2409 nan 0.2000 -0.0007
## 320 0.2232 nan 0.2000 -0.0004
## 340 0.2077 nan 0.2000 -0.0010
## 360 0.1953 nan 0.2000 -0.0010
## 380 0.1821 nan 0.2000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1957 nan 0.2000 0.0434
## 2 1.1248 nan 0.2000 0.0273
## 3 1.0665 nan 0.2000 0.0230
## 4 1.0327 nan 0.2000 0.0116
## 5 0.9965 nan 0.2000 0.0121
## 6 0.9613 nan 0.2000 0.0105
## 7 0.9402 nan 0.2000 0.0059
## 8 0.9205 nan 0.2000 0.0037
## 9 0.9065 nan 0.2000 0.0050
## 10 0.8886 nan 0.2000 0.0045
## 20 0.7871 nan 0.2000 -0.0013
## 40 0.6882 nan 0.2000 -0.0017
## 60 0.6150 nan 0.2000 -0.0024
## 80 0.5622 nan 0.2000 -0.0047
## 100 0.5128 nan 0.2000 -0.0025
## 120 0.4763 nan 0.2000 -0.0040
## 140 0.4354 nan 0.2000 -0.0026
## 160 0.3960 nan 0.2000 -0.0033
## 180 0.3649 nan 0.2000 -0.0009
## 200 0.3358 nan 0.2000 -0.0020
## 220 0.3102 nan 0.2000 -0.0008
## 240 0.2868 nan 0.2000 -0.0008
## 260 0.2679 nan 0.2000 -0.0010
## 280 0.2439 nan 0.2000 -0.0011
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## 320 0.2095 nan 0.2000 -0.0011
## 340 0.1944 nan 0.2000 -0.0005
## 360 0.1838 nan 0.2000 -0.0007
## 380 0.1711 nan 0.2000 -0.0010
## 400 0.1598 nan 0.2000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1999 nan 0.2000 0.0432
## 2 1.1306 nan 0.2000 0.0270
## 3 1.0734 nan 0.2000 0.0288
## 4 1.0361 nan 0.2000 0.0112
## 5 0.9940 nan 0.2000 0.0163
## 6 0.9676 nan 0.2000 0.0078
## 7 0.9445 nan 0.2000 0.0052
## 8 0.9302 nan 0.2000 0.0018
## 9 0.9166 nan 0.2000 0.0004
## 10 0.9022 nan 0.2000 0.0021
## 20 0.8005 nan 0.2000 0.0002
## 40 0.7011 nan 0.2000 -0.0015
## 60 0.6126 nan 0.2000 -0.0030
## 80 0.5597 nan 0.2000 -0.0002
## 100 0.5131 nan 0.2000 -0.0029
## 120 0.4695 nan 0.2000 -0.0014
## 140 0.4275 nan 0.2000 -0.0009
## 160 0.3955 nan 0.2000 -0.0013
## 180 0.3625 nan 0.2000 -0.0010
## 200 0.3331 nan 0.2000 -0.0011
## 220 0.3106 nan 0.2000 -0.0009
## 240 0.2890 nan 0.2000 -0.0025
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## 280 0.2457 nan 0.2000 -0.0015
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## 320 0.2132 nan 0.2000 -0.0004
## 340 0.2014 nan 0.2000 -0.0013
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## 380 0.1761 nan 0.2000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1982 nan 0.3000 0.0458
## 2 1.1458 nan 0.3000 0.0116
## 3 1.0941 nan 0.3000 0.0145
## 4 1.0474 nan 0.3000 0.0212
## 5 1.0176 nan 0.3000 0.0115
## 6 1.0033 nan 0.3000 -0.0005
## 7 0.9832 nan 0.3000 0.0043
## 8 0.9615 nan 0.3000 0.0098
## 9 0.9496 nan 0.3000 0.0036
## 10 0.9404 nan 0.3000 0.0006
## 20 0.8710 nan 0.3000 -0.0001
## 40 0.8067 nan 0.3000 -0.0055
## 60 0.7754 nan 0.3000 -0.0043
## 80 0.7548 nan 0.3000 -0.0036
## 100 0.7351 nan 0.3000 -0.0002
## 120 0.7133 nan 0.3000 -0.0024
## 140 0.6981 nan 0.3000 -0.0030
## 160 0.6813 nan 0.3000 -0.0010
## 180 0.6657 nan 0.3000 -0.0012
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## 220 0.6458 nan 0.3000 -0.0030
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## 280 0.6168 nan 0.3000 -0.0026
## 300 0.6069 nan 0.3000 -0.0036
## 320 0.6038 nan 0.3000 -0.0034
## 340 0.5905 nan 0.3000 -0.0012
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## 380 0.5802 nan 0.3000 -0.0014
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## 460 0.5462 nan 0.3000 -0.0016
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1884 nan 0.3000 0.0396
## 2 1.1432 nan 0.3000 0.0172
## 3 1.0945 nan 0.3000 0.0211
## 4 1.0619 nan 0.3000 0.0106
## 5 1.0201 nan 0.3000 0.0139
## 6 0.9974 nan 0.3000 0.0048
## 7 0.9821 nan 0.3000 0.0040
## 8 0.9623 nan 0.3000 0.0041
## 9 0.9493 nan 0.3000 0.0021
## 10 0.9415 nan 0.3000 0.0012
## 20 0.8771 nan 0.3000 -0.0015
## 40 0.8162 nan 0.3000 -0.0032
## 60 0.7802 nan 0.3000 -0.0017
## 80 0.7540 nan 0.3000 -0.0036
## 100 0.7332 nan 0.3000 -0.0046
## 120 0.7106 nan 0.3000 -0.0030
## 140 0.6992 nan 0.3000 -0.0013
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## 180 0.6800 nan 0.3000 -0.0016
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## 220 0.6613 nan 0.3000 -0.0023
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## 280 0.6196 nan 0.3000 -0.0026
## 300 0.6131 nan 0.3000 -0.0034
## 320 0.6063 nan 0.3000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1889 nan 0.3000 0.0488
## 2 1.1321 nan 0.3000 0.0244
## 3 1.0939 nan 0.3000 0.0182
## 4 1.0641 nan 0.3000 0.0086
## 5 1.0357 nan 0.3000 0.0087
## 6 1.0118 nan 0.3000 0.0086
## 7 0.9935 nan 0.3000 0.0042
## 8 0.9795 nan 0.3000 0.0024
## 9 0.9652 nan 0.3000 0.0030
## 10 0.9506 nan 0.3000 0.0060
## 20 0.8808 nan 0.3000 -0.0032
## 40 0.8246 nan 0.3000 -0.0037
## 60 0.7949 nan 0.3000 -0.0010
## 80 0.7746 nan 0.3000 -0.0051
## 100 0.7524 nan 0.3000 -0.0026
## 120 0.7307 nan 0.3000 -0.0013
## 140 0.7142 nan 0.3000 -0.0022
## 160 0.6994 nan 0.3000 0.0005
## 180 0.6905 nan 0.3000 -0.0010
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## 240 0.6563 nan 0.3000 -0.0022
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## 460 0.5667 nan 0.3000 -0.0037
## 480 0.5574 nan 0.3000 -0.0033
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1784 nan 0.3000 0.0557
## 2 1.0963 nan 0.3000 0.0303
## 3 1.0403 nan 0.3000 0.0204
## 4 0.9965 nan 0.3000 0.0188
## 5 0.9642 nan 0.3000 0.0129
## 6 0.9427 nan 0.3000 0.0032
## 7 0.9205 nan 0.3000 0.0078
## 8 0.8963 nan 0.3000 0.0024
## 9 0.8824 nan 0.3000 0.0016
## 10 0.8686 nan 0.3000 0.0025
## 20 0.8057 nan 0.3000 -0.0061
## 40 0.7046 nan 0.3000 -0.0009
## 60 0.6902 nan 0.3000 -0.0020
## 80 0.6386 nan 0.3000 -0.0049
## 100 0.5979 nan 0.3000 -0.0054
## 120 0.5333 nan 0.3000 -0.0014
## 140 0.4783 nan 0.3000 -0.0029
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## 180 0.4082 nan 0.3000 -0.0021
## 200 0.3799 nan 0.3000 -0.0028
## 220 inf nan 0.3000 nan
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## 320 inf nan 0.3000 -0.0030
## 340 inf nan 0.3000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1756 nan 0.3000 0.0559
## 2 1.1069 nan 0.3000 0.0220
## 3 1.0565 nan 0.3000 0.0212
## 4 1.0208 nan 0.3000 0.0073
## 5 0.9951 nan 0.3000 -0.0007
## 6 0.9573 nan 0.3000 0.0090
## 7 0.9286 nan 0.3000 0.0102
## 8 0.9087 nan 0.3000 0.0042
## 9 0.8837 nan 0.3000 0.0057
## 10 0.8747 nan 0.3000 -0.0013
## 20 0.8045 nan 0.3000 -0.0015
## 40 0.7238 nan 0.3000 -0.0037
## 60 0.6554 nan 0.3000 -0.0025
## 80 0.6072 nan 0.3000 -0.0031
## 100 0.5695 nan 0.3000 -0.0053
## 120 0.5382 nan 0.3000 -0.0061
## 140 0.4964 nan 0.3000 -0.0012
## 160 0.4623 nan 0.3000 -0.0023
## 180 0.4336 nan 0.3000 -0.0037
## 200 0.4072 nan 0.3000 -0.0025
## 220 0.3772 nan 0.3000 -0.0019
## 240 0.3746 nan 0.3000 -0.0016
## 260 0.3436 nan 0.3000 -0.0041
## 280 0.3168 nan 0.3000 -0.0037
## 300 0.2971 nan 0.3000 -0.0013
## 320 0.2657 nan 0.3000 -0.0032
## 340 0.2466 nan 0.3000 -0.0008
## 360 0.2370 nan 0.3000 -0.0020
## 380 0.2180 nan 0.3000 -0.0008
## 400 0.2085 nan 0.3000 -0.0012
## 420 0.1968 nan 0.3000 -0.0016
## 440 0.1826 nan 0.3000 -0.0018
## 460 0.1712 nan 0.3000 -0.0015
## 480 0.1627 nan 0.3000 -0.0012
## 500 0.1543 nan 0.3000 -0.0015
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1667 nan 0.3000 0.0561
## 2 1.0889 nan 0.3000 0.0379
## 3 1.0377 nan 0.3000 0.0190
## 4 1.0095 nan 0.3000 0.0058
## 5 0.9731 nan 0.3000 0.0144
## 6 0.9468 nan 0.3000 0.0068
## 7 0.9225 nan 0.3000 0.0038
## 8 0.9140 nan 0.3000 -0.0019
## 9 0.8980 nan 0.3000 0.0044
## 10 0.8837 nan 0.3000 0.0019
## 20 0.8092 nan 0.3000 -0.0047
## 40 0.6956 nan 0.3000 -0.0047
## 60 0.6539 nan 0.3000 -0.0039
## 80 0.6066 nan 0.3000 -0.0107
## 100 0.5662 nan 0.3000 -0.0029
## 120 0.5254 nan 0.3000 -0.0077
## 140 0.4948 nan 0.3000 -0.0029
## 160 0.4644 nan 0.3000 -0.0019
## 180 0.4214 nan 0.3000 -0.0049
## 200 0.3946 nan 0.3000 -0.0009
## 220 0.3699 nan 0.3000 -0.0021
## 240 0.3512 nan 0.3000 -0.0039
## 260 0.3255 nan 0.3000 -0.0026
## 280 0.3031 nan 0.3000 -0.0022
## 300 0.2898 nan 0.3000 -0.0008
## 320 0.2714 nan 0.3000 -0.0028
## 340 0.2571 nan 0.3000 -0.0017
## 360 0.2432 nan 0.3000 -0.0040
## 380 0.2281 nan 0.3000 -0.0003
## 400 0.2163 nan 0.3000 -0.0018
## 420 0.2038 nan 0.3000 -0.0018
## 440 0.1934 nan 0.3000 -0.0018
## 460 0.1814 nan 0.3000 -0.0022
## 480 0.1729 nan 0.3000 -0.0006
## 500 0.1654 nan 0.3000 -0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1390 nan 0.3000 0.0671
## 2 1.0581 nan 0.3000 0.0339
## 3 0.9992 nan 0.3000 0.0207
## 4 0.9632 nan 0.3000 0.0085
## 5 0.9283 nan 0.3000 0.0112
## 6 0.9136 nan 0.3000 -0.0066
## 7 0.8982 nan 0.3000 -0.0037
## 8 0.8787 nan 0.3000 0.0019
## 9 0.8664 nan 0.3000 -0.0013
## 10 0.8567 nan 0.3000 -0.0042
## 20 0.7494 nan 0.3000 -0.0096
## 40 0.6322 nan 0.3000 -0.0076
## 60 0.5301 nan 0.3000 -0.0059
## 80 0.4559 nan 0.3000 0.0002
## 100 0.4105 nan 0.3000 -0.0027
## 120 0.3556 nan 0.3000 -0.0019
## 140 0.3095 nan 0.3000 -0.0021
## 160 0.2822 nan 0.3000 -0.0020
## 180 0.2538 nan 0.3000 -0.0020
## 200 0.2305 nan 0.3000 -0.0022
## 220 0.2125 nan 0.3000 -0.0019
## 240 0.1910 nan 0.3000 -0.0029
## 260 0.1697 nan 0.3000 -0.0018
## 280 0.1526 nan 0.3000 -0.0016
## 300 0.1385 nan 0.3000 -0.0016
## 320 0.1258 nan 0.3000 -0.0013
## 340 0.1168 nan 0.3000 -0.0005
## 360 0.1049 nan 0.3000 -0.0008
## 380 0.0968 nan 0.3000 -0.0008
## 400 0.0891 nan 0.3000 -0.0011
## 420 0.0842 nan 0.3000 -0.0003
## 440 0.0772 nan 0.3000 -0.0006
## 460 0.0706 nan 0.3000 -0.0008
## 480 0.0657 nan 0.3000 -0.0013
## 500 0.0600 nan 0.3000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1572 nan 0.3000 0.0450
## 2 1.0806 nan 0.3000 0.0327
## 3 1.0234 nan 0.3000 0.0217
## 4 0.9743 nan 0.3000 0.0169
## 5 0.9401 nan 0.3000 0.0098
## 6 0.9066 nan 0.3000 0.0088
## 7 0.8876 nan 0.3000 0.0031
## 8 0.8718 nan 0.3000 0.0000
## 9 0.8566 nan 0.3000 -0.0004
## 10 0.8405 nan 0.3000 0.0017
## 20 0.7421 nan 0.3000 -0.0073
## 40 0.6234 nan 0.3000 -0.0012
## 60 0.5400 nan 0.3000 -0.0032
## 80 0.4757 nan 0.3000 -0.0067
## 100 0.4184 nan 0.3000 -0.0051
## 120 0.3823 nan 0.3000 -0.0043
## 140 0.3308 nan 0.3000 -0.0009
## 160 0.3009 nan 0.3000 -0.0008
## 180 0.2699 nan 0.3000 -0.0048
## 200 0.2345 nan 0.3000 -0.0034
## 220 0.2127 nan 0.3000 -0.0017
## 240 0.1963 nan 0.3000 -0.0011
## 260 0.1785 nan 0.3000 -0.0016
## 280 0.1636 nan 0.3000 -0.0018
## 300 0.1500 nan 0.3000 -0.0012
## 320 0.1371 nan 0.3000 -0.0006
## 340 0.1250 nan 0.3000 -0.0010
## 360 0.1125 nan 0.3000 -0.0013
## 380 0.1036 nan 0.3000 -0.0005
## 400 0.0944 nan 0.3000 -0.0005
## 420 0.0867 nan 0.3000 -0.0008
## 440 0.0798 nan 0.3000 -0.0000
## 460 0.0738 nan 0.3000 -0.0005
## 480 0.0679 nan 0.3000 -0.0006
## 500 0.0627 nan 0.3000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1388 nan 0.3000 0.0552
## 2 1.0591 nan 0.3000 0.0223
## 3 1.0066 nan 0.3000 0.0200
## 4 0.9669 nan 0.3000 0.0149
## 5 0.9235 nan 0.3000 0.0156
## 6 0.8977 nan 0.3000 0.0087
## 7 0.8860 nan 0.3000 -0.0017
## 8 0.8639 nan 0.3000 0.0044
## 9 0.8461 nan 0.3000 0.0023
## 10 0.8290 nan 0.3000 -0.0010
## 20 0.7489 nan 0.3000 -0.0070
## 40 0.6295 nan 0.3000 -0.0066
## 60 0.5440 nan 0.3000 -0.0003
## 80 0.4838 nan 0.3000 -0.0044
## 100 0.4321 nan 0.3000 -0.0054
## 120 0.3886 nan 0.3000 -0.0017
## 140 0.3312 nan 0.3000 -0.0023
## 160 0.2861 nan 0.3000 0.0003
## 180 0.2561 nan 0.3000 -0.0014
## 200 0.2268 nan 0.3000 -0.0011
## 220 0.2024 nan 0.3000 -0.0011
## 240 0.1852 nan 0.3000 -0.0005
## 260 0.1687 nan 0.3000 -0.0009
## 280 0.1550 nan 0.3000 -0.0012
## 300 0.1409 nan 0.3000 -0.0017
## 320 0.1258 nan 0.3000 -0.0016
## 340 0.1141 nan 0.3000 -0.0011
## 360 0.1021 nan 0.3000 -0.0003
## 380 0.0940 nan 0.3000 -0.0008
## 400 0.0875 nan 0.3000 -0.0003
## 420 0.0787 nan 0.3000 -0.0009
## 440 0.0732 nan 0.3000 -0.0011
## 460 0.0673 nan 0.3000 -0.0004
## 480 0.0614 nan 0.3000 -0.0005
## 500 0.0563 nan 0.3000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1469 nan 0.5000 0.0618
## 2 1.0905 nan 0.5000 0.0171
## 3 1.0374 nan 0.5000 0.0197
## 4 1.0133 nan 0.5000 0.0012
## 5 0.9682 nan 0.5000 0.0174
## 6 0.9586 nan 0.5000 0.0013
## 7 0.9480 nan 0.5000 -0.0025
## 8 0.9354 nan 0.5000 -0.0004
## 9 0.9139 nan 0.5000 0.0082
## 10 0.9090 nan 0.5000 -0.0019
## 20 0.8518 nan 0.5000 -0.0004
## 40 0.8049 nan 0.5000 -0.0071
## 60 0.7642 nan 0.5000 -0.0040
## 80 0.7356 nan 0.5000 -0.0046
## 100 0.7041 nan 0.5000 0.0001
## 120 0.6807 nan 0.5000 -0.0074
## 140 0.6604 nan 0.5000 -0.0022
## 160 0.6447 nan 0.5000 -0.0028
## 180 0.6284 nan 0.5000 -0.0063
## 200 0.6120 nan 0.5000 -0.0088
## 220 0.6007 nan 0.5000 -0.0041
## 240 0.5850 nan 0.5000 0.0008
## 260 0.5770 nan 0.5000 -0.0054
## 280 0.5656 nan 0.5000 0.0001
## 300 0.5444 nan 0.5000 -0.0009
## 320 0.5319 nan 0.5000 -0.0018
## 340 0.5234 nan 0.5000 -0.0070
## 360 0.5119 nan 0.5000 -0.0053
## 380 0.4957 nan 0.5000 -0.0015
## 400 0.4916 nan 0.5000 -0.0044
## 420 0.4937 nan 0.5000 -0.0042
## 440 0.4819 nan 0.5000 -0.0043
## 460 0.4780 nan 0.5000 -0.0055
## 480 0.4676 nan 0.5000 -0.0031
## 500 0.4678 nan 0.5000 -0.0020
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1510 nan 0.5000 0.0669
## 2 1.0830 nan 0.5000 0.0232
## 3 1.0355 nan 0.5000 0.0226
## 4 0.9862 nan 0.5000 0.0198
## 5 0.9712 nan 0.5000 0.0001
## 6 0.9488 nan 0.5000 -0.0003
## 7 0.9224 nan 0.5000 0.0079
## 8 0.9154 nan 0.5000 -0.0004
## 9 0.9009 nan 0.5000 0.0007
## 10 0.8933 nan 0.5000 0.0011
## 20 0.8263 nan 0.5000 0.0018
## 40 0.7903 nan 0.5000 -0.0049
## 60 0.7438 nan 0.5000 -0.0022
## 80 0.7296 nan 0.5000 -0.0045
## 100 0.6921 nan 0.5000 -0.0035
## 120 0.6721 nan 0.5000 -0.0045
## 140 1.1541 nan 0.5000 -0.0104
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1691 nan 0.5000 0.0576
## 2 1.0753 nan 0.5000 0.0309
## 3 1.0469 nan 0.5000 0.0011
## 4 0.9883 nan 0.5000 0.0251
## 5 0.9632 nan 0.5000 0.0104
## 6 0.9374 nan 0.5000 0.0081
## 7 0.9214 nan 0.5000 0.0020
## 8 0.9183 nan 0.5000 -0.0126
## 9 0.9071 nan 0.5000 -0.0078
## 10 0.9018 nan 0.5000 -0.0059
## 20 0.8355 nan 0.5000 -0.0030
## 40 0.7916 nan 0.5000 -0.0110
## 60 0.7508 nan 0.5000 -0.0035
## 80 0.7250 nan 0.5000 -0.0026
## 100 0.6974 nan 0.5000 0.0017
## 120 0.6694 nan 0.5000 -0.0017
## 140 0.6576 nan 0.5000 -0.0039
## 160 0.6463 nan 0.5000 -0.0018
## 180 0.6338 nan 0.5000 -0.0063
## 200 0.6255 nan 0.5000 -0.0056
## 220 0.6073 nan 0.5000 -0.0056
## 240 0.5984 nan 0.5000 -0.0095
## 260 0.5846 nan 0.5000 -0.0097
## 280 0.5633 nan 0.5000 -0.0038
## 300 0.5542 nan 0.5000 -0.0022
## 320 0.5454 nan 0.5000 -0.0020
## 340 0.5438 nan 0.5000 -0.0065
## 360 0.5264 nan 0.5000 -0.0048
## 380 0.5213 nan 0.5000 -0.0006
## 400 0.5112 nan 0.5000 -0.0020
## 420 0.4934 nan 0.5000 -0.0039
## 440 0.4934 nan 0.5000 -0.0021
## 460 0.4891 nan 0.5000 -0.0077
## 480 0.4937 nan 0.5000 -0.0080
## 500 0.4649 nan 0.5000 -0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1118 nan 0.5000 0.0805
## 2 1.0379 nan 0.5000 0.0273
## 3 0.9918 nan 0.5000 0.0128
## 4 0.9523 nan 0.5000 0.0097
## 5 0.9229 nan 0.5000 0.0098
## 6 0.8993 nan 0.5000 -0.0014
## 7 0.8902 nan 0.5000 -0.0065
## 8 0.8756 nan 0.5000 -0.0018
## 9 0.8641 nan 0.5000 -0.0114
## 10 0.8557 nan 0.5000 -0.0033
## 20 0.7781 nan 0.5000 -0.0124
## 40 0.6629 nan 0.5000 -0.0007
## 60 0.6018 nan 0.5000 -0.0017
## 80 0.5201 nan 0.5000 -0.0056
## 100 0.4694 nan 0.5000 -0.0069
## 120 0.4257 nan 0.5000 -0.0026
## 140 0.3877 nan 0.5000 -0.0009
## 160 0.3372 nan 0.5000 -0.0006
## 180 0.2960 nan 0.5000 -0.0024
## 200 0.2663 nan 0.5000 -0.0042
## 220 0.2466 nan 0.5000 0.0008
## 240 0.2201 nan 0.5000 -0.0026
## 260 0.1945 nan 0.5000 -0.0007
## 280 0.1728 nan 0.5000 -0.0017
## 300 0.1602 nan 0.5000 -0.0017
## 320 0.1477 nan 0.5000 -0.0022
## 340 0.1313 nan 0.5000 -0.0003
## 360 0.1231 nan 0.5000 -0.0013
## 380 0.1098 nan 0.5000 -0.0014
## 400 0.1022 nan 0.5000 -0.0013
## 420 0.0958 nan 0.5000 -0.0013
## 440 0.0886 nan 0.5000 -0.0020
## 460 0.0821 nan 0.5000 -0.0013
## 480 0.0767 nan 0.5000 -0.0009
## 500 0.0723 nan 0.5000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1150 nan 0.5000 0.0791
## 2 1.0194 nan 0.5000 0.0430
## 3 0.9747 nan 0.5000 0.0154
## 4 0.9415 nan 0.5000 0.0010
## 5 0.9197 nan 0.5000 -0.0062
## 6 0.8955 nan 0.5000 0.0032
## 7 0.8778 nan 0.5000 0.0032
## 8 0.8628 nan 0.5000 0.0003
## 9 0.8487 nan 0.5000 -0.0037
## 10 0.8361 nan 0.5000 -0.0069
## 20 0.7867 nan 0.5000 -0.0105
## 40 0.6898 nan 0.5000 -0.0127
## 60 0.6093 nan 0.5000 -0.0194
## 80 0.5321 nan 0.5000 -0.0056
## 100 0.4604 nan 0.5000 -0.0012
## 120 0.3973 nan 0.5000 -0.0049
## 140 0.3537 nan 0.5000 -0.0056
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1171 nan 0.5000 0.0835
## 2 1.0376 nan 0.5000 0.0328
## 3 1.0091 nan 0.5000 0.0011
## 4 0.9549 nan 0.5000 0.0131
## 5 0.9291 nan 0.5000 -0.0034
## 6 0.9103 nan 0.5000 -0.0034
## 7 0.8892 nan 0.5000 0.0051
## 8 0.8854 nan 0.5000 -0.0086
## 9 0.8738 nan 0.5000 -0.0035
## 10 0.8613 nan 0.5000 -0.0056
## 20 0.7800 nan 0.5000 -0.0137
## 40 0.6621 nan 0.5000 -0.0087
## 60 0.5910 nan 0.5000 -0.0144
## 80 0.5288 nan 0.5000 -0.0076
## 100 0.4680 nan 0.5000 0.0006
## 120 0.4220 nan 0.5000 -0.0032
## 140 0.4425 nan 0.5000 -0.0710
## 160 0.4065 nan 0.5000 -0.0041
## 180 0.3569 nan 0.5000 -0.0027
## 200 0.3203 nan 0.5000 -0.0057
## 220 0.2842 nan 0.5000 -0.0033
## 240 0.2478 nan 0.5000 -0.0048
## 260 0.2196 nan 0.5000 -0.0066
## 280 0.1995 nan 0.5000 -0.0005
## 300 0.1838 nan 0.5000 -0.0012
## 320 0.1641 nan 0.5000 -0.0026
## 340 0.1484 nan 0.5000 -0.0006
## 360 0.1389 nan 0.5000 -0.0031
## 380 0.1237 nan 0.5000 -0.0021
## 400 0.1132 nan 0.5000 -0.0015
## 420 0.1046 nan 0.5000 -0.0015
## 440 0.0970 nan 0.5000 -0.0020
## 460 0.0908 nan 0.5000 -0.0025
## 480 0.0835 nan 0.5000 -0.0012
## 500 0.0774 nan 0.5000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1060 nan 0.5000 0.0891
## 2 1.0061 nan 0.5000 0.0369
## 3 0.9588 nan 0.5000 0.0058
## 4 0.9234 nan 0.5000 0.0011
## 5 0.8937 nan 0.5000 -0.0042
## 6 0.8825 nan 0.5000 -0.0130
## 7 0.8568 nan 0.5000 -0.0008
## 8 0.8413 nan 0.5000 -0.0121
## 9 0.8257 nan 0.5000 -0.0124
## 10 0.8117 nan 0.5000 -0.0131
## 20 0.7607 nan 0.5000 -0.0173
## 40 inf nan 0.5000 nan
## 60 inf nan 0.5000 nan
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0913 nan 0.5000 0.0743
## 2 0.9914 nan 0.5000 0.0410
## 3 0.9623 nan 0.5000 -0.0015
## 4 0.9152 nan 0.5000 0.0007
## 5 0.9008 nan 0.5000 -0.0137
## 6 0.8639 nan 0.5000 0.0120
## 7 0.8419 nan 0.5000 0.0015
## 8 0.8321 nan 0.5000 -0.0168
## 9 0.8178 nan 0.5000 -0.0080
## 10 0.7985 nan 0.5000 0.0057
## 20 0.6865 nan 0.5000 -0.0060
## 40 0.5529 nan 0.5000 -0.0046
## 60 0.4816 nan 0.5000 -0.0041
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
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## 200 inf nan 0.5000 nan
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## 280 inf nan 0.5000 nan
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## 320 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0900 nan 0.5000 0.0996
## 2 0.9795 nan 0.5000 0.0486
## 3 0.9387 nan 0.5000 0.0055
## 4 0.9036 nan 0.5000 -0.0093
## 5 0.8663 nan 0.5000 0.0018
## 6 0.8419 nan 0.5000 -0.0017
## 7 0.8191 nan 0.5000 0.0020
## 8 0.8020 nan 0.5000 -0.0021
## 9 0.7910 nan 0.5000 -0.0074
## 10 0.7804 nan 0.5000 -0.0101
## 20 0.6923 nan 0.5000 -0.0018
## 40 0.5592 nan 0.5000 -0.0021
## 60 0.4489 nan 0.5000 -0.0067
## 80 0.3711 nan 0.5000 -0.0034
## 100 0.2980 nan 0.5000 -0.0047
## 120 0.2413 nan 0.5000 -0.0021
## 140 0.1938 nan 0.5000 -0.0041
## 160 0.1626 nan 0.5000 -0.0009
## 180 0.1349 nan 0.5000 -0.0023
## 200 0.1172 nan 0.5000 -0.0018
## 220 0.1018 nan 0.5000 -0.0006
## 240 0.0892 nan 0.5000 -0.0018
## 260 0.0798 nan 0.5000 -0.0022
## 280 0.0675 nan 0.5000 -0.0004
## 300 0.0572 nan 0.5000 -0.0019
## 320 0.0505 nan 0.5000 -0.0005
## 340 0.0436 nan 0.5000 -0.0004
## 360 0.0389 nan 0.5000 -0.0005
## 380 0.0348 nan 0.5000 -0.0002
## 400 0.0311 nan 0.5000 -0.0004
## 420 0.0282 nan 0.5000 -0.0003
## 440 0.0255 nan 0.5000 -0.0002
## 460 0.0216 nan 0.5000 -0.0001
## 480 0.0195 nan 0.5000 -0.0002
## 500 0.0172 nan 0.5000 -0.0004
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1158 nan 1.0000 0.0767
## 2 1.0423 nan 1.0000 0.0249
## 3 1.0053 nan 1.0000 0.0057
## 4 0.9769 nan 1.0000 -0.0043
## 5 0.9834 nan 1.0000 -0.0362
## 6 1.0110 nan 1.0000 -0.0540
## 7 0.9709 nan 1.0000 0.0160
## 8 0.9695 nan 1.0000 -0.0196
## 9 0.9623 nan 1.0000 -0.0137
## 10 0.9325 nan 1.0000 0.0095
## 20 0.8886 nan 1.0000 -0.0168
## 40 1.4646 nan 1.0000 -0.0306
## 60 1.3977 nan 1.0000 0.0058
## 80 1.3472 nan 1.0000 -0.0006
## 100 1.3348 nan 1.0000 -0.0125
## 120 1.3152 nan 1.0000 0.0013
## 140 1.3143 nan 1.0000 -0.0140
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1149 nan 1.0000 0.0731
## 2 1.0643 nan 1.0000 0.0125
## 3 1.0184 nan 1.0000 0.0143
## 4 1.0134 nan 1.0000 -0.0300
## 5 0.9858 nan 1.0000 -0.0040
## 6 1.0062 nan 1.0000 -0.0369
## 7 0.9838 nan 1.0000 -0.0014
## 8 0.9755 nan 1.0000 -0.0138
## 9 1.0046 nan 1.0000 -0.0494
## 10 1.2956 nan 1.0000 -0.3158
## 20 543.7399 nan 1.0000 0.0167
## 40 543.6978 nan 1.0000 -0.0065
## 60 548.5043 nan 1.0000 -0.0295
## 80 548.4274 nan 1.0000 -0.0070
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1186 nan 1.0000 0.0696
## 2 1.0604 nan 1.0000 0.0155
## 3 1.0580 nan 1.0000 -0.0166
## 4 1.0232 nan 1.0000 0.0148
## 5 1.0064 nan 1.0000 -0.0226
## 6 0.9997 nan 1.0000 -0.0242
## 7 0.9912 nan 1.0000 -0.0036
## 8 0.9763 nan 1.0000 0.0029
## 9 0.9669 nan 1.0000 0.0016
## 10 0.9627 nan 1.0000 -0.0108
## 20 0.8798 nan 1.0000 -0.0183
## 40 3.8492 nan 1.0000 -0.0173
## 60 7.0538 nan 1.0000 -0.0266
## 80 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0913 nan 1.0000 0.0640
## 2 1.0206 nan 1.0000 0.0001
## 3 1.0145 nan 1.0000 -0.0305
## 4 0.9507 nan 1.0000 0.0138
## 5 0.9261 nan 1.0000 -0.0264
## 6 0.8807 nan 1.0000 -0.0165
## 7 0.8575 nan 1.0000 -0.0131
## 8 0.9123 nan 1.0000 -0.0946
## 9 0.9078 nan 1.0000 -0.0160
## 10 0.9074 nan 1.0000 -0.0326
## 20 28774879976.9367 nan 1.0000 -0.1370
## 40 28789022483.0281 nan 1.0000 -0.0132
## 60 28789022483.0081 nan 1.0000 -0.0115
## 80 28789026460.9178 nan 1.0000 0.0000
## 100 28789026460.9149 nan 1.0000 -0.0015
## 120 28789026460.9338 nan 1.0000 -0.0268
## 140 28789026460.8627 nan 1.0000 -0.0320
## 160 28789026461.0191 nan 1.0000 -0.0407
## 180 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1027 nan 1.0000 0.0583
## 2 1.0129 nan 1.0000 0.0199
## 3 1.0044 nan 1.0000 -0.0423
## 4 1.0032 nan 1.0000 -0.0294
## 5 0.9675 nan 1.0000 -0.0057
## 6 0.9386 nan 1.0000 -0.0135
## 7 0.9273 nan 1.0000 -0.0046
## 8 0.9563 nan 1.0000 -0.0598
## 9 0.9242 nan 1.0000 0.0043
## 10 0.8994 nan 1.0000 0.0025
## 20 0.8700 nan 1.0000 -0.0379
## 40 2076618.0428 nan 1.0000 -0.0068
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0724 nan 1.0000 0.0825
## 2 0.9845 nan 1.0000 0.0284
## 3 0.9525 nan 1.0000 0.0058
## 4 0.9472 nan 1.0000 -0.0266
## 5 0.9349 nan 1.0000 -0.0226
## 6 0.9324 nan 1.0000 -0.0140
## 7 0.9142 nan 1.0000 -0.0040
## 8 0.8963 nan 1.0000 -0.0037
## 9 0.9036 nan 1.0000 -0.0263
## 10 0.8542 nan 1.0000 0.0050
## 20 32.9208 nan 1.0000 -31.9771
## 40 33.4190 nan 1.0000 -0.0538
## 60 21242.4558 nan 1.0000 -0.0000
## 80 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0452 nan 1.0000 0.0899
## 2 1.0353 nan 1.0000 -0.0466
## 3 0.9464 nan 1.0000 0.0336
## 4 0.9157 nan 1.0000 -0.0191
## 5 0.9503 nan 1.0000 -0.1008
## 6 0.9383 nan 1.0000 -0.0165
## 7 0.9199 nan 1.0000 -0.0147
## 8 0.9364 nan 1.0000 -0.0560
## 9 0.9241 nan 1.0000 -0.0143
## 10 1.0724 nan 1.0000 -0.2129
## 20 2.3101 nan 1.0000 -0.6892
## 40 169320.2191 nan 1.0000 -0.0134
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0272 nan 1.0000 0.0975
## 2 0.9336 nan 1.0000 0.0042
## 3 0.9058 nan 1.0000 -0.0146
## 4 0.9381 nan 1.0000 -0.0795
## 5 0.9098 nan 1.0000 -0.0230
## 6 0.9175 nan 1.0000 -0.0425
## 7 0.9140 nan 1.0000 -0.0534
## 8 1.1357 nan 1.0000 -0.3002
## 9 inf nan 1.0000 0.0071
## 10 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.9992 nan 1.0000 0.1452
## 2 0.9373 nan 1.0000 0.0194
## 3 0.9077 nan 1.0000 -0.0204
## 4 0.8816 nan 1.0000 -0.0191
## 5 0.8613 nan 1.0000 -0.0087
## 6 0.8440 nan 1.0000 -0.0126
## 7 0.8318 nan 1.0000 -0.0183
## 8 0.8075 nan 1.0000 -0.0160
## 9 0.8235 nan 1.0000 -0.0500
## 10 0.8208 nan 1.0000 -0.0260
## 20 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0001
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2905 nan 0.0010 0.0002
## 9 1.2902 nan 0.0010 0.0002
## 10 1.2898 nan 0.0010 0.0001
## 20 1.2864 nan 0.0010 0.0002
## 40 1.2795 nan 0.0010 0.0001
## 60 1.2728 nan 0.0010 0.0001
## 80 1.2665 nan 0.0010 0.0001
## 100 1.2603 nan 0.0010 0.0002
## 120 1.2544 nan 0.0010 0.0001
## 140 1.2486 nan 0.0010 0.0001
## 160 1.2431 nan 0.0010 0.0001
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## 380 1.1913 nan 0.0010 0.0001
## 400 1.1871 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2927 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0001
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2909 nan 0.0010 0.0002
## 8 1.2905 nan 0.0010 0.0002
## 9 1.2902 nan 0.0010 0.0001
## 10 1.2899 nan 0.0010 0.0002
## 20 1.2863 nan 0.0010 0.0002
## 40 1.2795 nan 0.0010 0.0001
## 60 1.2727 nan 0.0010 0.0002
## 80 1.2663 nan 0.0010 0.0001
## 100 1.2602 nan 0.0010 0.0001
## 120 1.2543 nan 0.0010 0.0001
## 140 1.2485 nan 0.0010 0.0001
## 160 1.2428 nan 0.0010 0.0001
## 180 1.2373 nan 0.0010 0.0001
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## 280 1.2128 nan 0.0010 0.0001
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## 380 1.1912 nan 0.0010 0.0001
## 400 1.1871 nan 0.0010 0.0001
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## 480 1.1720 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0001
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2909 nan 0.0010 0.0002
## 8 1.2905 nan 0.0010 0.0002
## 9 1.2902 nan 0.0010 0.0002
## 10 1.2899 nan 0.0010 0.0002
## 20 1.2863 nan 0.0010 0.0002
## 40 1.2794 nan 0.0010 0.0002
## 60 1.2728 nan 0.0010 0.0002
## 80 1.2662 nan 0.0010 0.0001
## 100 1.2600 nan 0.0010 0.0001
## 120 1.2541 nan 0.0010 0.0001
## 140 1.2482 nan 0.0010 0.0001
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## 180 1.2369 nan 0.0010 0.0001
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## 380 1.1908 nan 0.0010 0.0001
## 400 1.1867 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
## 9 1.2891 nan 0.0010 0.0002
## 10 1.2887 nan 0.0010 0.0002
## 20 1.2842 nan 0.0010 0.0002
## 40 1.2753 nan 0.0010 0.0002
## 60 1.2669 nan 0.0010 0.0002
## 80 1.2583 nan 0.0010 0.0002
## 100 1.2503 nan 0.0010 0.0002
## 120 1.2426 nan 0.0010 0.0002
## 140 1.2351 nan 0.0010 0.0002
## 160 1.2276 nan 0.0010 0.0002
## 180 1.2205 nan 0.0010 0.0001
## 200 1.2133 nan 0.0010 0.0002
## 220 1.2066 nan 0.0010 0.0001
## 240 1.1998 nan 0.0010 0.0001
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## 280 1.1870 nan 0.0010 0.0001
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## 380 1.1583 nan 0.0010 0.0001
## 400 1.1526 nan 0.0010 0.0001
## 420 1.1475 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2906 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
## 8 1.2897 nan 0.0010 0.0002
## 9 1.2892 nan 0.0010 0.0002
## 10 1.2887 nan 0.0010 0.0003
## 20 1.2841 nan 0.0010 0.0002
## 40 1.2751 nan 0.0010 0.0002
## 60 1.2665 nan 0.0010 0.0002
## 80 1.2580 nan 0.0010 0.0002
## 100 1.2499 nan 0.0010 0.0002
## 120 1.2419 nan 0.0010 0.0002
## 140 1.2342 nan 0.0010 0.0002
## 160 1.2269 nan 0.0010 0.0001
## 180 1.2198 nan 0.0010 0.0002
## 200 1.2129 nan 0.0010 0.0001
## 220 1.2061 nan 0.0010 0.0001
## 240 1.1995 nan 0.0010 0.0002
## 260 1.1930 nan 0.0010 0.0001
## 280 1.1870 nan 0.0010 0.0001
## 300 1.1809 nan 0.0010 0.0001
## 320 1.1750 nan 0.0010 0.0001
## 340 1.1693 nan 0.0010 0.0002
## 360 1.1636 nan 0.0010 0.0001
## 380 1.1583 nan 0.0010 0.0001
## 400 1.1529 nan 0.0010 0.0001
## 420 1.1478 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2911 nan 0.0010 0.0002
## 6 1.2906 nan 0.0010 0.0002
## 7 1.2902 nan 0.0010 0.0002
## 8 1.2898 nan 0.0010 0.0002
## 9 1.2893 nan 0.0010 0.0002
## 10 1.2889 nan 0.0010 0.0002
## 20 1.2842 nan 0.0010 0.0002
## 40 1.2753 nan 0.0010 0.0002
## 60 1.2666 nan 0.0010 0.0002
## 80 1.2580 nan 0.0010 0.0002
## 100 1.2500 nan 0.0010 0.0002
## 120 1.2421 nan 0.0010 0.0002
## 140 1.2342 nan 0.0010 0.0002
## 160 1.2266 nan 0.0010 0.0001
## 180 1.2193 nan 0.0010 0.0002
## 200 1.2124 nan 0.0010 0.0002
## 220 1.2055 nan 0.0010 0.0002
## 240 1.1988 nan 0.0010 0.0002
## 260 1.1925 nan 0.0010 0.0001
## 280 1.1861 nan 0.0010 0.0001
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## 320 1.1741 nan 0.0010 0.0001
## 340 1.1684 nan 0.0010 0.0001
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## 380 1.1571 nan 0.0010 0.0001
## 400 1.1517 nan 0.0010 0.0001
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## 460 1.1368 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0003
## 4 1.2912 nan 0.0010 0.0003
## 5 1.2906 nan 0.0010 0.0003
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0003
## 8 1.2890 nan 0.0010 0.0003
## 9 1.2885 nan 0.0010 0.0003
## 10 1.2880 nan 0.0010 0.0003
## 20 1.2826 nan 0.0010 0.0002
## 40 1.2725 nan 0.0010 0.0002
## 60 1.2626 nan 0.0010 0.0002
## 80 1.2530 nan 0.0010 0.0002
## 100 1.2438 nan 0.0010 0.0002
## 120 1.2347 nan 0.0010 0.0002
## 140 1.2258 nan 0.0010 0.0002
## 160 1.2174 nan 0.0010 0.0002
## 180 1.2091 nan 0.0010 0.0002
## 200 1.2011 nan 0.0010 0.0002
## 220 1.1933 nan 0.0010 0.0002
## 240 1.1857 nan 0.0010 0.0002
## 260 1.1782 nan 0.0010 0.0001
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## 320 1.1572 nan 0.0010 0.0002
## 340 1.1507 nan 0.0010 0.0001
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## 380 1.1379 nan 0.0010 0.0001
## 400 1.1318 nan 0.0010 0.0001
## 420 1.1258 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0003
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0002
## 8 1.2891 nan 0.0010 0.0003
## 9 1.2885 nan 0.0010 0.0002
## 10 1.2879 nan 0.0010 0.0003
## 20 1.2828 nan 0.0010 0.0002
## 40 1.2723 nan 0.0010 0.0002
## 60 1.2624 nan 0.0010 0.0002
## 80 1.2526 nan 0.0010 0.0002
## 100 1.2432 nan 0.0010 0.0002
## 120 1.2344 nan 0.0010 0.0002
## 140 1.2259 nan 0.0010 0.0002
## 160 1.2173 nan 0.0010 0.0002
## 180 1.2091 nan 0.0010 0.0001
## 200 1.2009 nan 0.0010 0.0001
## 220 1.1931 nan 0.0010 0.0002
## 240 1.1856 nan 0.0010 0.0002
## 260 1.1781 nan 0.0010 0.0001
## 280 1.1710 nan 0.0010 0.0001
## 300 1.1642 nan 0.0010 0.0002
## 320 1.1576 nan 0.0010 0.0002
## 340 1.1507 nan 0.0010 0.0001
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## 380 1.1378 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2900 nan 0.0010 0.0003
## 7 1.2895 nan 0.0010 0.0002
## 8 1.2889 nan 0.0010 0.0003
## 9 1.2884 nan 0.0010 0.0002
## 10 1.2879 nan 0.0010 0.0003
## 20 1.2826 nan 0.0010 0.0002
## 40 1.2724 nan 0.0010 0.0002
## 60 1.2623 nan 0.0010 0.0002
## 80 1.2525 nan 0.0010 0.0002
## 100 1.2431 nan 0.0010 0.0002
## 120 1.2340 nan 0.0010 0.0002
## 140 1.2252 nan 0.0010 0.0002
## 160 1.2168 nan 0.0010 0.0002
## 180 1.2086 nan 0.0010 0.0002
## 200 1.2005 nan 0.0010 0.0002
## 220 1.1930 nan 0.0010 0.0002
## 240 1.1854 nan 0.0010 0.0002
## 260 1.1781 nan 0.0010 0.0001
## 280 1.1709 nan 0.0010 0.0002
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## 320 1.1575 nan 0.0010 0.0001
## 340 1.1509 nan 0.0010 0.0001
## 360 1.1446 nan 0.0010 0.0001
## 380 1.1384 nan 0.0010 0.0001
## 400 1.1323 nan 0.0010 0.0002
## 420 1.1264 nan 0.0010 0.0001
## 440 1.1206 nan 0.0010 0.0001
## 460 1.1152 nan 0.0010 0.0001
## 480 1.1097 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2570 nan 0.1000 0.0164
## 2 1.2316 nan 0.1000 0.0123
## 3 1.2061 nan 0.1000 0.0095
## 4 1.1832 nan 0.1000 0.0101
## 5 1.1640 nan 0.1000 0.0066
## 6 1.1454 nan 0.1000 0.0074
## 7 1.1300 nan 0.1000 0.0071
## 8 1.1149 nan 0.1000 0.0055
## 9 1.1016 nan 0.1000 0.0055
## 10 1.0889 nan 0.1000 0.0051
## 20 1.0062 nan 0.1000 0.0013
## 40 0.9296 nan 0.1000 -0.0002
## 60 0.8844 nan 0.1000 -0.0005
## 80 0.8603 nan 0.1000 -0.0011
## 100 0.8415 nan 0.1000 -0.0003
## 120 0.8274 nan 0.1000 0.0000
## 140 0.8166 nan 0.1000 -0.0020
## 160 0.8073 nan 0.1000 -0.0006
## 180 0.7994 nan 0.1000 -0.0011
## 200 0.7915 nan 0.1000 0.0000
## 220 0.7870 nan 0.1000 -0.0013
## 240 0.7796 nan 0.1000 -0.0008
## 260 0.7721 nan 0.1000 -0.0005
## 280 0.7661 nan 0.1000 -0.0007
## 300 0.7590 nan 0.1000 -0.0010
## 320 0.7532 nan 0.1000 -0.0002
## 340 0.7475 nan 0.1000 -0.0008
## 360 0.7428 nan 0.1000 -0.0010
## 380 0.7384 nan 0.1000 -0.0003
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## 460 0.7172 nan 0.1000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2577 nan 0.1000 0.0164
## 2 1.2309 nan 0.1000 0.0123
## 3 1.2084 nan 0.1000 0.0084
## 4 1.1870 nan 0.1000 0.0087
## 5 1.1697 nan 0.1000 0.0070
## 6 1.1544 nan 0.1000 0.0064
## 7 1.1388 nan 0.1000 0.0071
## 8 1.1228 nan 0.1000 0.0071
## 9 1.1098 nan 0.1000 0.0045
## 10 1.0988 nan 0.1000 0.0021
## 20 1.0103 nan 0.1000 0.0009
## 40 0.9300 nan 0.1000 0.0014
## 60 0.8931 nan 0.1000 0.0001
## 80 0.8685 nan 0.1000 -0.0012
## 100 0.8499 nan 0.1000 -0.0008
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## 140 0.8234 nan 0.1000 -0.0014
## 160 0.8145 nan 0.1000 -0.0017
## 180 0.8058 nan 0.1000 -0.0009
## 200 0.7948 nan 0.1000 -0.0014
## 220 0.7864 nan 0.1000 -0.0004
## 240 0.7789 nan 0.1000 -0.0011
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## 280 0.7637 nan 0.1000 -0.0010
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## 320 0.7482 nan 0.1000 -0.0009
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## 460 0.7131 nan 0.1000 -0.0008
## 480 0.7086 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2615 nan 0.1000 0.0160
## 2 1.2332 nan 0.1000 0.0120
## 3 1.2142 nan 0.1000 0.0066
## 4 1.1872 nan 0.1000 0.0119
## 5 1.1681 nan 0.1000 0.0088
## 6 1.1522 nan 0.1000 0.0071
## 7 1.1430 nan 0.1000 0.0009
## 8 1.1280 nan 0.1000 0.0056
## 9 1.1179 nan 0.1000 0.0041
## 10 1.1037 nan 0.1000 0.0060
## 20 1.0089 nan 0.1000 0.0032
## 40 0.9346 nan 0.1000 -0.0005
## 60 0.8904 nan 0.1000 -0.0011
## 80 0.8689 nan 0.1000 -0.0006
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## 120 0.8353 nan 0.1000 -0.0007
## 140 0.8235 nan 0.1000 -0.0005
## 160 0.8144 nan 0.1000 -0.0019
## 180 0.8039 nan 0.1000 -0.0011
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## 220 0.7890 nan 0.1000 -0.0024
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## 280 0.7670 nan 0.1000 -0.0004
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## 320 0.7556 nan 0.1000 -0.0006
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## 460 0.7174 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2502 nan 0.1000 0.0221
## 2 1.2143 nan 0.1000 0.0167
## 3 1.1769 nan 0.1000 0.0158
## 4 1.1466 nan 0.1000 0.0141
## 5 1.1217 nan 0.1000 0.0108
## 6 1.1001 nan 0.1000 0.0082
## 7 1.0779 nan 0.1000 0.0070
## 8 1.0624 nan 0.1000 0.0063
## 9 1.0469 nan 0.1000 0.0046
## 10 1.0325 nan 0.1000 0.0039
## 20 0.9420 nan 0.1000 0.0006
## 40 0.8654 nan 0.1000 0.0003
## 60 0.8067 nan 0.1000 -0.0010
## 80 0.7714 nan 0.1000 -0.0027
## 100 0.7427 nan 0.1000 -0.0011
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## 140 0.6931 nan 0.1000 -0.0015
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## 180 0.6557 nan 0.1000 -0.0003
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## 220 0.6177 nan 0.1000 -0.0012
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## 380 0.4943 nan 0.1000 -0.0006
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## 480 0.4391 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2441 nan 0.1000 0.0199
## 2 1.2065 nan 0.1000 0.0166
## 3 1.1725 nan 0.1000 0.0160
## 4 1.1500 nan 0.1000 0.0111
## 5 1.1267 nan 0.1000 0.0084
## 6 1.1040 nan 0.1000 0.0100
## 7 1.0832 nan 0.1000 0.0087
## 8 1.0660 nan 0.1000 0.0062
## 9 1.0502 nan 0.1000 0.0030
## 10 1.0355 nan 0.1000 0.0071
## 20 0.9449 nan 0.1000 -0.0017
## 40 0.8623 nan 0.1000 -0.0019
## 60 0.8136 nan 0.1000 -0.0019
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## 180 0.6650 nan 0.1000 -0.0010
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## 460 0.4584 nan 0.1000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2467 nan 0.1000 0.0206
## 2 1.2121 nan 0.1000 0.0169
## 3 1.1794 nan 0.1000 0.0142
## 4 1.1529 nan 0.1000 0.0111
## 5 1.1292 nan 0.1000 0.0085
## 6 1.1056 nan 0.1000 0.0099
## 7 1.0863 nan 0.1000 0.0067
## 8 1.0673 nan 0.1000 0.0061
## 9 1.0501 nan 0.1000 0.0086
## 10 1.0386 nan 0.1000 0.0036
## 20 0.9391 nan 0.1000 0.0016
## 40 0.8520 nan 0.1000 -0.0009
## 60 0.8091 nan 0.1000 -0.0003
## 80 0.7789 nan 0.1000 -0.0010
## 100 0.7465 nan 0.1000 -0.0012
## 120 0.7216 nan 0.1000 -0.0015
## 140 0.7012 nan 0.1000 -0.0022
## 160 0.6825 nan 0.1000 -0.0008
## 180 0.6577 nan 0.1000 -0.0012
## 200 0.6389 nan 0.1000 -0.0016
## 220 0.6218 nan 0.1000 -0.0011
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## 260 0.5914 nan 0.1000 -0.0018
## 280 0.5757 nan 0.1000 -0.0023
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## 320 0.5429 nan 0.1000 -0.0010
## 340 0.5300 nan 0.1000 -0.0005
## 360 0.5143 nan 0.1000 -0.0011
## 380 0.5005 nan 0.1000 -0.0010
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## 460 0.4622 nan 0.1000 -0.0005
## 480 0.4507 nan 0.1000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2434 nan 0.1000 0.0238
## 2 1.2018 nan 0.1000 0.0168
## 3 1.1719 nan 0.1000 0.0132
## 4 1.1384 nan 0.1000 0.0122
## 5 1.1083 nan 0.1000 0.0109
## 6 1.0827 nan 0.1000 0.0088
## 7 1.0605 nan 0.1000 0.0076
## 8 1.0424 nan 0.1000 0.0063
## 9 1.0214 nan 0.1000 0.0074
## 10 1.0076 nan 0.1000 0.0049
## 20 0.9000 nan 0.1000 0.0011
## 40 0.8026 nan 0.1000 0.0017
## 60 0.7448 nan 0.1000 -0.0017
## 80 0.6950 nan 0.1000 -0.0011
## 100 0.6589 nan 0.1000 -0.0012
## 120 0.6292 nan 0.1000 -0.0013
## 140 0.5966 nan 0.1000 -0.0029
## 160 0.5656 nan 0.1000 -0.0005
## 180 0.5409 nan 0.1000 -0.0020
## 200 0.5107 nan 0.1000 -0.0010
## 220 0.4820 nan 0.1000 -0.0010
## 240 0.4610 nan 0.1000 -0.0001
## 260 0.4431 nan 0.1000 -0.0001
## 280 0.4244 nan 0.1000 -0.0005
## 300 0.4064 nan 0.1000 -0.0011
## 320 0.3880 nan 0.1000 -0.0006
## 340 0.3713 nan 0.1000 -0.0009
## 360 0.3548 nan 0.1000 -0.0006
## 380 0.3378 nan 0.1000 -0.0004
## 400 0.3252 nan 0.1000 -0.0007
## 420 0.3154 nan 0.1000 -0.0006
## 440 0.3015 nan 0.1000 -0.0004
## 460 0.2878 nan 0.1000 -0.0005
## 480 0.2786 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2430 nan 0.1000 0.0217
## 2 1.1952 nan 0.1000 0.0197
## 3 1.1535 nan 0.1000 0.0163
## 4 1.1187 nan 0.1000 0.0149
## 5 1.0948 nan 0.1000 0.0084
## 6 1.0734 nan 0.1000 0.0088
## 7 1.0546 nan 0.1000 0.0074
## 8 1.0350 nan 0.1000 0.0057
## 9 1.0165 nan 0.1000 0.0046
## 10 0.9985 nan 0.1000 0.0058
## 20 0.9005 nan 0.1000 0.0011
## 40 0.8051 nan 0.1000 -0.0006
## 60 0.7478 nan 0.1000 -0.0012
## 80 0.7047 nan 0.1000 -0.0002
## 100 0.6646 nan 0.1000 -0.0022
## 120 0.6308 nan 0.1000 -0.0008
## 140 0.5970 nan 0.1000 -0.0023
## 160 0.5664 nan 0.1000 -0.0010
## 180 0.5386 nan 0.1000 -0.0019
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## 220 0.4902 nan 0.1000 -0.0008
## 240 0.4693 nan 0.1000 -0.0001
## 260 0.4475 nan 0.1000 -0.0021
## 280 0.4295 nan 0.1000 -0.0006
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## 320 0.3950 nan 0.1000 -0.0005
## 340 0.3769 nan 0.1000 -0.0013
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## 380 0.3455 nan 0.1000 -0.0009
## 400 0.3325 nan 0.1000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2481 nan 0.1000 0.0180
## 2 1.1997 nan 0.1000 0.0209
## 3 1.1604 nan 0.1000 0.0153
## 4 1.1288 nan 0.1000 0.0142
## 5 1.1032 nan 0.1000 0.0102
## 6 1.0784 nan 0.1000 0.0103
## 7 1.0579 nan 0.1000 0.0077
## 8 1.0366 nan 0.1000 0.0094
## 9 1.0217 nan 0.1000 0.0067
## 10 1.0053 nan 0.1000 0.0038
## 20 0.9047 nan 0.1000 0.0019
## 40 0.8106 nan 0.1000 -0.0005
## 60 0.7476 nan 0.1000 -0.0009
## 80 0.7079 nan 0.1000 -0.0011
## 100 0.6651 nan 0.1000 -0.0013
## 120 0.6316 nan 0.1000 -0.0009
## 140 0.5994 nan 0.1000 -0.0009
## 160 0.5688 nan 0.1000 -0.0014
## 180 0.5381 nan 0.1000 -0.0019
## 200 0.5155 nan 0.1000 -0.0008
## 220 0.4888 nan 0.1000 -0.0006
## 240 0.4682 nan 0.1000 -0.0011
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## 280 0.4338 nan 0.1000 -0.0006
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## 320 0.3962 nan 0.1000 -0.0012
## 340 0.3829 nan 0.1000 -0.0005
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## 380 0.3491 nan 0.1000 -0.0003
## 400 0.3371 nan 0.1000 -0.0012
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## 480 0.2912 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2341 nan 0.2000 0.0215
## 2 1.1765 nan 0.2000 0.0236
## 3 1.1456 nan 0.2000 0.0131
## 4 1.1171 nan 0.2000 0.0104
## 5 1.0916 nan 0.2000 0.0106
## 6 1.0699 nan 0.2000 0.0067
## 7 1.0562 nan 0.2000 0.0055
## 8 1.0348 nan 0.2000 0.0071
## 9 1.0163 nan 0.2000 0.0066
## 10 1.0073 nan 0.2000 0.0014
## 20 0.9271 nan 0.2000 0.0020
## 40 0.8685 nan 0.2000 -0.0024
## 60 0.8405 nan 0.2000 -0.0011
## 80 0.8155 nan 0.2000 -0.0009
## 100 0.7962 nan 0.2000 -0.0023
## 120 0.7785 nan 0.2000 -0.0022
## 140 0.7674 nan 0.2000 -0.0017
## 160 0.7565 nan 0.2000 -0.0020
## 180 0.7445 nan 0.2000 -0.0017
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## 220 0.7254 nan 0.2000 -0.0015
## 240 0.7135 nan 0.2000 -0.0026
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## 280 0.6981 nan 0.2000 -0.0006
## 300 0.6894 nan 0.2000 -0.0015
## 320 0.6820 nan 0.2000 -0.0014
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## 360 0.6668 nan 0.2000 -0.0011
## 380 0.6624 nan 0.2000 -0.0015
## 400 0.6549 nan 0.2000 -0.0021
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## 440 0.6412 nan 0.2000 -0.0012
## 460 0.6344 nan 0.2000 -0.0011
## 480 0.6293 nan 0.2000 -0.0032
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2374 nan 0.2000 0.0299
## 2 1.1887 nan 0.2000 0.0211
## 3 1.1522 nan 0.2000 0.0141
## 4 1.1226 nan 0.2000 0.0115
## 5 1.1011 nan 0.2000 0.0080
## 6 1.0789 nan 0.2000 0.0091
## 7 1.0604 nan 0.2000 0.0075
## 8 1.0394 nan 0.2000 0.0083
## 9 1.0276 nan 0.2000 0.0043
## 10 1.0160 nan 0.2000 0.0034
## 20 0.9458 nan 0.2000 -0.0010
## 40 0.8745 nan 0.2000 -0.0007
## 60 0.8450 nan 0.2000 -0.0003
## 80 0.8230 nan 0.2000 0.0000
## 100 0.8070 nan 0.2000 -0.0005
## 120 0.7928 nan 0.2000 -0.0001
## 140 0.7803 nan 0.2000 -0.0008
## 160 0.7630 nan 0.2000 -0.0000
## 180 0.7533 nan 0.2000 -0.0022
## 200 0.7428 nan 0.2000 -0.0015
## 220 0.7336 nan 0.2000 -0.0017
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## 260 0.7140 nan 0.2000 -0.0008
## 280 0.7064 nan 0.2000 -0.0030
## 300 0.6992 nan 0.2000 -0.0011
## 320 0.6921 nan 0.2000 0.0001
## 340 0.6816 nan 0.2000 -0.0022
## 360 0.6784 nan 0.2000 -0.0023
## 380 0.6660 nan 0.2000 -0.0022
## 400 0.6584 nan 0.2000 -0.0015
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## 460 0.6394 nan 0.2000 -0.0026
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2306 nan 0.2000 0.0298
## 2 1.1813 nan 0.2000 0.0240
## 3 1.1424 nan 0.2000 0.0127
## 4 1.1078 nan 0.2000 0.0090
## 5 1.0789 nan 0.2000 0.0091
## 6 1.0609 nan 0.2000 0.0069
## 7 1.0436 nan 0.2000 0.0073
## 8 1.0297 nan 0.2000 0.0038
## 9 1.0127 nan 0.2000 0.0069
## 10 1.0017 nan 0.2000 0.0024
## 20 0.9292 nan 0.2000 0.0007
## 40 0.8626 nan 0.2000 -0.0009
## 60 0.8345 nan 0.2000 -0.0019
## 80 0.8084 nan 0.2000 -0.0024
## 100 0.7892 nan 0.2000 -0.0013
## 120 0.7742 nan 0.2000 -0.0021
## 140 0.7634 nan 0.2000 -0.0048
## 160 0.7512 nan 0.2000 -0.0018
## 180 0.7415 nan 0.2000 -0.0022
## 200 0.7365 nan 0.2000 -0.0031
## 220 0.7279 nan 0.2000 -0.0012
## 240 0.7221 nan 0.2000 -0.0018
## 260 0.7097 nan 0.2000 -0.0014
## 280 0.7011 nan 0.2000 -0.0024
## 300 0.6912 nan 0.2000 -0.0021
## 320 0.6824 nan 0.2000 -0.0016
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## 380 0.6596 nan 0.2000 -0.0016
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## 460 0.6384 nan 0.2000 -0.0021
## 480 0.6335 nan 0.2000 -0.0017
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2121 nan 0.2000 0.0352
## 2 1.1502 nan 0.2000 0.0261
## 3 1.0973 nan 0.2000 0.0226
## 4 1.0669 nan 0.2000 0.0070
## 5 1.0395 nan 0.2000 0.0096
## 6 1.0107 nan 0.2000 0.0107
## 7 0.9901 nan 0.2000 0.0049
## 8 0.9723 nan 0.2000 0.0027
## 9 0.9542 nan 0.2000 0.0039
## 10 0.9439 nan 0.2000 -0.0020
## 20 0.8579 nan 0.2000 0.0034
## 40 0.7789 nan 0.2000 -0.0035
## 60 0.7223 nan 0.2000 -0.0008
## 80 0.6860 nan 0.2000 -0.0013
## 100 0.6493 nan 0.2000 -0.0006
## 120 0.6184 nan 0.2000 -0.0024
## 140 0.5825 nan 0.2000 -0.0013
## 160 0.5545 nan 0.2000 -0.0013
## 180 0.5304 nan 0.2000 -0.0008
## 200 0.5089 nan 0.2000 -0.0013
## 220 0.4872 nan 0.2000 -0.0017
## 240 0.4657 nan 0.2000 -0.0009
## 260 0.4387 nan 0.2000 -0.0003
## 280 0.4207 nan 0.2000 -0.0010
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## 320 0.3835 nan 0.2000 -0.0012
## 340 0.3672 nan 0.2000 -0.0006
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## 380 0.3356 nan 0.2000 -0.0014
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## 460 0.2820 nan 0.2000 -0.0013
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2064 nan 0.2000 0.0365
## 2 1.1441 nan 0.2000 0.0308
## 3 1.0974 nan 0.2000 0.0206
## 4 1.0578 nan 0.2000 0.0167
## 5 1.0292 nan 0.2000 0.0099
## 6 1.0115 nan 0.2000 0.0060
## 7 0.9936 nan 0.2000 0.0046
## 8 0.9748 nan 0.2000 0.0063
## 9 0.9582 nan 0.2000 0.0006
## 10 0.9401 nan 0.2000 0.0059
## 20 0.8666 nan 0.2000 -0.0033
## 40 0.7790 nan 0.2000 -0.0025
## 60 0.7263 nan 0.2000 -0.0022
## 80 0.6904 nan 0.2000 -0.0044
## 100 0.6434 nan 0.2000 -0.0003
## 120 0.6058 nan 0.2000 -0.0038
## 140 0.5731 nan 0.2000 -0.0007
## 160 0.5513 nan 0.2000 -0.0035
## 180 0.5248 nan 0.2000 -0.0005
## 200 0.5007 nan 0.2000 -0.0022
## 220 0.4792 nan 0.2000 0.0000
## 240 0.4629 nan 0.2000 -0.0005
## 260 0.4379 nan 0.2000 -0.0031
## 280 0.4166 nan 0.2000 -0.0022
## 300 0.3946 nan 0.2000 -0.0021
## 320 0.3798 nan 0.2000 -0.0012
## 340 0.3607 nan 0.2000 -0.0015
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## 380 0.3333 nan 0.2000 -0.0022
## 400 0.3205 nan 0.2000 -0.0017
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## 480 0.2696 nan 0.2000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2146 nan 0.2000 0.0386
## 2 1.1569 nan 0.2000 0.0280
## 3 1.1107 nan 0.2000 0.0237
## 4 1.0737 nan 0.2000 0.0118
## 5 1.0405 nan 0.2000 0.0141
## 6 1.0223 nan 0.2000 0.0032
## 7 0.9994 nan 0.2000 0.0090
## 8 0.9818 nan 0.2000 0.0072
## 9 0.9671 nan 0.2000 0.0017
## 10 0.9480 nan 0.2000 0.0040
## 20 0.8579 nan 0.2000 -0.0018
## 40 0.7808 nan 0.2000 0.0007
## 60 0.7315 nan 0.2000 -0.0004
## 80 0.6834 nan 0.2000 -0.0057
## 100 0.6452 nan 0.2000 -0.0022
## 120 0.6041 nan 0.2000 -0.0020
## 140 0.5796 nan 0.2000 -0.0018
## 160 0.5537 nan 0.2000 -0.0024
## 180 0.5295 nan 0.2000 -0.0016
## 200 0.5089 nan 0.2000 -0.0048
## 220 0.4889 nan 0.2000 -0.0025
## 240 0.4728 nan 0.2000 -0.0009
## 260 0.4517 nan 0.2000 -0.0016
## 280 0.4319 nan 0.2000 -0.0013
## 300 0.4117 nan 0.2000 -0.0011
## 320 0.3955 nan 0.2000 -0.0007
## 340 0.3815 nan 0.2000 -0.0015
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## 380 0.3509 nan 0.2000 -0.0012
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## 460 0.2983 nan 0.2000 -0.0026
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1956 nan 0.2000 0.0471
## 2 1.1240 nan 0.2000 0.0338
## 3 1.0763 nan 0.2000 0.0149
## 4 1.0343 nan 0.2000 0.0126
## 5 1.0045 nan 0.2000 0.0090
## 6 0.9746 nan 0.2000 0.0080
## 7 0.9549 nan 0.2000 0.0035
## 8 0.9363 nan 0.2000 0.0045
## 9 0.9239 nan 0.2000 0.0032
## 10 0.9061 nan 0.2000 0.0019
## 20 0.8238 nan 0.2000 -0.0019
## 40 0.7162 nan 0.2000 -0.0022
## 60 0.6414 nan 0.2000 -0.0016
## 80 0.5848 nan 0.2000 -0.0023
## 100 0.5377 nan 0.2000 -0.0016
## 120 0.4745 nan 0.2000 -0.0088
## 140 0.4330 nan 0.2000 -0.0027
## 160 0.3994 nan 0.2000 -0.0019
## 180 0.3652 nan 0.2000 -0.0011
## 200 0.3416 nan 0.2000 -0.0022
## 220 0.3179 nan 0.2000 -0.0018
## 240 0.3003 nan 0.2000 -0.0016
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## 280 0.2573 nan 0.2000 -0.0013
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## 320 0.2268 nan 0.2000 -0.0017
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## 360 0.1973 nan 0.2000 -0.0015
## 380 0.1836 nan 0.2000 -0.0010
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## 460 0.1410 nan 0.2000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2001 nan 0.2000 0.0316
## 2 1.1301 nan 0.2000 0.0324
## 3 1.0736 nan 0.2000 0.0173
## 4 1.0310 nan 0.2000 0.0172
## 5 0.9996 nan 0.2000 0.0091
## 6 0.9746 nan 0.2000 0.0054
## 7 0.9565 nan 0.2000 0.0033
## 8 0.9375 nan 0.2000 0.0070
## 9 0.9194 nan 0.2000 0.0073
## 10 0.9012 nan 0.2000 0.0021
## 20 0.8291 nan 0.2000 -0.0029
## 40 0.7227 nan 0.2000 -0.0041
## 60 0.6484 nan 0.2000 -0.0020
## 80 0.5830 nan 0.2000 -0.0020
## 100 0.5277 nan 0.2000 -0.0031
## 120 0.4827 nan 0.2000 -0.0049
## 140 0.4472 nan 0.2000 -0.0018
## 160 0.4103 nan 0.2000 -0.0005
## 180 0.3797 nan 0.2000 -0.0014
## 200 0.3488 nan 0.2000 -0.0007
## 220 0.3189 nan 0.2000 -0.0018
## 240 0.2970 nan 0.2000 -0.0024
## 260 0.2743 nan 0.2000 -0.0030
## 280 0.2554 nan 0.2000 -0.0010
## 300 0.2362 nan 0.2000 -0.0007
## 320 0.2206 nan 0.2000 -0.0008
## 340 0.2076 nan 0.2000 -0.0015
## 360 0.1924 nan 0.2000 -0.0010
## 380 0.1796 nan 0.2000 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2015 nan 0.2000 0.0419
## 2 1.1294 nan 0.2000 0.0305
## 3 1.0692 nan 0.2000 0.0213
## 4 1.0317 nan 0.2000 0.0117
## 5 1.0055 nan 0.2000 0.0062
## 6 0.9787 nan 0.2000 0.0088
## 7 0.9594 nan 0.2000 0.0048
## 8 0.9370 nan 0.2000 0.0072
## 9 0.9215 nan 0.2000 0.0035
## 10 0.9053 nan 0.2000 0.0049
## 20 0.8178 nan 0.2000 -0.0072
## 40 0.7118 nan 0.2000 -0.0030
## 60 0.6367 nan 0.2000 -0.0035
## 80 0.5663 nan 0.2000 -0.0063
## 100 0.5147 nan 0.2000 -0.0014
## 120 0.4766 nan 0.2000 -0.0029
## 140 0.4461 nan 0.2000 -0.0036
## 160 0.4087 nan 0.2000 -0.0019
## 180 0.3771 nan 0.2000 -0.0019
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## 240 0.2991 nan 0.2000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2114 nan 0.3000 0.0425
## 2 1.1466 nan 0.3000 0.0276
## 3 1.1099 nan 0.3000 0.0142
## 4 1.0666 nan 0.3000 0.0160
## 5 1.0431 nan 0.3000 0.0085
## 6 1.0234 nan 0.3000 0.0059
## 7 0.9996 nan 0.3000 0.0129
## 8 0.9860 nan 0.3000 0.0030
## 9 0.9764 nan 0.3000 0.0007
## 10 0.9676 nan 0.3000 0.0022
## 20 0.8969 nan 0.3000 -0.0082
## 40 0.8354 nan 0.3000 -0.0031
## 60 0.7987 nan 0.3000 -0.0001
## 80 0.7698 nan 0.3000 -0.0030
## 100 0.7524 nan 0.3000 -0.0069
## 120 0.7283 nan 0.3000 -0.0009
## 140 0.7184 nan 0.3000 -0.0013
## 160 0.7061 nan 0.3000 -0.0046
## 180 0.6918 nan 0.3000 -0.0029
## 200 0.6806 nan 0.3000 -0.0034
## 220 0.6689 nan 0.3000 -0.0057
## 240 0.6627 nan 0.3000 -0.0018
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## 320 0.6226 nan 0.3000 -0.0013
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## 460 0.5650 nan 0.3000 -0.0031
## 480 0.5588 nan 0.3000 -0.0037
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2098 nan 0.3000 0.0506
## 2 1.1391 nan 0.3000 0.0308
## 3 1.0993 nan 0.3000 0.0169
## 4 1.0666 nan 0.3000 0.0122
## 5 1.0417 nan 0.3000 0.0098
## 6 1.0143 nan 0.3000 0.0097
## 7 0.9925 nan 0.3000 0.0049
## 8 0.9775 nan 0.3000 0.0047
## 9 0.9661 nan 0.3000 0.0005
## 10 0.9512 nan 0.3000 0.0033
## 20 0.8828 nan 0.3000 0.0014
## 40 0.8326 nan 0.3000 -0.0010
## 60 0.8009 nan 0.3000 -0.0006
## 80 0.7763 nan 0.3000 -0.0007
## 100 0.7554 nan 0.3000 -0.0025
## 120 0.7401 nan 0.3000 -0.0047
## 140 0.7172 nan 0.3000 -0.0019
## 160 0.7007 nan 0.3000 -0.0031
## 180 0.6863 nan 0.3000 -0.0040
## 200 0.6771 nan 0.3000 -0.0044
## 220 0.6632 nan 0.3000 -0.0044
## 240 0.6534 nan 0.3000 -0.0050
## 260 0.6377 nan 0.3000 -0.0015
## 280 0.6273 nan 0.3000 0.0001
## 300 0.6201 nan 0.3000 -0.0012
## 320 0.6141 nan 0.3000 -0.0025
## 340 0.6103 nan 0.3000 -0.0029
## 360 0.6064 nan 0.3000 -0.0074
## 380 0.5954 nan 0.3000 -0.0020
## 400 0.5884 nan 0.3000 -0.0027
## 420 0.5831 nan 0.3000 -0.0040
## 440 0.5735 nan 0.3000 -0.0016
## 460 0.5712 nan 0.3000 -0.0021
## 480 0.5637 nan 0.3000 -0.0009
## 500 0.5607 nan 0.3000 -0.0043
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1992 nan 0.3000 0.0392
## 2 1.1471 nan 0.3000 0.0145
## 3 1.0963 nan 0.3000 0.0227
## 4 1.0640 nan 0.3000 0.0075
## 5 1.0382 nan 0.3000 0.0078
## 6 1.0133 nan 0.3000 0.0121
## 7 0.9924 nan 0.3000 0.0060
## 8 0.9752 nan 0.3000 0.0052
## 9 0.9660 nan 0.3000 0.0026
## 10 0.9522 nan 0.3000 0.0007
## 20 0.8903 nan 0.3000 -0.0012
## 40 0.8432 nan 0.3000 -0.0010
## 60 0.8153 nan 0.3000 -0.0001
## 80 0.7933 nan 0.3000 0.0002
## 100 0.7746 nan 0.3000 -0.0019
## 120 0.7559 nan 0.3000 0.0005
## 140 0.7404 nan 0.3000 -0.0031
## 160 0.7227 nan 0.3000 -0.0031
## 180 0.7096 nan 0.3000 -0.0040
## 200 0.7012 nan 0.3000 -0.0017
## 220 0.6919 nan 0.3000 -0.0020
## 240 0.6839 nan 0.3000 -0.0043
## 260 0.6687 nan 0.3000 -0.0028
## 280 0.6585 nan 0.3000 -0.0034
## 300 0.6495 nan 0.3000 -0.0044
## 320 0.6437 nan 0.3000 -0.0026
## 340 0.6344 nan 0.3000 -0.0033
## 360 0.6236 nan 0.3000 -0.0028
## 380 0.6098 nan 0.3000 -0.0030
## 400 0.6028 nan 0.3000 -0.0041
## 420 0.5977 nan 0.3000 -0.0058
## 440 0.5928 nan 0.3000 -0.0013
## 460 0.5899 nan 0.3000 -0.0067
## 480 0.5837 nan 0.3000 -0.0005
## 500 0.5771 nan 0.3000 -0.0021
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1791 nan 0.3000 0.0485
## 2 1.0958 nan 0.3000 0.0383
## 3 1.0528 nan 0.3000 0.0128
## 4 1.0176 nan 0.3000 0.0127
## 5 0.9877 nan 0.3000 0.0061
## 6 0.9656 nan 0.3000 0.0034
## 7 0.9493 nan 0.3000 0.0036
## 8 0.9325 nan 0.3000 -0.0025
## 9 0.9206 nan 0.3000 -0.0003
## 10 0.9064 nan 0.3000 -0.0026
## 20 0.8275 nan 0.3000 0.0017
## 40 0.7253 nan 0.3000 -0.0026
## 60 0.6719 nan 0.3000 -0.0001
## 80 0.6080 nan 0.3000 -0.0008
## 100 0.5747 nan 0.3000 -0.0056
## 120 0.5325 nan 0.3000 -0.0015
## 140 0.4960 nan 0.3000 -0.0016
## 160 0.4564 nan 0.3000 -0.0020
## 180 0.4150 nan 0.3000 -0.0013
## 200 0.3940 nan 0.3000 -0.0030
## 220 0.3669 nan 0.3000 -0.0018
## 240 0.3444 nan 0.3000 -0.0020
## 260 0.3148 nan 0.3000 -0.0017
## 280 0.2967 nan 0.3000 -0.0012
## 300 0.2854 nan 0.3000 -0.0017
## 320 0.2721 nan 0.3000 -0.0028
## 340 0.2569 nan 0.3000 -0.0007
## 360 0.2444 nan 0.3000 -0.0024
## 380 0.2315 nan 0.3000 -0.0026
## 400 0.2189 nan 0.3000 -0.0014
## 420 0.2088 nan 0.3000 -0.0024
## 440 0.1995 nan 0.3000 -0.0019
## 460 0.1894 nan 0.3000 -0.0013
## 480 0.1818 nan 0.3000 -0.0021
## 500 0.1724 nan 0.3000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1738 nan 0.3000 0.0500
## 2 1.1077 nan 0.3000 0.0179
## 3 1.0631 nan 0.3000 0.0207
## 4 1.0171 nan 0.3000 0.0096
## 5 0.9903 nan 0.3000 0.0044
## 6 0.9577 nan 0.3000 0.0107
## 7 0.9394 nan 0.3000 0.0016
## 8 0.9344 nan 0.3000 -0.0052
## 9 0.9101 nan 0.3000 0.0058
## 10 0.9018 nan 0.3000 -0.0033
## 20 0.8240 nan 0.3000 -0.0040
## 40 0.7424 nan 0.3000 -0.0009
## 60 0.6720 nan 0.3000 -0.0041
## 80 0.6140 nan 0.3000 -0.0064
## 100 0.5589 nan 0.3000 -0.0052
## 120 0.5231 nan 0.3000 -0.0043
## 140 0.4879 nan 0.3000 -0.0010
## 160 0.4436 nan 0.3000 -0.0009
## 180 0.4151 nan 0.3000 -0.0040
## 200 0.3867 nan 0.3000 -0.0030
## 220 0.3670 nan 0.3000 -0.0049
## 240 0.3329 nan 0.3000 -0.0019
## 260 0.3139 nan 0.3000 -0.0023
## 280 0.2974 nan 0.3000 -0.0029
## 300 0.2745 nan 0.3000 0.0002
## 320 0.2571 nan 0.3000 -0.0002
## 340 0.2466 nan 0.3000 -0.0022
## 360 0.2320 nan 0.3000 -0.0016
## 380 0.2183 nan 0.3000 -0.0006
## 400 0.2062 nan 0.3000 -0.0006
## 420 0.1926 nan 0.3000 -0.0010
## 440 0.1822 nan 0.3000 -0.0011
## 460 0.1762 nan 0.3000 -0.0016
## 480 0.1658 nan 0.3000 -0.0012
## 500 0.1579 nan 0.3000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1707 nan 0.3000 0.0553
## 2 1.0951 nan 0.3000 0.0290
## 3 1.0460 nan 0.3000 0.0248
## 4 1.0043 nan 0.3000 0.0137
## 5 0.9858 nan 0.3000 0.0037
## 6 0.9662 nan 0.3000 0.0032
## 7 0.9490 nan 0.3000 0.0046
## 8 0.9306 nan 0.3000 0.0049
## 9 0.9146 nan 0.3000 0.0018
## 10 0.8964 nan 0.3000 0.0041
## 20 0.8233 nan 0.3000 -0.0026
## 40 0.7495 nan 0.3000 -0.0037
## 60 0.6973 nan 0.3000 -0.0024
## 80 0.6509 nan 0.3000 -0.0044
## 100 0.6072 nan 0.3000 -0.0076
## 120 0.5659 nan 0.3000 -0.0016
## 140 0.5295 nan 0.3000 -0.0059
## 160 0.5081 nan 0.3000 -0.0040
## 180 0.4623 nan 0.3000 -0.0029
## 200 0.4392 nan 0.3000 -0.0049
## 220 0.4084 nan 0.3000 -0.0052
## 240 0.3833 nan 0.3000 -0.0053
## 260 0.3615 nan 0.3000 -0.0021
## 280 0.3449 nan 0.3000 -0.0022
## 300 0.3188 nan 0.3000 -0.0010
## 320 0.2931 nan 0.3000 -0.0003
## 340 0.2740 nan 0.3000 -0.0024
## 360 0.2639 nan 0.3000 -0.0009
## 380 0.2537 nan 0.3000 -0.0017
## 400 0.2452 nan 0.3000 -0.0027
## 420 0.2289 nan 0.3000 -0.0011
## 440 0.2172 nan 0.3000 -0.0024
## 460 0.2065 nan 0.3000 -0.0005
## 480 0.1954 nan 0.3000 -0.0012
## 500 0.1870 nan 0.3000 -0.0012
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1639 nan 0.3000 0.0544
## 2 1.0883 nan 0.3000 0.0278
## 3 1.0066 nan 0.3000 0.0360
## 4 0.9634 nan 0.3000 0.0149
## 5 0.9373 nan 0.3000 0.0055
## 6 0.9196 nan 0.3000 -0.0002
## 7 0.8948 nan 0.3000 0.0015
## 8 0.8781 nan 0.3000 -0.0004
## 9 0.8629 nan 0.3000 -0.0003
## 10 0.8537 nan 0.3000 -0.0066
## 20 0.7532 nan 0.3000 -0.0043
## 40 0.6191 nan 0.3000 -0.0076
## 60 0.5561 nan 0.3000 -0.0073
## 80 0.4870 nan 0.3000 -0.0020
## 100 0.4680 nan 0.3000 -0.0015
## 120 inf nan 0.3000 nan
## 140 inf nan 0.3000 nan
## 160 inf nan 0.3000 nan
## 180 inf nan 0.3000 -0.0005
## 200 inf nan 0.3000 -0.0016
## 220 inf nan 0.3000 -0.0014
## 240 inf nan 0.3000 nan
## 260 inf nan 0.3000 -0.0018
## 280 inf nan 0.3000 nan
## 300 inf nan 0.3000 nan
## 320 inf nan 0.3000 -0.0012
## 340 inf nan 0.3000 -0.0014
## 360 inf nan 0.3000 nan
## 380 inf nan 0.3000 nan
## 400 inf nan 0.3000 nan
## 420 inf nan 0.3000 -0.0007
## 440 inf nan 0.3000 -0.0006
## 460 inf nan 0.3000 nan
## 480 inf nan 0.3000 nan
## 500 inf nan 0.3000 -0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1614 nan 0.3000 0.0617
## 2 1.0670 nan 0.3000 0.0443
## 3 1.0176 nan 0.3000 0.0107
## 4 0.9741 nan 0.3000 0.0140
## 5 0.9426 nan 0.3000 0.0073
## 6 0.9241 nan 0.3000 -0.0019
## 7 0.8996 nan 0.3000 0.0043
## 8 0.8844 nan 0.3000 0.0012
## 9 0.8780 nan 0.3000 -0.0050
## 10 0.8620 nan 0.3000 -0.0021
## 20 0.7731 nan 0.3000 -0.0052
## 40 0.6527 nan 0.3000 -0.0028
## 60 0.5643 nan 0.3000 -0.0105
## 80 0.4992 nan 0.3000 -0.0006
## 100 0.4467 nan 0.3000 -0.0013
## 120 0.4049 nan 0.3000 -0.0038
## 140 0.3657 nan 0.3000 -0.0007
## 160 0.3143 nan 0.3000 -0.0037
## 180 0.2826 nan 0.3000 -0.0055
## 200 0.2513 nan 0.3000 -0.0016
## 220 0.2280 nan 0.3000 -0.0038
## 240 0.2022 nan 0.3000 -0.0001
## 260 0.1791 nan 0.3000 -0.0029
## 280 0.1627 nan 0.3000 -0.0025
## 300 0.1504 nan 0.3000 -0.0015
## 320 0.1398 nan 0.3000 -0.0016
## 340 0.1243 nan 0.3000 -0.0012
## 360 0.1139 nan 0.3000 -0.0010
## 380 0.1056 nan 0.3000 -0.0005
## 400 0.0956 nan 0.3000 -0.0006
## 420 0.0893 nan 0.3000 -0.0008
## 440 0.0803 nan 0.3000 -0.0005
## 460 0.0729 nan 0.3000 -0.0004
## 480 0.0676 nan 0.3000 -0.0008
## 500 0.0617 nan 0.3000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1515 nan 0.3000 0.0611
## 2 1.0820 nan 0.3000 0.0237
## 3 1.0420 nan 0.3000 0.0074
## 4 0.9868 nan 0.3000 0.0208
## 5 0.9442 nan 0.3000 0.0107
## 6 0.9179 nan 0.3000 0.0086
## 7 0.8927 nan 0.3000 0.0038
## 8 0.8781 nan 0.3000 -0.0079
## 9 0.8612 nan 0.3000 0.0041
## 10 0.8495 nan 0.3000 -0.0030
## 20 0.7710 nan 0.3000 0.0013
## 40 0.6611 nan 0.3000 -0.0005
## 60 0.5995 nan 0.3000 -0.0023
## 80 0.5275 nan 0.3000 -0.0047
## 100 0.4720 nan 0.3000 -0.0059
## 120 0.4161 nan 0.3000 -0.0032
## 140 0.3703 nan 0.3000 -0.0036
## 160 0.3274 nan 0.3000 -0.0013
## 180 0.2838 nan 0.3000 -0.0041
## 200 0.2490 nan 0.3000 -0.0015
## 220 0.2273 nan 0.3000 -0.0030
## 240 0.2010 nan 0.3000 -0.0018
## 260 0.1852 nan 0.3000 -0.0037
## 280 0.1662 nan 0.3000 0.0003
## 300 0.1480 nan 0.3000 -0.0008
## 320 0.1340 nan 0.3000 -0.0011
## 340 0.1203 nan 0.3000 -0.0013
## 360 0.1098 nan 0.3000 -0.0011
## 380 0.1018 nan 0.3000 -0.0009
## 400 0.0922 nan 0.3000 -0.0009
## 420 0.0837 nan 0.3000 -0.0014
## 440 0.0765 nan 0.3000 -0.0006
## 460 0.0695 nan 0.3000 -0.0004
## 480 0.0649 nan 0.3000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1648 nan 0.5000 0.0690
## 2 1.0886 nan 0.5000 0.0309
## 3 1.0341 nan 0.5000 0.0186
## 4 0.9935 nan 0.5000 0.0137
## 5 0.9771 nan 0.5000 0.0031
## 6 0.9553 nan 0.5000 0.0009
## 7 0.9339 nan 0.5000 0.0089
## 8 0.9233 nan 0.5000 0.0004
## 9 0.9219 nan 0.5000 -0.0086
## 10 0.9158 nan 0.5000 -0.0016
## 20 0.8739 nan 0.5000 -0.0069
## 40 0.8249 nan 0.5000 -0.0027
## 60 0.7890 nan 0.5000 -0.0095
## 80 0.7614 nan 0.5000 -0.0038
## 100 0.7335 nan 0.5000 -0.0040
## 120 0.7141 nan 0.5000 -0.0017
## 140 0.6959 nan 0.5000 -0.0075
## 160 0.6694 nan 0.5000 -0.0032
## 180 0.6569 nan 0.5000 -0.0076
## 200 0.6431 nan 0.5000 -0.0048
## 220 0.6313 nan 0.5000 -0.0066
## 240 0.6192 nan 0.5000 -0.0066
## 260 0.6054 nan 0.5000 -0.0027
## 280 0.5932 nan 0.5000 -0.0046
## 300 0.5840 nan 0.5000 -0.0037
## 320 0.5795 nan 0.5000 -0.0038
## 340 0.5641 nan 0.5000 -0.0053
## 360 0.5498 nan 0.5000 -0.0039
## 380 0.5444 nan 0.5000 -0.0025
## 400 0.5322 nan 0.5000 -0.0011
## 420 0.5246 nan 0.5000 -0.0041
## 440 0.5195 nan 0.5000 -0.0012
## 460 0.5064 nan 0.5000 -0.0011
## 480 0.4963 nan 0.5000 -0.0016
## 500 0.4941 nan 0.5000 -0.0041
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1728 nan 0.5000 0.0552
## 2 1.0987 nan 0.5000 0.0299
## 3 1.0566 nan 0.5000 0.0139
## 4 1.0181 nan 0.5000 0.0162
## 5 0.9764 nan 0.5000 0.0208
## 6 0.9687 nan 0.5000 -0.0070
## 7 0.9574 nan 0.5000 -0.0001
## 8 0.9396 nan 0.5000 0.0005
## 9 0.9388 nan 0.5000 -0.0094
## 10 0.9277 nan 0.5000 -0.0003
## 20 0.8559 nan 0.5000 -0.0005
## 40 0.7974 nan 0.5000 -0.0080
## 60 0.7704 nan 0.5000 -0.0027
## 80 0.7497 nan 0.5000 -0.0034
## 100 0.7326 nan 0.5000 -0.0090
## 120 0.7105 nan 0.5000 -0.0036
## 140 0.6897 nan 0.5000 -0.0061
## 160 0.6711 nan 0.5000 -0.0074
## 180 0.6521 nan 0.5000 -0.0087
## 200 0.6330 nan 0.5000 -0.0034
## 220 0.6283 nan 0.5000 -0.0139
## 240 0.6177 nan 0.5000 -0.0012
## 260 0.6094 nan 0.5000 -0.0074
## 280 0.5974 nan 0.5000 -0.0045
## 300 0.5793 nan 0.5000 -0.0005
## 320 0.5662 nan 0.5000 -0.0047
## 340 0.5578 nan 0.5000 -0.0046
## 360 0.5554 nan 0.5000 -0.0042
## 380 0.5399 nan 0.5000 -0.0028
## 400 0.5382 nan 0.5000 -0.0021
## 420 0.5333 nan 0.5000 -0.0055
## 440 0.5254 nan 0.5000 -0.0032
## 460 0.5183 nan 0.5000 -0.0029
## 480 0.5140 nan 0.5000 -0.0065
## 500 0.5058 nan 0.5000 -0.0041
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1503 nan 0.5000 0.0619
## 2 1.0854 nan 0.5000 0.0199
## 3 1.0342 nan 0.5000 0.0206
## 4 0.9927 nan 0.5000 0.0185
## 5 0.9737 nan 0.5000 0.0028
## 6 0.9515 nan 0.5000 0.0074
## 7 0.9360 nan 0.5000 0.0064
## 8 0.9241 nan 0.5000 -0.0019
## 9 0.9173 nan 0.5000 -0.0011
## 10 0.9115 nan 0.5000 -0.0009
## 20 0.8666 nan 0.5000 -0.0004
## 40 0.8163 nan 0.5000 -0.0027
## 60 0.7797 nan 0.5000 -0.0049
## 80 0.7494 nan 0.5000 -0.0007
## 100 0.7223 nan 0.5000 -0.0027
## 120 0.7075 nan 0.5000 -0.0053
## 140 0.6866 nan 0.5000 -0.0015
## 160 0.6845 nan 0.5000 -0.0127
## 180 0.6620 nan 0.5000 -0.0050
## 200 0.6413 nan 0.5000 -0.0045
## 220 0.6289 nan 0.5000 -0.0076
## 240 0.6097 nan 0.5000 -0.0052
## 260 0.5952 nan 0.5000 -0.0032
## 280 0.5857 nan 0.5000 -0.0072
## 300 0.5758 nan 0.5000 -0.0019
## 320 0.5627 nan 0.5000 -0.0072
## 340 0.5549 nan 0.5000 -0.0036
## 360 0.5430 nan 0.5000 -0.0033
## 380 0.5278 nan 0.5000 -0.0039
## 400 0.5235 nan 0.5000 -0.0034
## 420 0.5078 nan 0.5000 -0.0003
## 440 0.5022 nan 0.5000 -0.0015
## 460 0.5017 nan 0.5000 -0.0027
## 480 0.5020 nan 0.5000 -0.0070
## 500 0.4885 nan 0.5000 -0.0050
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1316 nan 0.5000 0.0713
## 2 1.0409 nan 0.5000 0.0305
## 3 0.9845 nan 0.5000 0.0118
## 4 0.9595 nan 0.5000 0.0011
## 5 0.9307 nan 0.5000 0.0028
## 6 0.9090 nan 0.5000 0.0030
## 7 0.8933 nan 0.5000 -0.0057
## 8 0.8791 nan 0.5000 -0.0005
## 9 0.8957 nan 0.5000 -0.0388
## 10 0.8775 nan 0.5000 -0.0023
## 20 0.7895 nan 0.5000 -0.0140
## 40 0.6676 nan 0.5000 -0.0034
## 60 0.6065 nan 0.5000 0.0007
## 80 0.5824 nan 0.5000 -0.0131
## 100 0.5366 nan 0.5000 -0.0012
## 120 1.1926 nan 0.5000 -0.0061
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1130 nan 0.5000 0.0831
## 2 1.0147 nan 0.5000 0.0293
## 3 0.9714 nan 0.5000 0.0033
## 4 0.9476 nan 0.5000 -0.0075
## 5 0.9162 nan 0.5000 0.0124
## 6 0.9095 nan 0.5000 -0.0045
## 7 0.8901 nan 0.5000 0.0046
## 8 0.8789 nan 0.5000 -0.0090
## 9 0.8623 nan 0.5000 -0.0121
## 10 0.8463 nan 0.5000 -0.0021
## 20 0.7806 nan 0.5000 -0.0114
## 40 0.6689 nan 0.5000 -0.0085
## 60 0.6116 nan 0.5000 -0.0037
## 80 0.5608 nan 0.5000 -0.0003
## 100 0.5148 nan 0.5000 -0.0094
## 120 0.4610 nan 0.5000 -0.0099
## 140 0.4216 nan 0.5000 -0.0052
## 160 0.3755 nan 0.5000 -0.0088
## 180 0.3274 nan 0.5000 -0.0067
## 200 0.3040 nan 0.5000 -0.0054
## 220 0.2841 nan 0.5000 -0.0061
## 240 inf nan 0.5000 -inf
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1194 nan 0.5000 0.0885
## 2 1.0369 nan 0.5000 0.0326
## 3 0.9862 nan 0.5000 0.0185
## 4 0.9434 nan 0.5000 0.0152
## 5 0.9194 nan 0.5000 0.0009
## 6 0.9081 nan 0.5000 -0.0073
## 7 0.8908 nan 0.5000 -0.0086
## 8 0.8791 nan 0.5000 -0.0084
## 9 0.8657 nan 0.5000 0.0048
## 10 0.8622 nan 0.5000 -0.0138
## 20 0.7710 nan 0.5000 0.0001
## 40 0.6658 nan 0.5000 -0.0060
## 60 0.5841 nan 0.5000 -0.0056
## 80 0.5166 nan 0.5000 -0.0056
## 100 0.4580 nan 0.5000 -0.0115
## 120 0.4134 nan 0.5000 -0.0047
## 140 0.3576 nan 0.5000 -0.0041
## 160 0.3279 nan 0.5000 -0.0075
## 180 0.2979 nan 0.5000 -0.0048
## 200 0.2720 nan 0.5000 -0.0035
## 220 0.2439 nan 0.5000 -0.0017
## 240 0.2272 nan 0.5000 -0.0042
## 260 0.2097 nan 0.5000 -0.0046
## 280 0.1890 nan 0.5000 -0.0067
## 300 0.1663 nan 0.5000 -0.0013
## 320 0.1573 nan 0.5000 -0.0016
## 340 0.1460 nan 0.5000 -0.0015
## 360 0.1319 nan 0.5000 -0.0008
## 380 0.1181 nan 0.5000 -0.0008
## 400 0.1062 nan 0.5000 -0.0019
## 420 0.0971 nan 0.5000 -0.0010
## 440 0.0899 nan 0.5000 -0.0004
## 460 0.0834 nan 0.5000 -0.0009
## 480 0.0762 nan 0.5000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1228 nan 0.5000 0.0686
## 2 1.0247 nan 0.5000 0.0362
## 3 0.9717 nan 0.5000 0.0051
## 4 0.9261 nan 0.5000 0.0078
## 5 0.8972 nan 0.5000 -0.0120
## 6 0.8749 nan 0.5000 -0.0007
## 7 0.8527 nan 0.5000 -0.0067
## 8 0.8479 nan 0.5000 -0.0120
## 9 0.8425 nan 0.5000 -0.0133
## 10 0.8210 nan 0.5000 -0.0005
## 20 0.7136 nan 0.5000 -0.0008
## 40 23.1725 nan 0.5000 -0.2983
## 60 26.6824 nan 0.5000 -0.0134
## 80 26.6617 nan 0.5000 -0.0335
## 100 1173151856648.1650 nan 0.5000 -0.0034
## 120 1173151856648.1345 nan 0.5000 -0.0001
## 140 1173151856648.1089 nan 0.5000 -0.0048
## 160 1173151856648.1216 nan 0.5000 0.0001
## 180 1173151855354.3801 nan 0.5000 -0.0025
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
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## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1007 nan 0.5000 0.0833
## 2 1.0010 nan 0.5000 0.0415
## 3 0.9526 nan 0.5000 0.0091
## 4 0.9248 nan 0.5000 -0.0005
## 5 0.9131 nan 0.5000 -0.0085
## 6 0.8876 nan 0.5000 -0.0042
## 7 0.8693 nan 0.5000 -0.0011
## 8 0.8573 nan 0.5000 -0.0083
## 9 0.8307 nan 0.5000 -0.0039
## 10 0.8237 nan 0.5000 -0.0122
## 20 0.7262 nan 0.5000 -0.0199
## 40 0.5879 nan 0.5000 -0.0075
## 60 0.4756 nan 0.5000 -0.0135
## 80 0.3895 nan 0.5000 -0.0014
## 100 0.3259 nan 0.5000 -0.0037
## 120 0.2773 nan 0.5000 -0.0089
## 140 0.2167 nan 0.5000 -0.0045
## 160 0.1641 nan 0.5000 -0.0005
## 180 0.1365 nan 0.5000 -0.0011
## 200 0.1183 nan 0.5000 -0.0008
## 220 0.1002 nan 0.5000 -0.0012
## 240 0.0868 nan 0.5000 -0.0005
## 260 0.0749 nan 0.5000 0.0002
## 280 0.0680 nan 0.5000 -0.0023
## 300 0.0585 nan 0.5000 -0.0009
## 320 0.0514 nan 0.5000 -0.0008
## 340 0.0450 nan 0.5000 -0.0011
## 360 0.0406 nan 0.5000 -0.0002
## 380 0.0356 nan 0.5000 -0.0003
## 400 0.0316 nan 0.5000 -0.0003
## 420 0.0279 nan 0.5000 -0.0003
## 440 0.0245 nan 0.5000 -0.0001
## 460 0.0219 nan 0.5000 -0.0004
## 480 0.0186 nan 0.5000 -0.0001
## 500 0.0172 nan 0.5000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0730 nan 0.5000 0.0869
## 2 0.9772 nan 0.5000 0.0382
## 3 0.9294 nan 0.5000 0.0124
## 4 0.9052 nan 0.5000 -0.0048
## 5 0.8758 nan 0.5000 -0.0001
## 6 0.8567 nan 0.5000 -0.0036
## 7 0.8476 nan 0.5000 -0.0087
## 8 0.8413 nan 0.5000 -0.0102
## 9 0.8334 nan 0.5000 -0.0049
## 10 0.8093 nan 0.5000 0.0002
## 20 0.7016 nan 0.5000 -0.0048
## 40 0.5885 nan 0.5000 -0.0047
## 60 0.4818 nan 0.5000 -0.0106
## 80 0.4040 nan 0.5000 -0.0084
## 100 0.3307 nan 0.5000 -0.0059
## 120 0.2730 nan 0.5000 -0.0019
## 140 0.2214 nan 0.5000 -0.0029
## 160 0.2164 nan 0.5000 -0.0171
## 180 0.1609 nan 0.5000 -0.0028
## 200 0.1317 nan 0.5000 -0.0006
## 220 0.1134 nan 0.5000 -0.0007
## 240 0.0948 nan 0.5000 -0.0013
## 260 0.0825 nan 0.5000 -0.0003
## 280 0.0711 nan 0.5000 -0.0018
## 300 0.0623 nan 0.5000 -0.0004
## 320 0.0532 nan 0.5000 -0.0009
## 340 0.0460 nan 0.5000 -0.0002
## 360 0.0402 nan 0.5000 -0.0005
## 380 0.0349 nan 0.5000 -0.0004
## 400 0.0303 nan 0.5000 0.0001
## 420 0.0274 nan 0.5000 -0.0004
## 440 0.0242 nan 0.5000 -0.0002
## 460 0.0214 nan 0.5000 -0.0002
## 480 0.0194 nan 0.5000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1308 nan 1.0000 0.0460
## 2 1.0528 nan 1.0000 0.0306
## 3 1.0352 nan 1.0000 -0.0052
## 4 1.0055 nan 1.0000 0.0070
## 5 1.0128 nan 1.0000 -0.0317
## 6 1.0014 nan 1.0000 -0.0222
## 7 0.9820 nan 1.0000 -0.0127
## 8 0.9478 nan 1.0000 0.0144
## 9 0.9304 nan 1.0000 0.0049
## 10 0.9149 nan 1.0000 0.0033
## 20 0.9750 nan 1.0000 -0.0007
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1480 nan 1.0000 0.0590
## 2 1.0832 nan 1.0000 0.0324
## 3 1.0217 nan 1.0000 0.0234
## 4 0.9924 nan 1.0000 0.0070
## 5 0.9909 nan 1.0000 -0.0211
## 6 0.9804 nan 1.0000 -0.0097
## 7 0.9958 nan 1.0000 -0.0322
## 8 1.0032 nan 1.0000 -0.0259
## 9 1.0006 nan 1.0000 -0.0205
## 10 0.9985 nan 1.0000 -0.0162
## 20 0.9159 nan 1.0000 0.0116
## 40 3.8254 nan 1.0000 -0.0164
## 60 3.8086 nan 1.0000 0.0001
## 80 8628202.9085 nan 1.0000 -5743820.4699
## 100 8628202.8606 nan 1.0000 0.0038
## 120 8628202.8273 nan 1.0000 -0.0302
## 140 8628202.8023 nan 1.0000 0.0037
## 160 8628202.7985 nan 1.0000 0.0018
## 180 8628202.7487 nan 1.0000 -0.0010
## 200 8628202.7405 nan 1.0000 -0.0361
## 220 8628202.7393 nan 1.0000 -0.0014
## 240 8628208.4030 nan 1.0000 -0.0057
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1185 nan 1.0000 0.0740
## 2 1.0691 nan 1.0000 0.0094
## 3 1.0174 nan 1.0000 0.0024
## 4 0.9964 nan 1.0000 0.0026
## 5 0.9874 nan 1.0000 -0.0144
## 6 0.9835 nan 1.0000 -0.0212
## 7 0.9737 nan 1.0000 -0.0085
## 8 0.9580 nan 1.0000 0.0066
## 9 0.9603 nan 1.0000 -0.0135
## 10 0.9696 nan 1.0000 -0.0335
## 20 0.9359 nan 1.0000 -0.0512
## 40 0.8632 nan 1.0000 0.0008
## 60 0.8041 nan 1.0000 -0.0208
## 80 0.7984 nan 1.0000 -0.0229
## 100 0.7312 nan 1.0000 -0.0100
## 120 0.7273 nan 1.0000 -0.0338
## 140 0.6956 nan 1.0000 -0.0175
## 160 0.6773 nan 1.0000 0.0017
## 180 0.7876 nan 1.0000 -0.0153
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0590 nan 1.0000 0.0811
## 2 0.9905 nan 1.0000 0.0013
## 3 0.9500 nan 1.0000 0.0075
## 4 0.9618 nan 1.0000 -0.0267
## 5 0.9271 nan 1.0000 -0.0067
## 6 0.9003 nan 1.0000 0.0010
## 7 0.9030 nan 1.0000 -0.0248
## 8 0.9098 nan 1.0000 -0.0331
## 9 0.8959 nan 1.0000 -0.0089
## 10 0.8639 nan 1.0000 0.0045
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 -0.0043
## 120 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0747 nan 1.0000 0.0649
## 2 1.0041 nan 1.0000 0.0088
## 3 0.9694 nan 1.0000 -0.0011
## 4 0.9480 nan 1.0000 0.0034
## 5 0.9505 nan 1.0000 -0.0319
## 6 0.9316 nan 1.0000 -0.0122
## 7 0.9370 nan 1.0000 -0.0368
## 8 0.9171 nan 1.0000 -0.0143
## 9 0.9164 nan 1.0000 -0.0175
## 10 0.9224 nan 1.0000 -0.0354
## 20 0.8828 nan 1.0000 -0.0172
## 40 inf nan 1.0000 -inf
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 0.1627
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
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## 180 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0751 nan 1.0000 0.0781
## 2 0.9972 nan 1.0000 -0.0068
## 3 0.9766 nan 1.0000 -0.0152
## 4 0.9896 nan 1.0000 -0.0352
## 5 0.9874 nan 1.0000 -0.0319
## 6 0.9426 nan 1.0000 0.0105
## 7 0.9559 nan 1.0000 -0.0396
## 8 0.9524 nan 1.0000 -0.0305
## 9 1.0052 nan 1.0000 -0.0502
## 10 0.9876 nan 1.0000 -0.0194
## 20 inf nan 1.0000 -inf
## 40 592575273.2485 nan 1.0000 -0.0322
## 60 592575273.1291 nan 1.0000 -0.0193
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 -0.0252
## 420 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0755 nan 1.0000 0.0674
## 2 1.0177 nan 1.0000 -0.0010
## 3 0.9631 nan 1.0000 -0.0023
## 4 0.9460 nan 1.0000 -0.0140
## 5 0.9124 nan 1.0000 -0.0022
## 6 1.1324 nan 1.0000 -0.2553
## 7 1.1068 nan 1.0000 -0.0580
## 8 1.0576 nan 1.0000 -0.0067
## 9 inf nan 1.0000 -inf
## 10 inf nan 1.0000 nan
## 20 inf nan 1.0000 0.0016
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 -0.0102
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 nan nan 1.0000 nan
## 200 nan nan 1.0000 nan
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## 300 nan nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0672 nan 1.0000 0.0637
## 2 1.0092 nan 1.0000 0.0037
## 3 0.9480 nan 1.0000 0.0060
## 4 0.9404 nan 1.0000 -0.0323
## 5 0.9308 nan 1.0000 -0.0343
## 6 0.9191 nan 1.0000 -0.0155
## 7 0.8707 nan 1.0000 0.0026
## 8 0.8586 nan 1.0000 -0.0198
## 9 0.8727 nan 1.0000 -0.0610
## 10 0.8743 nan 1.0000 -0.0521
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0541 nan 1.0000 0.1122
## 2 0.9829 nan 1.0000 0.0015
## 3 0.9586 nan 1.0000 -0.0171
## 4 0.9455 nan 1.0000 -0.0308
## 5 0.9682 nan 1.0000 -0.0760
## 6 0.9772 nan 1.0000 -0.0608
## 7 0.9780 nan 1.0000 -0.0443
## 8 0.9533 nan 1.0000 -0.0555
## 9 0.9298 nan 1.0000 -0.0224
## 10 0.9070 nan 1.0000 -0.0305
## 20 9.5833 nan 1.0000 -0.0307
## 40 1585163930813153811042442464088684406686.0000 nan 1.0000 -0.0396
## 60 1585163930813153811042442464088684406686.0000 nan 1.0000 -0.0157
## 80 1585163930813153811042442464088684406686.0000 nan 1.0000 -0.0139
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
## 2 1.2945 nan 0.0000 0.0000
## 3 1.2945 nan 0.0000 0.0000
## 4 1.2945 nan 0.0000 0.0000
## 5 1.2945 nan 0.0000 0.0000
## 6 1.2945 nan 0.0000 0.0000
## 7 1.2945 nan 0.0000 0.0000
## 8 1.2945 nan 0.0000 0.0000
## 9 1.2945 nan 0.0000 0.0000
## 10 1.2945 nan 0.0000 0.0000
## 20 1.2945 nan 0.0000 0.0000
## 40 1.2945 nan 0.0000 0.0000
## 60 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## 20 1.2945 nan 0.0000 0.0000
## 40 1.2945 nan 0.0000 0.0000
## 60 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## 3 1.2945 nan 0.0000 0.0000
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## 40 1.2945 nan 0.0000 0.0000
## 60 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## 3 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## 40 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2945 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2942 nan 0.0010 0.0002
## 2 1.2937 nan 0.0010 0.0002
## 3 1.2933 nan 0.0010 0.0002
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## 10 1.2906 nan 0.0010 0.0002
## 20 1.2867 nan 0.0010 0.0001
## 40 1.2794 nan 0.0010 0.0002
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## 80 1.2656 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
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## 3 1.2933 nan 0.0010 0.0002
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## 6 1.2922 nan 0.0010 0.0002
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## 8 1.2915 nan 0.0010 0.0002
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## 10 1.2908 nan 0.0010 0.0002
## 20 1.2870 nan 0.0010 0.0002
## 40 1.2795 nan 0.0010 0.0002
## 60 1.2724 nan 0.0010 0.0002
## 80 1.2654 nan 0.0010 0.0002
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## 460 1.1684 nan 0.0010 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
## 2 1.2937 nan 0.0010 0.0002
## 3 1.2933 nan 0.0010 0.0002
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## 5 1.2925 nan 0.0010 0.0002
## 6 1.2921 nan 0.0010 0.0002
## 7 1.2917 nan 0.0010 0.0002
## 8 1.2914 nan 0.0010 0.0002
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## 10 1.2905 nan 0.0010 0.0002
## 20 1.2869 nan 0.0010 0.0002
## 40 1.2795 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
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## 3 1.2931 nan 0.0010 0.0002
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## 6 1.2916 nan 0.0010 0.0002
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## 10 1.2897 nan 0.0010 0.0002
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## 40 1.2757 nan 0.0010 0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2941 nan 0.0010 0.0002
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## 3 1.2931 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2940 nan 0.0010 0.0002
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## 3 1.2931 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2939 nan 0.0010 0.0003
## 2 1.2933 nan 0.0010 0.0003
## 3 1.2927 nan 0.0010 0.0002
## 4 1.2922 nan 0.0010 0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2939 nan 0.0010 0.0002
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## 3 1.2927 nan 0.0010 0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2939 nan 0.0010 0.0003
## 2 1.2933 nan 0.0010 0.0002
## 3 1.2927 nan 0.0010 0.0003
## 4 1.2922 nan 0.0010 0.0003
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2557 nan 0.1000 0.0177
## 2 1.2248 nan 0.1000 0.0138
## 3 1.1999 nan 0.1000 0.0113
## 4 1.1795 nan 0.1000 0.0092
## 5 1.1571 nan 0.1000 0.0093
## 6 1.1413 nan 0.1000 0.0064
## 7 1.1239 nan 0.1000 0.0067
## 8 1.1075 nan 0.1000 0.0070
## 9 1.0950 nan 0.1000 0.0048
## 10 1.0806 nan 0.1000 0.0046
## 20 0.9947 nan 0.1000 -0.0005
## 40 0.9169 nan 0.1000 -0.0003
## 60 0.8759 nan 0.1000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2549 nan 0.1000 0.0166
## 2 1.2268 nan 0.1000 0.0129
## 3 1.2021 nan 0.1000 0.0126
## 4 1.1817 nan 0.1000 0.0082
## 5 1.1645 nan 0.1000 0.0075
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2554 nan 0.1000 0.0180
## 2 1.2282 nan 0.1000 0.0138
## 3 1.1991 nan 0.1000 0.0129
## 4 1.1757 nan 0.1000 0.0094
## 5 1.1576 nan 0.1000 0.0062
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## 7 1.1219 nan 0.1000 0.0070
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## 160 0.7933 nan 0.1000 -0.0005
## 180 0.7857 nan 0.1000 -0.0011
## 200 0.7775 nan 0.1000 -0.0005
## 220 0.7711 nan 0.1000 -0.0008
## 240 0.7639 nan 0.1000 -0.0011
## 260 0.7599 nan 0.1000 -0.0004
## 280 0.7562 nan 0.1000 -0.0011
## 300 0.7489 nan 0.1000 -0.0011
## 320 0.7442 nan 0.1000 -0.0007
## 340 0.7397 nan 0.1000 -0.0006
## 360 0.7344 nan 0.1000 -0.0010
## 380 0.7289 nan 0.1000 -0.0007
## 400 0.7258 nan 0.1000 -0.0010
## 420 0.7219 nan 0.1000 -0.0015
## 440 0.7146 nan 0.1000 -0.0006
## 460 0.7113 nan 0.1000 -0.0003
## 480 0.7070 nan 0.1000 -0.0010
## 500 0.7031 nan 0.1000 -0.0010
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2535 nan 0.1000 0.0153
## 2 1.2178 nan 0.1000 0.0191
## 3 1.1809 nan 0.1000 0.0158
## 4 1.1492 nan 0.1000 0.0134
## 5 1.1191 nan 0.1000 0.0125
## 6 1.0947 nan 0.1000 0.0088
## 7 1.0748 nan 0.1000 0.0081
## 8 1.0538 nan 0.1000 0.0084
## 9 1.0384 nan 0.1000 0.0049
## 10 1.0220 nan 0.1000 0.0064
## 20 0.9348 nan 0.1000 0.0001
## 40 0.8399 nan 0.1000 -0.0004
## 60 0.7908 nan 0.1000 -0.0009
## 80 0.7556 nan 0.1000 -0.0010
## 100 0.7289 nan 0.1000 -0.0015
## 120 0.7048 nan 0.1000 0.0001
## 140 0.6776 nan 0.1000 -0.0022
## 160 0.6612 nan 0.1000 -0.0005
## 180 0.6432 nan 0.1000 -0.0003
## 200 0.6243 nan 0.1000 -0.0009
## 220 0.6079 nan 0.1000 -0.0002
## 240 0.5922 nan 0.1000 -0.0001
## 260 0.5787 nan 0.1000 -0.0005
## 280 0.5607 nan 0.1000 -0.0011
## 300 0.5427 nan 0.1000 -0.0021
## 320 0.5259 nan 0.1000 0.0001
## 340 0.5155 nan 0.1000 -0.0009
## 360 0.5022 nan 0.1000 -0.0005
## 380 0.4903 nan 0.1000 -0.0003
## 400 0.4782 nan 0.1000 -0.0012
## 420 0.4674 nan 0.1000 -0.0009
## 440 0.4548 nan 0.1000 -0.0008
## 460 0.4438 nan 0.1000 -0.0015
## 480 0.4334 nan 0.1000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2514 nan 0.1000 0.0221
## 2 1.2136 nan 0.1000 0.0179
## 3 1.1804 nan 0.1000 0.0157
## 4 1.1510 nan 0.1000 0.0142
## 5 1.1251 nan 0.1000 0.0095
## 6 1.1027 nan 0.1000 0.0096
## 7 1.0791 nan 0.1000 0.0077
## 8 1.0592 nan 0.1000 0.0055
## 9 1.0417 nan 0.1000 0.0059
## 10 1.0270 nan 0.1000 0.0069
## 20 0.9318 nan 0.1000 0.0000
## 40 0.8485 nan 0.1000 0.0003
## 60 0.8062 nan 0.1000 -0.0010
## 80 0.7762 nan 0.1000 -0.0017
## 100 0.7473 nan 0.1000 -0.0008
## 120 0.7255 nan 0.1000 -0.0013
## 140 0.6988 nan 0.1000 -0.0005
## 160 0.6790 nan 0.1000 -0.0009
## 180 0.6553 nan 0.1000 -0.0013
## 200 0.6365 nan 0.1000 -0.0015
## 220 0.6139 nan 0.1000 -0.0014
## 240 0.5939 nan 0.1000 -0.0009
## 260 0.5784 nan 0.1000 -0.0011
## 280 0.5621 nan 0.1000 -0.0022
## 300 0.5478 nan 0.1000 -0.0008
## 320 0.5330 nan 0.1000 -0.0004
## 340 0.5174 nan 0.1000 -0.0014
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## 380 0.4978 nan 0.1000 -0.0013
## 400 0.4864 nan 0.1000 -0.0015
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## 440 0.4637 nan 0.1000 -0.0009
## 460 0.4511 nan 0.1000 -0.0014
## 480 0.4454 nan 0.1000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2493 nan 0.1000 0.0220
## 2 1.2075 nan 0.1000 0.0150
## 3 1.1751 nan 0.1000 0.0131
## 4 1.1454 nan 0.1000 0.0133
## 5 1.1168 nan 0.1000 0.0114
## 6 1.0967 nan 0.1000 0.0068
## 7 1.0782 nan 0.1000 0.0072
## 8 1.0606 nan 0.1000 0.0052
## 9 1.0410 nan 0.1000 0.0079
## 10 1.0245 nan 0.1000 0.0050
## 20 0.9254 nan 0.1000 0.0007
## 40 0.8427 nan 0.1000 -0.0013
## 60 0.7994 nan 0.1000 -0.0026
## 80 0.7670 nan 0.1000 -0.0019
## 100 0.7412 nan 0.1000 -0.0007
## 120 0.7194 nan 0.1000 -0.0019
## 140 0.6901 nan 0.1000 -0.0021
## 160 0.6712 nan 0.1000 -0.0023
## 180 0.6507 nan 0.1000 -0.0009
## 200 0.6369 nan 0.1000 -0.0014
## 220 0.6190 nan 0.1000 -0.0011
## 240 0.5995 nan 0.1000 -0.0000
## 260 0.5823 nan 0.1000 -0.0014
## 280 0.5665 nan 0.1000 -0.0018
## 300 0.5514 nan 0.1000 -0.0009
## 320 0.5389 nan 0.1000 -0.0013
## 340 0.5212 nan 0.1000 -0.0005
## 360 0.5076 nan 0.1000 -0.0010
## 380 0.4950 nan 0.1000 -0.0003
## 400 0.4834 nan 0.1000 -0.0010
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## 440 0.4629 nan 0.1000 -0.0012
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## 480 0.4471 nan 0.1000 -0.0017
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2439 nan 0.1000 0.0247
## 2 1.1943 nan 0.1000 0.0221
## 3 1.1600 nan 0.1000 0.0141
## 4 1.1220 nan 0.1000 0.0151
## 5 1.0920 nan 0.1000 0.0118
## 6 1.0666 nan 0.1000 0.0103
## 7 1.0482 nan 0.1000 0.0083
## 8 1.0311 nan 0.1000 0.0062
## 9 1.0099 nan 0.1000 0.0068
## 10 0.9947 nan 0.1000 0.0044
## 20 0.8922 nan 0.1000 0.0019
## 40 0.7982 nan 0.1000 -0.0003
## 60 0.7406 nan 0.1000 0.0000
## 80 0.6954 nan 0.1000 -0.0019
## 100 0.6574 nan 0.1000 -0.0003
## 120 0.6281 nan 0.1000 -0.0017
## 140 0.5967 nan 0.1000 -0.0008
## 160 0.5735 nan 0.1000 -0.0020
## 180 0.5385 nan 0.1000 -0.0005
## 200 0.5100 nan 0.1000 -0.0010
## 220 0.4867 nan 0.1000 -0.0005
## 240 0.4644 nan 0.1000 -0.0011
## 260 0.4450 nan 0.1000 -0.0017
## 280 0.4271 nan 0.1000 -0.0009
## 300 0.4096 nan 0.1000 -0.0008
## 320 0.3919 nan 0.1000 -0.0009
## 340 0.3746 nan 0.1000 -0.0010
## 360 0.3556 nan 0.1000 -0.0003
## 380 0.3387 nan 0.1000 -0.0004
## 400 0.3278 nan 0.1000 -0.0008
## 420 0.3150 nan 0.1000 -0.0007
## 440 0.3039 nan 0.1000 -0.0005
## 460 0.2934 nan 0.1000 -0.0009
## 480 0.2832 nan 0.1000 -0.0004
## 500 0.2717 nan 0.1000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2416 nan 0.1000 0.0255
## 2 1.1992 nan 0.1000 0.0192
## 3 1.1538 nan 0.1000 0.0194
## 4 1.1223 nan 0.1000 0.0119
## 5 1.0938 nan 0.1000 0.0112
## 6 1.0657 nan 0.1000 0.0108
## 7 1.0467 nan 0.1000 0.0057
## 8 1.0280 nan 0.1000 0.0054
## 9 1.0099 nan 0.1000 0.0068
## 10 0.9936 nan 0.1000 0.0061
## 20 0.8886 nan 0.1000 0.0016
## 40 0.7960 nan 0.1000 -0.0012
## 60 0.7384 nan 0.1000 -0.0004
## 80 0.6980 nan 0.1000 -0.0016
## 100 0.6526 nan 0.1000 -0.0014
## 120 0.6190 nan 0.1000 -0.0009
## 140 0.5877 nan 0.1000 -0.0012
## 160 0.5580 nan 0.1000 -0.0010
## 180 0.5276 nan 0.1000 -0.0003
## 200 0.5003 nan 0.1000 -0.0004
## 220 0.4779 nan 0.1000 -0.0004
## 240 0.4551 nan 0.1000 -0.0006
## 260 0.4423 nan 0.1000 -0.0010
## 280 0.4218 nan 0.1000 -0.0008
## 300 0.4043 nan 0.1000 -0.0012
## 320 0.3867 nan 0.1000 -0.0010
## 340 0.3726 nan 0.1000 -0.0007
## 360 0.3588 nan 0.1000 -0.0006
## 380 0.3464 nan 0.1000 -0.0012
## 400 0.3323 nan 0.1000 -0.0006
## 420 0.3190 nan 0.1000 -0.0001
## 440 0.3072 nan 0.1000 -0.0002
## 460 0.2968 nan 0.1000 -0.0003
## 480 0.2868 nan 0.1000 -0.0005
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2376 nan 0.1000 0.0241
## 2 1.1904 nan 0.1000 0.0165
## 3 1.1548 nan 0.1000 0.0191
## 4 1.1268 nan 0.1000 0.0104
## 5 1.0984 nan 0.1000 0.0100
## 6 1.0760 nan 0.1000 0.0062
## 7 1.0526 nan 0.1000 0.0084
## 8 1.0267 nan 0.1000 0.0100
## 9 1.0110 nan 0.1000 0.0025
## 10 0.9957 nan 0.1000 0.0042
## 20 0.8915 nan 0.1000 0.0009
## 40 0.7966 nan 0.1000 -0.0012
## 60 0.7375 nan 0.1000 -0.0001
## 80 0.6907 nan 0.1000 -0.0022
## 100 0.6478 nan 0.1000 -0.0012
## 120 0.6136 nan 0.1000 -0.0012
## 140 0.5784 nan 0.1000 -0.0023
## 160 0.5502 nan 0.1000 -0.0007
## 180 0.5284 nan 0.1000 -0.0031
## 200 0.4969 nan 0.1000 -0.0008
## 220 0.4729 nan 0.1000 -0.0007
## 240 0.4519 nan 0.1000 -0.0006
## 260 0.4301 nan 0.1000 -0.0012
## 280 0.4119 nan 0.1000 -0.0010
## 300 0.3934 nan 0.1000 -0.0009
## 320 0.3781 nan 0.1000 -0.0012
## 340 0.3613 nan 0.1000 -0.0004
## 360 0.3459 nan 0.1000 -0.0007
## 380 0.3332 nan 0.1000 -0.0010
## 400 0.3201 nan 0.1000 -0.0007
## 420 0.3055 nan 0.1000 -0.0013
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## 460 0.2801 nan 0.1000 -0.0011
## 480 0.2726 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2290 nan 0.2000 0.0355
## 2 1.1778 nan 0.2000 0.0198
## 3 1.1427 nan 0.2000 0.0136
## 4 1.1136 nan 0.2000 0.0104
## 5 1.0850 nan 0.2000 0.0093
## 6 1.0579 nan 0.2000 0.0126
## 7 1.0400 nan 0.2000 0.0076
## 8 1.0263 nan 0.2000 0.0026
## 9 1.0079 nan 0.2000 0.0076
## 10 0.9903 nan 0.2000 0.0052
## 20 0.9101 nan 0.2000 0.0004
## 40 0.8459 nan 0.2000 -0.0026
## 60 0.8192 nan 0.2000 -0.0011
## 80 0.7977 nan 0.2000 -0.0018
## 100 0.7817 nan 0.2000 -0.0019
## 120 0.7663 nan 0.2000 -0.0020
## 140 0.7538 nan 0.2000 -0.0017
## 160 0.7422 nan 0.2000 -0.0020
## 180 0.7315 nan 0.2000 -0.0022
## 200 0.7224 nan 0.2000 -0.0002
## 220 0.7157 nan 0.2000 -0.0023
## 240 0.7050 nan 0.2000 -0.0021
## 260 0.6965 nan 0.2000 -0.0032
## 280 0.6896 nan 0.2000 -0.0026
## 300 0.6792 nan 0.2000 -0.0017
## 320 0.6764 nan 0.2000 -0.0013
## 340 0.6642 nan 0.2000 -0.0004
## 360 0.6576 nan 0.2000 -0.0017
## 380 0.6554 nan 0.2000 -0.0023
## 400 0.6471 nan 0.2000 -0.0017
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## 460 0.6309 nan 0.2000 -0.0022
## 480 0.6261 nan 0.2000 -0.0036
## 500 0.6218 nan 0.2000 -0.0027
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2119 nan 0.2000 0.0309
## 2 1.1666 nan 0.2000 0.0214
## 3 1.1332 nan 0.2000 0.0163
## 4 1.1045 nan 0.2000 0.0102
## 5 1.0789 nan 0.2000 0.0110
## 6 1.0598 nan 0.2000 0.0047
## 7 1.0347 nan 0.2000 0.0113
## 8 1.0132 nan 0.2000 0.0071
## 9 0.9983 nan 0.2000 0.0060
## 10 0.9884 nan 0.2000 0.0014
## 20 0.9090 nan 0.2000 -0.0002
## 40 0.8425 nan 0.2000 -0.0013
## 60 0.8146 nan 0.2000 -0.0046
## 80 0.7939 nan 0.2000 -0.0020
## 100 0.7800 nan 0.2000 -0.0002
## 120 0.7677 nan 0.2000 -0.0016
## 140 0.7558 nan 0.2000 -0.0014
## 160 0.7498 nan 0.2000 -0.0036
## 180 0.7383 nan 0.2000 -0.0025
## 200 0.7310 nan 0.2000 -0.0022
## 220 0.7199 nan 0.2000 -0.0024
## 240 0.7116 nan 0.2000 -0.0036
## 260 0.7011 nan 0.2000 -0.0012
## 280 0.6961 nan 0.2000 -0.0026
## 300 0.6901 nan 0.2000 -0.0017
## 320 0.6835 nan 0.2000 -0.0010
## 340 0.6759 nan 0.2000 -0.0018
## 360 0.6710 nan 0.2000 -0.0030
## 380 0.6615 nan 0.2000 -0.0019
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## 460 0.6401 nan 0.2000 -0.0019
## 480 0.6383 nan 0.2000 -0.0020
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2249 nan 0.2000 0.0337
## 2 1.1686 nan 0.2000 0.0226
## 3 1.1348 nan 0.2000 0.0134
## 4 1.1076 nan 0.2000 0.0135
## 5 1.0890 nan 0.2000 0.0061
## 6 1.0631 nan 0.2000 0.0107
## 7 1.0392 nan 0.2000 0.0087
## 8 1.0211 nan 0.2000 0.0080
## 9 1.0063 nan 0.2000 0.0073
## 10 0.9943 nan 0.2000 0.0040
## 20 0.9139 nan 0.2000 -0.0003
## 40 0.8475 nan 0.2000 -0.0006
## 60 0.8165 nan 0.2000 -0.0014
## 80 0.7992 nan 0.2000 -0.0043
## 100 0.7781 nan 0.2000 -0.0012
## 120 0.7691 nan 0.2000 -0.0012
## 140 0.7561 nan 0.2000 -0.0004
## 160 0.7455 nan 0.2000 -0.0030
## 180 0.7317 nan 0.2000 -0.0028
## 200 0.7204 nan 0.2000 -0.0025
## 220 0.7104 nan 0.2000 -0.0038
## 240 0.7070 nan 0.2000 -0.0013
## 260 0.6982 nan 0.2000 -0.0014
## 280 0.6918 nan 0.2000 -0.0020
## 300 0.6847 nan 0.2000 -0.0009
## 320 0.6799 nan 0.2000 -0.0034
## 340 0.6733 nan 0.2000 -0.0039
## 360 0.6638 nan 0.2000 -0.0015
## 380 0.6572 nan 0.2000 -0.0013
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## 460 0.6340 nan 0.2000 -0.0040
## 480 0.6306 nan 0.2000 -0.0025
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2002 nan 0.2000 0.0372
## 2 1.1511 nan 0.2000 0.0206
## 3 1.1018 nan 0.2000 0.0250
## 4 1.0608 nan 0.2000 0.0137
## 5 1.0258 nan 0.2000 0.0164
## 6 1.0025 nan 0.2000 0.0063
## 7 0.9867 nan 0.2000 0.0011
## 8 0.9662 nan 0.2000 0.0037
## 9 0.9506 nan 0.2000 0.0019
## 10 0.9353 nan 0.2000 0.0051
## 20 0.8587 nan 0.2000 -0.0016
## 40 0.7827 nan 0.2000 -0.0029
## 60 0.7332 nan 0.2000 0.0000
## 80 0.6825 nan 0.2000 -0.0040
## 100 0.6458 nan 0.2000 -0.0022
## 120 0.6075 nan 0.2000 -0.0008
## 140 0.5646 nan 0.2000 -0.0026
## 160 0.5462 nan 0.2000 -0.0013
## 180 0.5207 nan 0.2000 -0.0002
## 200 0.4962 nan 0.2000 -0.0001
## 220 0.4775 nan 0.2000 -0.0013
## 240 0.4502 nan 0.2000 -0.0026
## 260 0.4283 nan 0.2000 -0.0009
## 280 0.4123 nan 0.2000 -0.0011
## 300 0.3961 nan 0.2000 -0.0017
## 320 0.3799 nan 0.2000 -0.0004
## 340 0.3629 nan 0.2000 -0.0010
## 360 0.3475 nan 0.2000 -0.0027
## 380 0.3353 nan 0.2000 -0.0013
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## 460 0.2852 nan 0.2000 -0.0014
## 480 0.2728 nan 0.2000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1960 nan 0.2000 0.0441
## 2 1.1392 nan 0.2000 0.0274
## 3 1.1004 nan 0.2000 0.0175
## 4 1.0623 nan 0.2000 0.0127
## 5 1.0224 nan 0.2000 0.0174
## 6 0.9959 nan 0.2000 0.0078
## 7 0.9744 nan 0.2000 0.0068
## 8 0.9513 nan 0.2000 0.0077
## 9 0.9423 nan 0.2000 -0.0004
## 10 0.9302 nan 0.2000 0.0011
## 20 0.8436 nan 0.2000 -0.0026
## 40 0.7740 nan 0.2000 -0.0036
## 60 0.7156 nan 0.2000 -0.0008
## 80 0.6696 nan 0.2000 -0.0008
## 100 0.6328 nan 0.2000 -0.0013
## 120 0.6007 nan 0.2000 -0.0037
## 140 0.5747 nan 0.2000 -0.0027
## 160 0.5466 nan 0.2000 -0.0010
## 180 0.5235 nan 0.2000 -0.0022
## 200 0.5004 nan 0.2000 -0.0036
## 220 0.4792 nan 0.2000 -0.0024
## 240 0.4534 nan 0.2000 -0.0004
## 260 0.4326 nan 0.2000 -0.0038
## 280 0.4178 nan 0.2000 -0.0028
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## 320 0.3752 nan 0.2000 -0.0020
## 340 0.3564 nan 0.2000 -0.0016
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## 380 0.3268 nan 0.2000 -0.0025
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## 460 0.2784 nan 0.2000 -0.0013
## 480 0.2647 nan 0.2000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2043 nan 0.2000 0.0419
## 2 1.1423 nan 0.2000 0.0294
## 3 1.0912 nan 0.2000 0.0203
## 4 1.0573 nan 0.2000 0.0123
## 5 1.0314 nan 0.2000 0.0088
## 6 1.0065 nan 0.2000 0.0084
## 7 0.9847 nan 0.2000 0.0056
## 8 0.9559 nan 0.2000 0.0117
## 9 0.9406 nan 0.2000 0.0042
## 10 0.9239 nan 0.2000 0.0037
## 20 0.8341 nan 0.2000 0.0001
## 40 0.7675 nan 0.2000 -0.0037
## 60 0.7238 nan 0.2000 -0.0014
## 80 0.6674 nan 0.2000 -0.0040
## 100 0.6294 nan 0.2000 -0.0013
## 120 0.5911 nan 0.2000 -0.0038
## 140 0.5596 nan 0.2000 -0.0013
## 160 0.5356 nan 0.2000 -0.0012
## 180 0.5048 nan 0.2000 -0.0017
## 200 0.4805 nan 0.2000 -0.0037
## 220 0.4592 nan 0.2000 -0.0024
## 240 0.4385 nan 0.2000 -0.0019
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## 320 0.3701 nan 0.2000 -0.0014
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1923 nan 0.2000 0.0440
## 2 1.1062 nan 0.2000 0.0312
## 3 1.0538 nan 0.2000 0.0184
## 4 1.0149 nan 0.2000 0.0128
## 5 0.9848 nan 0.2000 0.0104
## 6 0.9611 nan 0.2000 0.0061
## 7 0.9422 nan 0.2000 0.0063
## 8 0.9198 nan 0.2000 0.0051
## 9 0.9016 nan 0.2000 0.0038
## 10 0.8832 nan 0.2000 0.0037
## 20 0.8018 nan 0.2000 0.0001
## 40 0.7043 nan 0.2000 0.0011
## 60 0.6246 nan 0.2000 -0.0034
## 80 0.5596 nan 0.2000 -0.0011
## 100 0.5017 nan 0.2000 -0.0017
## 120 0.4570 nan 0.2000 -0.0009
## 140 0.4243 nan 0.2000 -0.0019
## 160 0.3922 nan 0.2000 -0.0012
## 180 0.3648 nan 0.2000 -0.0005
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## 220 0.3125 nan 0.2000 -0.0011
## 240 0.2922 nan 0.2000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1941 nan 0.2000 0.0464
## 2 1.1227 nan 0.2000 0.0289
## 3 1.0700 nan 0.2000 0.0217
## 4 1.0320 nan 0.2000 0.0144
## 5 0.9913 nan 0.2000 0.0124
## 6 0.9703 nan 0.2000 0.0055
## 7 0.9462 nan 0.2000 0.0080
## 8 0.9222 nan 0.2000 0.0079
## 9 0.9033 nan 0.2000 0.0036
## 10 0.8791 nan 0.2000 0.0032
## 20 0.8036 nan 0.2000 -0.0036
## 40 0.7034 nan 0.2000 -0.0014
## 60 0.6374 nan 0.2000 -0.0044
## 80 0.5821 nan 0.2000 -0.0036
## 100 0.5230 nan 0.2000 -0.0027
## 120 0.4803 nan 0.2000 -0.0009
## 140 0.4378 nan 0.2000 -0.0009
## 160 0.4002 nan 0.2000 -0.0029
## 180 0.3686 nan 0.2000 -0.0027
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## 340 0.2061 nan 0.2000 -0.0004
## 360 0.1949 nan 0.2000 -0.0015
## 380 0.1806 nan 0.2000 -0.0009
## 400 0.1688 nan 0.2000 -0.0014
## 420 0.1575 nan 0.2000 -0.0006
## 440 0.1493 nan 0.2000 -0.0009
## 460 0.1384 nan 0.2000 -0.0008
## 480 0.1317 nan 0.2000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1891 nan 0.2000 0.0475
## 2 1.1444 nan 0.2000 0.0131
## 3 1.0873 nan 0.2000 0.0220
## 4 1.0437 nan 0.2000 0.0167
## 5 1.0138 nan 0.2000 0.0097
## 6 0.9806 nan 0.2000 0.0104
## 7 0.9577 nan 0.2000 0.0042
## 8 0.9391 nan 0.2000 -0.0009
## 9 0.9211 nan 0.2000 0.0040
## 10 0.9128 nan 0.2000 -0.0037
## 20 0.8104 nan 0.2000 -0.0006
## 40 0.7139 nan 0.2000 -0.0038
## 60 0.6309 nan 0.2000 -0.0037
## 80 0.5671 nan 0.2000 -0.0053
## 100 0.5198 nan 0.2000 -0.0027
## 120 0.4734 nan 0.2000 -0.0014
## 140 0.4384 nan 0.2000 -0.0016
## 160 0.4013 nan 0.2000 -0.0014
## 180 0.3761 nan 0.2000 -0.0014
## 200 0.3527 nan 0.2000 -0.0030
## 220 0.3243 nan 0.2000 -0.0032
## 240 0.3003 nan 0.2000 -0.0017
## 260 0.2756 nan 0.2000 -0.0012
## 280 0.2561 nan 0.2000 -0.0010
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## 320 0.2235 nan 0.2000 -0.0014
## 340 0.2088 nan 0.2000 -0.0024
## 360 0.1950 nan 0.2000 -0.0009
## 380 0.1826 nan 0.2000 -0.0004
## 400 0.1704 nan 0.2000 -0.0013
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## 460 0.1395 nan 0.2000 -0.0006
## 480 0.1322 nan 0.2000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1956 nan 0.3000 0.0457
## 2 1.1417 nan 0.3000 0.0290
## 3 1.0982 nan 0.3000 0.0165
## 4 1.0636 nan 0.3000 0.0147
## 5 1.0308 nan 0.3000 0.0129
## 6 1.0120 nan 0.3000 0.0078
## 7 0.9962 nan 0.3000 0.0014
## 8 0.9712 nan 0.3000 0.0061
## 9 0.9654 nan 0.3000 -0.0010
## 10 0.9477 nan 0.3000 -0.0004
## 20 0.8889 nan 0.3000 -0.0041
## 40 0.8250 nan 0.3000 -0.0010
## 60 0.7980 nan 0.3000 -0.0033
## 80 0.7732 nan 0.3000 -0.0055
## 100 0.7548 nan 0.3000 -0.0042
## 120 0.7411 nan 0.3000 -0.0022
## 140 0.7299 nan 0.3000 -0.0013
## 160 0.7170 nan 0.3000 -0.0054
## 180 0.7037 nan 0.3000 -0.0045
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## 220 0.6810 nan 0.3000 -0.0063
## 240 0.6747 nan 0.3000 -0.0020
## 260 0.6592 nan 0.3000 -0.0037
## 280 0.6485 nan 0.3000 -0.0017
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## 320 0.6414 nan 0.3000 -0.0034
## 340 0.6341 nan 0.3000 -0.0037
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## 380 0.6183 nan 0.3000 -0.0029
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## 420 0.5988 nan 0.3000 -0.0031
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## 460 0.5814 nan 0.3000 -0.0043
## 480 0.5789 nan 0.3000 -0.0043
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1940 nan 0.3000 0.0450
## 2 1.1411 nan 0.3000 0.0119
## 3 1.0882 nan 0.3000 0.0217
## 4 1.0543 nan 0.3000 0.0131
## 5 1.0329 nan 0.3000 0.0080
## 6 1.0028 nan 0.3000 0.0079
## 7 0.9951 nan 0.3000 -0.0015
## 8 0.9667 nan 0.3000 0.0129
## 9 0.9533 nan 0.3000 0.0009
## 10 0.9413 nan 0.3000 0.0058
## 20 0.8740 nan 0.3000 0.0016
## 40 0.8267 nan 0.3000 -0.0009
## 60 0.7927 nan 0.3000 0.0006
## 80 0.7740 nan 0.3000 -0.0019
## 100 0.7518 nan 0.3000 -0.0008
## 120 0.7429 nan 0.3000 -0.0047
## 140 0.7293 nan 0.3000 -0.0043
## 160 0.7151 nan 0.3000 -0.0019
## 180 0.7114 nan 0.3000 -0.0042
## 200 0.7019 nan 0.3000 -0.0047
## 220 0.6842 nan 0.3000 -0.0037
## 240 0.6744 nan 0.3000 -0.0059
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## 280 0.6577 nan 0.3000 -0.0021
## 300 0.6464 nan 0.3000 -0.0029
## 320 0.6325 nan 0.3000 -0.0004
## 340 0.6276 nan 0.3000 -0.0026
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## 380 0.6137 nan 0.3000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2035 nan 0.3000 0.0357
## 2 1.1386 nan 0.3000 0.0280
## 3 1.0942 nan 0.3000 0.0164
## 4 1.0582 nan 0.3000 0.0181
## 5 1.0275 nan 0.3000 0.0145
## 6 1.0036 nan 0.3000 0.0079
## 7 0.9801 nan 0.3000 0.0066
## 8 0.9692 nan 0.3000 0.0049
## 9 0.9559 nan 0.3000 0.0006
## 10 0.9422 nan 0.3000 0.0061
## 20 0.8704 nan 0.3000 -0.0040
## 40 0.8051 nan 0.3000 -0.0017
## 60 0.7884 nan 0.3000 -0.0006
## 80 0.7689 nan 0.3000 -0.0019
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## 140 0.7094 nan 0.3000 -0.0044
## 160 0.6999 nan 0.3000 -0.0043
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## 240 0.6622 nan 0.3000 -0.0032
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## 280 0.6543 nan 0.3000 -0.0065
## 300 0.6445 nan 0.3000 -0.0023
## 320 0.6355 nan 0.3000 -0.0017
## 340 0.6293 nan 0.3000 -0.0029
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## 380 0.6184 nan 0.3000 -0.0025
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## 480 0.5770 nan 0.3000 -0.0038
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1686 nan 0.3000 0.0552
## 2 1.0876 nan 0.3000 0.0355
## 3 1.0338 nan 0.3000 0.0192
## 4 0.9924 nan 0.3000 0.0116
## 5 0.9585 nan 0.3000 0.0119
## 6 0.9421 nan 0.3000 -0.0014
## 7 0.9259 nan 0.3000 -0.0013
## 8 0.9059 nan 0.3000 0.0054
## 9 0.8893 nan 0.3000 0.0046
## 10 0.8776 nan 0.3000 -0.0005
## 20 0.8069 nan 0.3000 -0.0055
## 40 0.7342 nan 0.3000 -0.0115
## 60 0.6738 nan 0.3000 -0.0054
## 80 0.6167 nan 0.3000 -0.0073
## 100 0.5617 nan 0.3000 -0.0002
## 120 0.5292 nan 0.3000 -0.0028
## 140 0.4883 nan 0.3000 -0.0040
## 160 0.4478 nan 0.3000 -0.0004
## 180 0.4130 nan 0.3000 -0.0012
## 200 0.3798 nan 0.3000 -0.0010
## 220 0.3591 nan 0.3000 0.0003
## 240 0.3333 nan 0.3000 -0.0014
## 260 0.3175 nan 0.3000 -0.0019
## 280 0.2968 nan 0.3000 -0.0016
## 300 0.2804 nan 0.3000 -0.0016
## 320 0.2649 nan 0.3000 -0.0017
## 340 0.2469 nan 0.3000 -0.0011
## 360 0.2319 nan 0.3000 -0.0025
## 380 0.2174 nan 0.3000 -0.0006
## 400 0.2037 nan 0.3000 -0.0009
## 420 0.1901 nan 0.3000 -0.0010
## 440 0.1820 nan 0.3000 -0.0013
## 460 0.1734 nan 0.3000 -0.0010
## 480 0.1652 nan 0.3000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1648 nan 0.3000 0.0583
## 2 1.0964 nan 0.3000 0.0262
## 3 1.0409 nan 0.3000 0.0206
## 4 0.9892 nan 0.3000 0.0236
## 5 0.9622 nan 0.3000 0.0081
## 6 0.9466 nan 0.3000 -0.0021
## 7 0.9273 nan 0.3000 0.0025
## 8 0.9029 nan 0.3000 0.0068
## 9 0.8954 nan 0.3000 -0.0058
## 10 0.8852 nan 0.3000 -0.0027
## 20 0.8084 nan 0.3000 -0.0030
## 40 0.7376 nan 0.3000 -0.0042
## 60 0.6659 nan 0.3000 -0.0032
## 80 0.6169 nan 0.3000 -0.0038
## 100 0.5787 nan 0.3000 -0.0004
## 120 0.5381 nan 0.3000 0.0001
## 140 0.5039 nan 0.3000 -0.0017
## 160 0.4729 nan 0.3000 -0.0013
## 180 0.4436 nan 0.3000 -0.0006
## 200 0.4083 nan 0.3000 -0.0025
## 220 0.3824 nan 0.3000 -0.0019
## 240 0.3522 nan 0.3000 -0.0035
## 260 0.3364 nan 0.3000 -0.0011
## 280 0.3185 nan 0.3000 -0.0026
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## 320 0.2745 nan 0.3000 -0.0010
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1819 nan 0.3000 0.0587
## 2 1.1070 nan 0.3000 0.0319
## 3 1.0498 nan 0.3000 0.0207
## 4 1.0039 nan 0.3000 0.0141
## 5 0.9761 nan 0.3000 0.0033
## 6 0.9501 nan 0.3000 0.0086
## 7 0.9301 nan 0.3000 0.0065
## 8 0.9124 nan 0.3000 0.0026
## 9 0.8952 nan 0.3000 0.0052
## 10 0.8824 nan 0.3000 -0.0003
## 20 0.8159 nan 0.3000 -0.0013
## 40 0.7452 nan 0.3000 -0.0036
## 60 0.6688 nan 0.3000 -0.0003
## 80 0.6123 nan 0.3000 -0.0015
## 100 0.5634 nan 0.3000 -0.0069
## 120 0.5250 nan 0.3000 -0.0043
## 140 0.4898 nan 0.3000 -0.0039
## 160 0.4485 nan 0.3000 -0.0018
## 180 0.4295 nan 0.3000 -0.0050
## 200 0.3993 nan 0.3000 -0.0067
## 220 0.3705 nan 0.3000 -0.0007
## 240 0.3543 nan 0.3000 -0.0021
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## 280 0.3102 nan 0.3000 -0.0009
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## 320 0.2755 nan 0.3000 -0.0031
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1449 nan 0.3000 0.0600
## 2 1.0439 nan 0.3000 0.0438
## 3 0.9956 nan 0.3000 0.0111
## 4 0.9574 nan 0.3000 0.0044
## 5 0.9313 nan 0.3000 0.0060
## 6 0.8996 nan 0.3000 0.0112
## 7 0.8854 nan 0.3000 -0.0019
## 8 0.8695 nan 0.3000 -0.0015
## 9 0.8571 nan 0.3000 -0.0022
## 10 0.8475 nan 0.3000 -0.0045
## 20 0.7469 nan 0.3000 -0.0061
## 40 0.6476 nan 0.3000 -0.0059
## 60 0.5655 nan 0.3000 -0.0051
## 80 0.4909 nan 0.3000 -0.0019
## 100 0.4275 nan 0.3000 -0.0011
## 120 0.3757 nan 0.3000 -0.0024
## 140 0.3412 nan 0.3000 -0.0032
## 160 0.2970 nan 0.3000 0.0003
## 180 0.2626 nan 0.3000 -0.0008
## 200 0.2390 nan 0.3000 -0.0016
## 220 0.2154 nan 0.3000 -0.0008
## 240 0.1922 nan 0.3000 -0.0024
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## 280 0.1663 nan 0.3000 -0.0012
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## 320 0.1386 nan 0.3000 -0.0014
## 340 0.1258 nan 0.3000 -0.0006
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## 380 0.1025 nan 0.3000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1566 nan 0.3000 0.0445
## 2 1.0613 nan 0.3000 0.0356
## 3 1.0112 nan 0.3000 0.0174
## 4 0.9795 nan 0.3000 0.0054
## 5 0.9588 nan 0.3000 -0.0005
## 6 0.9350 nan 0.3000 0.0055
## 7 0.9103 nan 0.3000 -0.0016
## 8 0.8904 nan 0.3000 0.0055
## 9 0.8734 nan 0.3000 -0.0024
## 10 0.8641 nan 0.3000 -0.0068
## 20 0.7751 nan 0.3000 -0.0077
## 40 0.6394 nan 0.3000 -0.0023
## 60 0.5555 nan 0.3000 -0.0023
## 80 0.4899 nan 0.3000 -0.0066
## 100 0.4433 nan 0.3000 -0.0050
## 120 0.3858 nan 0.3000 -0.0031
## 140 0.3397 nan 0.3000 -0.0029
## 160 0.3013 nan 0.3000 -0.0033
## 180 0.2711 nan 0.3000 -0.0029
## 200 0.2398 nan 0.3000 -0.0045
## 220 0.2127 nan 0.3000 -0.0011
## 240 0.1927 nan 0.3000 -0.0012
## 260 0.1713 nan 0.3000 -0.0016
## 280 0.1544 nan 0.3000 -0.0013
## 300 0.1417 nan 0.3000 -0.0026
## 320 0.1292 nan 0.3000 -0.0012
## 340 0.1190 nan 0.3000 -0.0010
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## 380 0.0994 nan 0.3000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1565 nan 0.3000 0.0633
## 2 1.0805 nan 0.3000 0.0303
## 3 1.0195 nan 0.3000 0.0246
## 4 0.9706 nan 0.3000 0.0144
## 5 0.9352 nan 0.3000 0.0109
## 6 0.9048 nan 0.3000 0.0089
## 7 0.8751 nan 0.3000 0.0079
## 8 0.8614 nan 0.3000 -0.0048
## 9 0.8483 nan 0.3000 -0.0008
## 10 0.8455 nan 0.3000 -0.0086
## 20 0.7611 nan 0.3000 -0.0113
## 40 0.6389 nan 0.3000 -0.0070
## 60 0.5471 nan 0.3000 -0.0116
## 80 0.4790 nan 0.3000 -0.0042
## 100 0.4320 nan 0.3000 -0.0053
## 120 0.3837 nan 0.3000 -0.0041
## 140 0.3424 nan 0.3000 -0.0019
## 160 0.3070 nan 0.3000 -0.0023
## 180 0.2661 nan 0.3000 -0.0005
## 200 0.2488 nan 0.3000 -0.0035
## 220 0.2255 nan 0.3000 -0.0024
## 240 0.2039 nan 0.3000 -0.0013
## 260 0.1815 nan 0.3000 -0.0001
## 280 0.1660 nan 0.3000 -0.0001
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## 320 0.1377 nan 0.3000 -0.0017
## 340 0.1265 nan 0.3000 -0.0012
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## 380 0.1053 nan 0.3000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1484 nan 0.5000 0.0674
## 2 1.0791 nan 0.5000 0.0235
## 3 1.0318 nan 0.5000 0.0266
## 4 1.0034 nan 0.5000 0.0085
## 5 0.9644 nan 0.5000 0.0166
## 6 0.9481 nan 0.5000 -0.0004
## 7 0.9348 nan 0.5000 0.0014
## 8 0.9216 nan 0.5000 0.0010
## 9 0.9063 nan 0.5000 0.0054
## 10 0.8988 nan 0.5000 -0.0013
## 20 0.8450 nan 0.5000 0.0007
## 40 0.8026 nan 0.5000 -0.0094
## 60 0.9953 nan 0.5000 0.0002
## 80 0.9754 nan 0.5000 -0.0006
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1416 nan 0.5000 0.0478
## 2 1.0705 nan 0.5000 0.0196
## 3 1.0139 nan 0.5000 0.0216
## 4 0.9922 nan 0.5000 0.0063
## 5 0.9791 nan 0.5000 0.0012
## 6 0.9486 nan 0.5000 0.0136
## 7 0.9282 nan 0.5000 0.0025
## 8 0.9142 nan 0.5000 -0.0037
## 9 0.9073 nan 0.5000 -0.0022
## 10 0.9013 nan 0.5000 -0.0020
## 20 0.8485 nan 0.5000 0.0011
## 40 0.7967 nan 0.5000 0.0016
## 60 0.7667 nan 0.5000 -0.0026
## 80 0.7593 nan 0.5000 0.0116
## 100 0.7341 nan 0.5000 -0.0081
## 120 0.7241 nan 0.5000 -0.0052
## 140 0.7053 nan 0.5000 -0.0103
## 160 0.6907 nan 0.5000 -0.0025
## 180 0.6807 nan 0.5000 -0.0044
## 200 0.6499 nan 0.5000 -0.0024
## 220 0.6388 nan 0.5000 -0.0056
## 240 0.6128 nan 0.5000 0.0001
## 260 0.6088 nan 0.5000 -0.0079
## 280 0.5954 nan 0.5000 -0.0038
## 300 0.5842 nan 0.5000 -0.0038
## 320 0.7547 nan 0.5000 -0.0010
## 340 0.7404 nan 0.5000 -0.0014
## 360 0.7314 nan 0.5000 -0.0039
## 380 24784.4697 nan 0.5000 -0.0020
## 400 24784.4694 nan 0.5000 -0.0006
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## 460 24784.3534 nan 0.5000 0.0001
## 480 24808.2080 nan 0.5000 -0.0007
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1522 nan 0.5000 0.0615
## 2 1.1036 nan 0.5000 0.0080
## 3 1.0449 nan 0.5000 0.0240
## 4 1.0003 nan 0.5000 0.0171
## 5 0.9810 nan 0.5000 0.0022
## 6 0.9665 nan 0.5000 0.0016
## 7 0.9550 nan 0.5000 -0.0052
## 8 0.9440 nan 0.5000 0.0007
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## 10 0.9254 nan 0.5000 -0.0063
## 20 0.8494 nan 0.5000 -0.0039
## 40 0.8078 nan 0.5000 -0.0096
## 60 0.7684 nan 0.5000 -0.0048
## 80 0.7449 nan 0.5000 -0.0003
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## 140 0.6832 nan 0.5000 -0.0021
## 160 0.6754 nan 0.5000 -0.0062
## 180 0.6564 nan 0.5000 -0.0029
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## 280 0.5917 nan 0.5000 -0.0057
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## 320 0.5668 nan 0.5000 -0.0045
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1218 nan 0.5000 0.0749
## 2 1.0150 nan 0.5000 0.0414
## 3 0.9762 nan 0.5000 0.0028
## 4 0.9417 nan 0.5000 0.0095
## 5 0.9245 nan 0.5000 -0.0013
## 6 0.9079 nan 0.5000 -0.0058
## 7 0.8961 nan 0.5000 -0.0027
## 8 0.8801 nan 0.5000 -0.0013
## 9 0.8766 nan 0.5000 -0.0120
## 10 0.8618 nan 0.5000 -0.0028
## 20 0.7967 nan 0.5000 -0.0073
## 40 0.6882 nan 0.5000 -0.0018
## 60 0.8248 nan 0.5000 -0.0028
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1191 nan 0.5000 0.0897
## 2 1.0111 nan 0.5000 0.0454
## 3 0.9741 nan 0.5000 0.0142
## 4 0.9459 nan 0.5000 0.0056
## 5 0.9273 nan 0.5000 -0.0024
## 6 0.8952 nan 0.5000 0.0089
## 7 0.8799 nan 0.5000 0.0027
## 8 0.8649 nan 0.5000 0.0015
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## 10 0.8432 nan 0.5000 -0.0017
## 20 0.7792 nan 0.5000 -0.0051
## 40 0.6641 nan 0.5000 0.0060
## 60 0.5976 nan 0.5000 -0.0118
## 80 0.5528 nan 0.5000 -0.0044
## 100 0.5084 nan 0.5000 -0.0090
## 120 0.4530 nan 0.5000 -0.0035
## 140 0.4077 nan 0.5000 -0.0072
## 160 0.3722 nan 0.5000 -0.0077
## 180 0.3431 nan 0.5000 -0.0032
## 200 0.2993 nan 0.5000 -0.0045
## 220 0.2696 nan 0.5000 0.0001
## 240 0.2424 nan 0.5000 -0.0032
## 260 0.2168 nan 0.5000 -0.0028
## 280 0.2012 nan 0.5000 -0.0034
## 300 0.1812 nan 0.5000 -0.0028
## 320 0.1695 nan 0.5000 -0.0022
## 340 0.1525 nan 0.5000 -0.0016
## 360 0.1374 nan 0.5000 -0.0012
## 380 0.1275 nan 0.5000 -0.0018
## 400 0.1208 nan 0.5000 -0.0029
## 420 0.1087 nan 0.5000 -0.0023
## 440 0.1024 nan 0.5000 -0.0011
## 460 0.0935 nan 0.5000 -0.0007
## 480 0.0839 nan 0.5000 -0.0005
## 500 0.0781 nan 0.5000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1248 nan 0.5000 0.0749
## 2 1.0407 nan 0.5000 0.0376
## 3 0.9916 nan 0.5000 0.0047
## 4 0.9460 nan 0.5000 0.0088
## 5 0.9329 nan 0.5000 -0.0065
## 6 0.9112 nan 0.5000 0.0003
## 7 0.8868 nan 0.5000 0.0008
## 8 0.8718 nan 0.5000 -0.0045
## 9 0.8528 nan 0.5000 0.0042
## 10 0.8549 nan 0.5000 -0.0174
## 20 0.8045 nan 0.5000 -0.0047
## 40 0.7341 nan 0.5000 -0.0107
## 60 0.6571 nan 0.5000 -0.0130
## 80 0.6261 nan 0.5000 -0.0063
## 100 0.5196 nan 0.5000 -0.0003
## 120 0.4836 nan 0.5000 -0.0136
## 140 0.4405 nan 0.5000 -0.0102
## 160 0.3863 nan 0.5000 -0.0101
## 180 0.3429 nan 0.5000 -0.0025
## 200 0.3039 nan 0.5000 -0.0020
## 220 0.2693 nan 0.5000 -0.0029
## 240 0.2504 nan 0.5000 -0.0032
## 260 0.2286 nan 0.5000 -0.0036
## 280 0.2049 nan 0.5000 -0.0020
## 300 0.1850 nan 0.5000 -0.0034
## 320 0.1734 nan 0.5000 -0.0027
## 340 0.1581 nan 0.5000 -0.0010
## 360 0.1496 nan 0.5000 -0.0033
## 380 0.1341 nan 0.5000 -0.0022
## 400 0.1211 nan 0.5000 -0.0001
## 420 0.1125 nan 0.5000 -0.0023
## 440 0.1013 nan 0.5000 -0.0010
## 460 0.0924 nan 0.5000 -0.0012
## 480 0.0856 nan 0.5000 -0.0023
## 500 0.0811 nan 0.5000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1040 nan 0.5000 0.0865
## 2 1.0286 nan 0.5000 0.0200
## 3 0.9603 nan 0.5000 0.0239
## 4 0.9229 nan 0.5000 0.0035
## 5 0.8947 nan 0.5000 -0.0004
## 6 0.8661 nan 0.5000 0.0050
## 7 0.8539 nan 0.5000 -0.0047
## 8 0.8402 nan 0.5000 -0.0146
## 9 0.8139 nan 0.5000 0.0057
## 10 0.8008 nan 0.5000 -0.0054
## 20 3.5636 nan 0.5000 -0.0095
## 40 3.5028 nan 0.5000 -0.0218
## 60 3.3449 nan 0.5000 -0.0116
## 80 3.2498 nan 0.5000 -0.0083
## 100 inf nan 0.5000 -0.0107
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
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## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0856 nan 0.5000 0.0887
## 2 0.9858 nan 0.5000 0.0423
## 3 0.9317 nan 0.5000 0.0137
## 4 0.8921 nan 0.5000 0.0036
## 5 0.8630 nan 0.5000 -0.0060
## 6 0.8511 nan 0.5000 -0.0154
## 7 0.8307 nan 0.5000 -0.0062
## 8 0.8155 nan 0.5000 -0.0210
## 9 0.7990 nan 0.5000 -0.0049
## 10 0.7847 nan 0.5000 -0.0042
## 20 0.6700 nan 0.5000 -0.0017
## 40 0.5282 nan 0.5000 -0.0110
## 60 0.4378 nan 0.5000 -0.0117
## 80 0.3578 nan 0.5000 -0.0011
## 100 0.2911 nan 0.5000 -0.0052
## 120 0.2487 nan 0.5000 -0.0076
## 140 0.2195 nan 0.5000 -0.0030
## 160 0.1897 nan 0.5000 -0.0051
## 180 0.1539 nan 0.5000 -0.0008
## 200 0.1365 nan 0.5000 -0.0033
## 220 0.1116 nan 0.5000 -0.0016
## 240 0.0979 nan 0.5000 0.0000
## 260 0.0877 nan 0.5000 -0.0004
## 280 0.0787 nan 0.5000 -0.0013
## 300 0.0689 nan 0.5000 -0.0013
## 320 0.0617 nan 0.5000 -0.0005
## 340 0.0538 nan 0.5000 -0.0010
## 360 0.0466 nan 0.5000 -0.0012
## 380 0.0399 nan 0.5000 -0.0002
## 400 0.0361 nan 0.5000 -0.0004
## 420 0.0318 nan 0.5000 -0.0002
## 440 0.0276 nan 0.5000 -0.0003
## 460 0.0253 nan 0.5000 -0.0003
## 480 0.0221 nan 0.5000 -0.0000
## 500 0.0203 nan 0.5000 -0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1113 nan 0.5000 0.0792
## 2 1.0204 nan 0.5000 0.0229
## 3 0.9576 nan 0.5000 0.0216
## 4 0.9142 nan 0.5000 0.0069
## 5 0.9111 nan 0.5000 -0.0295
## 6 0.8803 nan 0.5000 0.0024
## 7 0.8542 nan 0.5000 -0.0017
## 8 0.8400 nan 0.5000 0.0000
## 9 0.8214 nan 0.5000 -0.0013
## 10 0.8028 nan 0.5000 -0.0080
## 20 0.6789 nan 0.5000 -0.0080
## 40 0.5761 nan 0.5000 -0.0100
## 60 0.4873 nan 0.5000 -0.0062
## 80 0.4004 nan 0.5000 -0.0089
## 100 0.3202 nan 0.5000 -0.0029
## 120 0.2660 nan 0.5000 -0.0036
## 140 0.2199 nan 0.5000 -0.0034
## 160 0.1719 nan 0.5000 -0.0034
## 180 0.1460 nan 0.5000 -0.0045
## 200 0.1275 nan 0.5000 -0.0041
## 220 0.1108 nan 0.5000 -0.0027
## 240 0.0937 nan 0.5000 -0.0007
## 260 0.0806 nan 0.5000 -0.0006
## 280 0.0717 nan 0.5000 -0.0010
## 300 0.0642 nan 0.5000 -0.0014
## 320 0.0570 nan 0.5000 -0.0007
## 340 0.0486 nan 0.5000 -0.0002
## 360 0.0429 nan 0.5000 -0.0000
## 380 0.0406 nan 0.5000 -0.0007
## 400 0.0357 nan 0.5000 -0.0002
## 420 0.0310 nan 0.5000 -0.0006
## 440 0.0277 nan 0.5000 -0.0007
## 460 0.0242 nan 0.5000 0.0000
## 480 0.0219 nan 0.5000 -0.0004
## 500 0.0197 nan 0.5000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1246 nan 1.0000 0.0498
## 2 1.0445 nan 1.0000 0.0330
## 3 0.9964 nan 1.0000 0.0085
## 4 0.9675 nan 1.0000 0.0056
## 5 0.9583 nan 1.0000 -0.0063
## 6 0.9483 nan 1.0000 -0.0099
## 7 0.9494 nan 1.0000 -0.0170
## 8 0.9466 nan 1.0000 -0.0094
## 9 0.9369 nan 1.0000 -0.0007
## 10 0.9638 nan 1.0000 -0.0673
## 20 0.9451 nan 1.0000 -0.0087
## 40 inf nan 1.0000 -0.0291
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000 nan 1.0000 0.0007
## 300 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000 nan 1.0000 -0.0219
## 320 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000 nan 1.0000 0.0027
## 340 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000 nan 1.0000 -0.0053
## 360 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000 nan 1.0000 -0.0016
## 380 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000 nan 1.0000 -0.0010
## 400 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000 nan 1.0000 -0.0008
## 420 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000 nan 1.0000 0.0047
## 440 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000 nan 1.0000 -0.0009
## 460 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000 nan 1.0000 -0.0043
## 480 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000 nan 1.0000 0.0000
## 500 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000 nan 1.0000 -0.0034
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1114 nan 1.0000 0.1011
## 2 1.0635 nan 1.0000 -0.0015
## 3 1.0018 nan 1.0000 0.0213
## 4 0.9738 nan 1.0000 -0.0069
## 5 0.9725 nan 1.0000 -0.0342
## 6 0.9527 nan 1.0000 -0.0042
## 7 0.9346 nan 1.0000 0.0042
## 8 0.9437 nan 1.0000 -0.0181
## 9 0.9378 nan 1.0000 -0.0056
## 10 0.9154 nan 1.0000 0.0076
## 20 0.9271 nan 1.0000 -0.0164
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 200 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1100 nan 1.0000 0.1054
## 2 1.0353 nan 1.0000 0.0213
## 3 0.9730 nan 1.0000 0.0168
## 4 0.9677 nan 1.0000 -0.0135
## 5 0.9538 nan 1.0000 -0.0067
## 6 0.9256 nan 1.0000 0.0129
## 7 0.9413 nan 1.0000 -0.0265
## 8 0.9236 nan 1.0000 0.0003
## 9 0.9085 nan 1.0000 -0.0166
## 10 0.8965 nan 1.0000 -0.0042
## 20 0.8550 nan 1.0000 -0.0197
## 40 0.7972 nan 1.0000 -0.0012
## 60 0.8010 nan 1.0000 -0.0172
## 80 1.4753 nan 1.0000 -0.3277
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0488 nan 1.0000 0.1211
## 2 0.9782 nan 1.0000 0.0133
## 3 0.9646 nan 1.0000 -0.0197
## 4 0.9465 nan 1.0000 -0.0222
## 5 0.9373 nan 1.0000 -0.0243
## 6 0.9464 nan 1.0000 -0.0263
## 7 0.9561 nan 1.0000 -0.0342
## 8 0.9550 nan 1.0000 -0.0271
## 9 0.9465 nan 1.0000 -0.0194
## 10 0.9582 nan 1.0000 -0.0366
## 20 0.9325 nan 1.0000 -0.0403
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0597 nan 1.0000 0.0818
## 2 0.9786 nan 1.0000 0.0153
## 3 0.9384 nan 1.0000 0.0061
## 4 0.9098 nan 1.0000 -0.0001
## 5 0.9203 nan 1.0000 -0.0400
## 6 0.9205 nan 1.0000 -0.0223
## 7 0.9156 nan 1.0000 -0.0326
## 8 0.9154 nan 1.0000 -0.0172
## 9 0.8951 nan 1.0000 -0.0097
## 10 0.8921 nan 1.0000 -0.0202
## 20 0.9368 nan 1.0000 -0.0281
## 40 0.9183 nan 1.0000 -0.0776
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0463 nan 1.0000 0.1023
## 2 0.9348 nan 1.0000 0.0428
## 3 0.9226 nan 1.0000 -0.0191
## 4 0.8837 nan 1.0000 0.0043
## 5 0.8839 nan 1.0000 -0.0286
## 6 0.8885 nan 1.0000 -0.0331
## 7 0.9106 nan 1.0000 -0.0520
## 8 0.8884 nan 1.0000 -0.0188
## 9 0.8873 nan 1.0000 -0.0357
## 10 0.8731 nan 1.0000 -0.0099
## 20 0.8340 nan 1.0000 -0.0307
## 40 0.6796 nan 1.0000 -0.0032
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0415 nan 1.0000 0.0998
## 2 0.9470 nan 1.0000 0.0302
## 3 0.9102 nan 1.0000 -0.0232
## 4 0.9289 nan 1.0000 -0.0445
## 5 0.9483 nan 1.0000 -0.0768
## 6 0.9242 nan 1.0000 -0.0167
## 7 1.3181 nan 1.0000 -0.0221
## 8 1.3492 nan 1.0000 -0.0839
## 9 1.3115 nan 1.0000 -0.0237
## 10 1.2841 nan 1.0000 -0.0093
## 20 1.2950 nan 1.0000 -0.0306
## 40 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0181 nan 1.0000 0.1361
## 2 0.9541 nan 1.0000 -0.0060
## 3 0.9239 nan 1.0000 -0.0132
## 4 0.9510 nan 1.0000 -0.0724
## 5 0.9344 nan 1.0000 -0.0270
## 6 0.9829 nan 1.0000 -0.0932
## 7 0.9221 nan 1.0000 -0.0016
## 8 0.9085 nan 1.0000 -0.0385
## 9 0.8486 nan 1.0000 0.0052
## 10 0.8475 nan 1.0000 -0.0407
## 20 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0534 nan 1.0000 0.0981
## 2 0.9539 nan 1.0000 0.0390
## 3 0.9923 nan 1.0000 -0.0641
## 4 1.2407 nan 1.0000 -0.3070
## 5 1.2011 nan 1.0000 -0.0022
## 6 1.2235 nan 1.0000 -0.0637
## 7 1.2521 nan 1.0000 -0.0779
## 8 1.2221 nan 1.0000 -0.0164
## 9 1.2440 nan 1.0000 -0.0566
## 10 1.2352 nan 1.0000 -0.0352
## 20 1.1388 nan 1.0000 -0.0346
## 40 208258942306149.3750 nan 1.0000 -0.0615
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
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## 40 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
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## 40 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
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## 3 1.2923 nan 0.0010 0.0002
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## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
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## 10 1.2896 nan 0.0010 0.0002
## 20 1.2859 nan 0.0010 0.0002
## 40 1.2790 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0001
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## 3 1.2922 nan 0.0010 0.0002
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## 5 1.2915 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## 3 1.2919 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
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## 3 1.2919 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
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## 360 1.1461 nan 0.0010 0.0001
## 380 1.1399 nan 0.0010 0.0001
## 400 1.1343 nan 0.0010 0.0001
## 420 1.1281 nan 0.0010 0.0001
## 440 1.1223 nan 0.0010 0.0001
## 460 1.1168 nan 0.0010 0.0001
## 480 1.1112 nan 0.0010 0.0001
## 500 1.1058 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0003
## 2 1.2922 nan 0.0010 0.0003
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0002
## 8 1.2891 nan 0.0010 0.0002
## 9 1.2886 nan 0.0010 0.0002
## 10 1.2880 nan 0.0010 0.0002
## 20 1.2829 nan 0.0010 0.0002
## 40 1.2727 nan 0.0010 0.0002
## 60 1.2626 nan 0.0010 0.0002
## 80 1.2532 nan 0.0010 0.0002
## 100 1.2439 nan 0.0010 0.0002
## 120 1.2353 nan 0.0010 0.0002
## 140 1.2267 nan 0.0010 0.0002
## 160 1.2183 nan 0.0010 0.0002
## 180 1.2101 nan 0.0010 0.0002
## 200 1.2021 nan 0.0010 0.0002
## 220 1.1944 nan 0.0010 0.0002
## 240 1.1869 nan 0.0010 0.0002
## 260 1.1796 nan 0.0010 0.0001
## 280 1.1725 nan 0.0010 0.0001
## 300 1.1657 nan 0.0010 0.0001
## 320 1.1590 nan 0.0010 0.0001
## 340 1.1525 nan 0.0010 0.0001
## 360 1.1463 nan 0.0010 0.0001
## 380 1.1402 nan 0.0010 0.0001
## 400 1.1342 nan 0.0010 0.0001
## 420 1.1284 nan 0.0010 0.0001
## 440 1.1227 nan 0.0010 0.0001
## 460 1.1172 nan 0.0010 0.0001
## 480 1.1119 nan 0.0010 0.0001
## 500 1.1065 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2590 nan 0.1000 0.0167
## 2 1.2300 nan 0.1000 0.0115
## 3 1.2042 nan 0.1000 0.0116
## 4 1.1814 nan 0.1000 0.0090
## 5 1.1644 nan 0.1000 0.0065
## 6 1.1487 nan 0.1000 0.0074
## 7 1.1336 nan 0.1000 0.0066
## 8 1.1177 nan 0.1000 0.0063
## 9 1.1047 nan 0.1000 0.0042
## 10 1.0932 nan 0.1000 0.0042
## 20 1.0085 nan 0.1000 0.0005
## 40 0.9299 nan 0.1000 -0.0004
## 60 0.8884 nan 0.1000 0.0000
## 80 0.8588 nan 0.1000 0.0002
## 100 0.8413 nan 0.1000 -0.0008
## 120 0.8261 nan 0.1000 -0.0003
## 140 0.8170 nan 0.1000 -0.0014
## 160 0.8084 nan 0.1000 -0.0001
## 180 0.7998 nan 0.1000 0.0000
## 200 0.7922 nan 0.1000 -0.0016
## 220 0.7848 nan 0.1000 -0.0006
## 240 0.7771 nan 0.1000 -0.0011
## 260 0.7712 nan 0.1000 -0.0006
## 280 0.7664 nan 0.1000 -0.0007
## 300 0.7576 nan 0.1000 -0.0010
## 320 0.7535 nan 0.1000 -0.0016
## 340 0.7476 nan 0.1000 -0.0001
## 360 0.7405 nan 0.1000 -0.0005
## 380 0.7350 nan 0.1000 -0.0005
## 400 0.7320 nan 0.1000 -0.0007
## 420 0.7268 nan 0.1000 -0.0016
## 440 0.7215 nan 0.1000 -0.0017
## 460 0.7175 nan 0.1000 -0.0014
## 480 0.7120 nan 0.1000 -0.0008
## 500 0.7083 nan 0.1000 -0.0013
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2532 nan 0.1000 0.0159
## 2 1.2215 nan 0.1000 0.0122
## 3 1.1970 nan 0.1000 0.0092
## 4 1.1764 nan 0.1000 0.0063
## 5 1.1577 nan 0.1000 0.0075
## 6 1.1401 nan 0.1000 0.0067
## 7 1.1256 nan 0.1000 0.0060
## 8 1.1112 nan 0.1000 0.0058
## 9 1.0978 nan 0.1000 0.0045
## 10 1.0869 nan 0.1000 0.0048
## 20 1.0072 nan 0.1000 0.0016
## 40 0.9268 nan 0.1000 0.0007
## 60 0.8888 nan 0.1000 -0.0005
## 80 0.8605 nan 0.1000 -0.0004
## 100 0.8464 nan 0.1000 -0.0004
## 120 0.8275 nan 0.1000 -0.0010
## 140 0.8135 nan 0.1000 -0.0010
## 160 0.8022 nan 0.1000 -0.0003
## 180 0.7932 nan 0.1000 -0.0003
## 200 0.7827 nan 0.1000 -0.0007
## 220 0.7765 nan 0.1000 -0.0014
## 240 0.7713 nan 0.1000 -0.0013
## 260 0.7652 nan 0.1000 -0.0008
## 280 0.7590 nan 0.1000 -0.0006
## 300 0.7543 nan 0.1000 -0.0006
## 320 0.7485 nan 0.1000 -0.0011
## 340 0.7447 nan 0.1000 -0.0015
## 360 0.7385 nan 0.1000 -0.0006
## 380 0.7332 nan 0.1000 -0.0025
## 400 0.7283 nan 0.1000 -0.0012
## 420 0.7232 nan 0.1000 -0.0021
## 440 0.7197 nan 0.1000 -0.0006
## 460 0.7157 nan 0.1000 -0.0004
## 480 0.7125 nan 0.1000 -0.0011
## 500 0.7088 nan 0.1000 -0.0008
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2601 nan 0.1000 0.0148
## 2 1.2276 nan 0.1000 0.0132
## 3 1.2058 nan 0.1000 0.0088
## 4 1.1849 nan 0.1000 0.0097
## 5 1.1677 nan 0.1000 0.0074
## 6 1.1500 nan 0.1000 0.0071
## 7 1.1351 nan 0.1000 0.0065
## 8 1.1224 nan 0.1000 0.0055
## 9 1.1091 nan 0.1000 0.0058
## 10 1.0978 nan 0.1000 0.0036
## 20 1.0127 nan 0.1000 0.0027
## 40 0.9256 nan 0.1000 0.0004
## 60 0.8899 nan 0.1000 -0.0002
## 80 0.8637 nan 0.1000 -0.0010
## 100 0.8428 nan 0.1000 0.0003
## 120 0.8278 nan 0.1000 -0.0017
## 140 0.8137 nan 0.1000 -0.0009
## 160 0.8047 nan 0.1000 -0.0007
## 180 0.7955 nan 0.1000 -0.0015
## 200 0.7870 nan 0.1000 -0.0008
## 220 0.7809 nan 0.1000 -0.0011
## 240 0.7736 nan 0.1000 -0.0013
## 260 0.7678 nan 0.1000 -0.0013
## 280 0.7612 nan 0.1000 -0.0007
## 300 0.7528 nan 0.1000 -0.0011
## 320 0.7479 nan 0.1000 -0.0015
## 340 0.7435 nan 0.1000 -0.0018
## 360 0.7382 nan 0.1000 -0.0010
## 380 0.7362 nan 0.1000 -0.0007
## 400 0.7327 nan 0.1000 -0.0003
## 420 0.7269 nan 0.1000 -0.0006
## 440 0.7234 nan 0.1000 -0.0006
## 460 0.7187 nan 0.1000 -0.0011
## 480 0.7136 nan 0.1000 -0.0010
## 500 0.7097 nan 0.1000 -0.0006
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2553 nan 0.1000 0.0184
## 2 1.2156 nan 0.1000 0.0136
## 3 1.1853 nan 0.1000 0.0171
## 4 1.1560 nan 0.1000 0.0104
## 5 1.1306 nan 0.1000 0.0091
## 6 1.1075 nan 0.1000 0.0109
## 7 1.0858 nan 0.1000 0.0068
## 8 1.0716 nan 0.1000 0.0068
## 9 1.0570 nan 0.1000 0.0057
## 10 1.0430 nan 0.1000 0.0055
## 20 0.9404 nan 0.1000 0.0014
## 40 0.8547 nan 0.1000 -0.0005
## 60 0.8026 nan 0.1000 -0.0024
## 80 0.7711 nan 0.1000 -0.0004
## 100 0.7472 nan 0.1000 -0.0011
## 120 0.7214 nan 0.1000 -0.0011
## 140 0.6947 nan 0.1000 -0.0018
## 160 0.6712 nan 0.1000 -0.0023
## 180 0.6535 nan 0.1000 -0.0016
## 200 0.6351 nan 0.1000 -0.0022
## 220 0.6160 nan 0.1000 0.0002
## 240 0.6032 nan 0.1000 -0.0008
## 260 0.5872 nan 0.1000 -0.0014
## 280 0.5735 nan 0.1000 -0.0010
## 300 0.5544 nan 0.1000 -0.0022
## 320 0.5402 nan 0.1000 -0.0006
## 340 0.5282 nan 0.1000 -0.0006
## 360 0.5170 nan 0.1000 -0.0023
## 380 0.5055 nan 0.1000 -0.0017
## 400 0.4899 nan 0.1000 -0.0010
## 420 0.4792 nan 0.1000 0.0000
## 440 0.4674 nan 0.1000 -0.0014
## 460 0.4593 nan 0.1000 -0.0012
## 480 0.4514 nan 0.1000 -0.0014
## 500 0.4416 nan 0.1000 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2498 nan 0.1000 0.0176
## 2 1.2140 nan 0.1000 0.0165
## 3 1.1814 nan 0.1000 0.0145
## 4 1.1555 nan 0.1000 0.0109
## 5 1.1271 nan 0.1000 0.0128
## 6 1.1031 nan 0.1000 0.0094
## 7 1.0840 nan 0.1000 0.0089
## 8 1.0674 nan 0.1000 0.0046
## 9 1.0499 nan 0.1000 0.0064
## 10 1.0339 nan 0.1000 0.0052
## 20 0.9391 nan 0.1000 0.0019
## 40 0.8615 nan 0.1000 -0.0001
## 60 0.8163 nan 0.1000 0.0012
## 80 0.7763 nan 0.1000 -0.0008
## 100 0.7526 nan 0.1000 -0.0016
## 120 0.7291 nan 0.1000 -0.0018
## 140 0.7067 nan 0.1000 -0.0015
## 160 0.6897 nan 0.1000 -0.0003
## 180 0.6715 nan 0.1000 -0.0008
## 200 0.6533 nan 0.1000 -0.0007
## 220 0.6344 nan 0.1000 -0.0016
## 240 0.6184 nan 0.1000 -0.0003
## 260 0.6035 nan 0.1000 -0.0007
## 280 0.5902 nan 0.1000 -0.0020
## 300 0.5727 nan 0.1000 -0.0015
## 320 0.5606 nan 0.1000 -0.0013
## 340 0.5486 nan 0.1000 -0.0010
## 360 0.5370 nan 0.1000 -0.0008
## 380 0.5232 nan 0.1000 -0.0018
## 400 0.5084 nan 0.1000 -0.0008
## 420 0.4956 nan 0.1000 -0.0011
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## 460 0.4705 nan 0.1000 -0.0005
## 480 0.4604 nan 0.1000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2518 nan 0.1000 0.0189
## 2 1.2153 nan 0.1000 0.0180
## 3 1.1829 nan 0.1000 0.0124
## 4 1.1523 nan 0.1000 0.0144
## 5 1.1271 nan 0.1000 0.0107
## 6 1.1056 nan 0.1000 0.0090
## 7 1.0866 nan 0.1000 0.0065
## 8 1.0681 nan 0.1000 0.0059
## 9 1.0478 nan 0.1000 0.0060
## 10 1.0339 nan 0.1000 0.0042
## 20 0.9384 nan 0.1000 0.0002
## 40 0.8516 nan 0.1000 0.0003
## 60 0.8030 nan 0.1000 -0.0009
## 80 0.7725 nan 0.1000 -0.0011
## 100 0.7453 nan 0.1000 -0.0017
## 120 0.7252 nan 0.1000 -0.0033
## 140 0.6987 nan 0.1000 -0.0005
## 160 0.6792 nan 0.1000 -0.0012
## 180 0.6613 nan 0.1000 -0.0008
## 200 0.6424 nan 0.1000 0.0002
## 220 0.6284 nan 0.1000 -0.0011
## 240 0.6120 nan 0.1000 -0.0021
## 260 0.5982 nan 0.1000 -0.0020
## 280 0.5871 nan 0.1000 -0.0007
## 300 0.5733 nan 0.1000 -0.0014
## 320 0.5573 nan 0.1000 -0.0012
## 340 0.5441 nan 0.1000 0.0001
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## 380 0.5199 nan 0.1000 -0.0007
## 400 0.5083 nan 0.1000 -0.0010
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## 440 0.4856 nan 0.1000 -0.0012
## 460 0.4750 nan 0.1000 -0.0008
## 480 0.4656 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2407 nan 0.1000 0.0231
## 2 1.1945 nan 0.1000 0.0211
## 3 1.1599 nan 0.1000 0.0133
## 4 1.1306 nan 0.1000 0.0117
## 5 1.1074 nan 0.1000 0.0104
## 6 1.0824 nan 0.1000 0.0095
## 7 1.0616 nan 0.1000 0.0091
## 8 1.0444 nan 0.1000 0.0069
## 9 1.0260 nan 0.1000 0.0067
## 10 1.0126 nan 0.1000 0.0030
## 20 0.9037 nan 0.1000 -0.0004
## 40 0.8011 nan 0.1000 -0.0010
## 60 0.7468 nan 0.1000 0.0000
## 80 0.6969 nan 0.1000 -0.0007
## 100 0.6600 nan 0.1000 -0.0020
## 120 0.6235 nan 0.1000 -0.0007
## 140 0.5926 nan 0.1000 -0.0006
## 160 0.5664 nan 0.1000 -0.0009
## 180 0.5425 nan 0.1000 -0.0009
## 200 0.5188 nan 0.1000 -0.0008
## 220 0.4996 nan 0.1000 -0.0016
## 240 0.4805 nan 0.1000 -0.0020
## 260 0.4569 nan 0.1000 -0.0005
## 280 0.4378 nan 0.1000 -0.0005
## 300 0.4188 nan 0.1000 -0.0007
## 320 0.4008 nan 0.1000 -0.0013
## 340 0.3858 nan 0.1000 -0.0001
## 360 0.3714 nan 0.1000 -0.0012
## 380 0.3564 nan 0.1000 -0.0010
## 400 0.3427 nan 0.1000 -0.0010
## 420 0.3303 nan 0.1000 -0.0013
## 440 0.3208 nan 0.1000 -0.0007
## 460 0.3099 nan 0.1000 -0.0002
## 480 0.2985 nan 0.1000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2487 nan 0.1000 0.0205
## 2 1.2091 nan 0.1000 0.0164
## 3 1.1789 nan 0.1000 0.0104
## 4 1.1438 nan 0.1000 0.0141
## 5 1.1139 nan 0.1000 0.0118
## 6 1.0881 nan 0.1000 0.0077
## 7 1.0614 nan 0.1000 0.0093
## 8 1.0447 nan 0.1000 0.0050
## 9 1.0208 nan 0.1000 0.0102
## 10 1.0013 nan 0.1000 0.0057
## 20 0.9023 nan 0.1000 0.0013
## 40 0.8104 nan 0.1000 -0.0001
## 60 0.7623 nan 0.1000 -0.0032
## 80 0.7052 nan 0.1000 0.0001
## 100 0.6673 nan 0.1000 -0.0011
## 120 0.6335 nan 0.1000 -0.0016
## 140 0.5987 nan 0.1000 -0.0027
## 160 0.5719 nan 0.1000 -0.0017
## 180 0.5444 nan 0.1000 -0.0017
## 200 0.5161 nan 0.1000 -0.0011
## 220 0.4943 nan 0.1000 -0.0009
## 240 0.4736 nan 0.1000 -0.0006
## 260 0.4549 nan 0.1000 -0.0015
## 280 0.4386 nan 0.1000 -0.0010
## 300 0.4228 nan 0.1000 -0.0016
## 320 0.4065 nan 0.1000 -0.0006
## 340 0.3895 nan 0.1000 -0.0008
## 360 0.3764 nan 0.1000 -0.0003
## 380 0.3593 nan 0.1000 -0.0008
## 400 0.3458 nan 0.1000 -0.0013
## 420 0.3324 nan 0.1000 -0.0006
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## 460 0.3053 nan 0.1000 -0.0008
## 480 0.2914 nan 0.1000 -0.0002
## 500 0.2804 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2399 nan 0.1000 0.0238
## 2 1.1907 nan 0.1000 0.0178
## 3 1.1505 nan 0.1000 0.0170
## 4 1.1180 nan 0.1000 0.0151
## 5 1.0900 nan 0.1000 0.0123
## 6 1.0667 nan 0.1000 0.0072
## 7 1.0476 nan 0.1000 0.0078
## 8 1.0270 nan 0.1000 0.0070
## 9 1.0085 nan 0.1000 0.0053
## 10 0.9921 nan 0.1000 0.0040
## 20 0.8985 nan 0.1000 0.0010
## 40 0.8023 nan 0.1000 -0.0006
## 60 0.7365 nan 0.1000 -0.0010
## 80 0.6951 nan 0.1000 -0.0012
## 100 0.6596 nan 0.1000 -0.0028
## 120 0.6225 nan 0.1000 -0.0010
## 140 0.5959 nan 0.1000 -0.0012
## 160 0.5701 nan 0.1000 -0.0007
## 180 0.5437 nan 0.1000 -0.0006
## 200 0.5191 nan 0.1000 -0.0026
## 220 0.4961 nan 0.1000 -0.0003
## 240 0.4758 nan 0.1000 -0.0011
## 260 0.4544 nan 0.1000 -0.0008
## 280 0.4370 nan 0.1000 -0.0014
## 300 0.4203 nan 0.1000 -0.0004
## 320 0.4045 nan 0.1000 -0.0012
## 340 0.3893 nan 0.1000 -0.0011
## 360 0.3713 nan 0.1000 -0.0007
## 380 0.3571 nan 0.1000 -0.0003
## 400 0.3448 nan 0.1000 -0.0013
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## 440 0.3195 nan 0.1000 -0.0004
## 460 0.3066 nan 0.1000 -0.0009
## 480 0.2956 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2316 nan 0.2000 0.0320
## 2 1.1764 nan 0.2000 0.0217
## 3 1.1432 nan 0.2000 0.0146
## 4 1.1174 nan 0.2000 0.0116
## 5 1.0939 nan 0.2000 0.0103
## 6 1.0682 nan 0.2000 0.0060
## 7 1.0482 nan 0.2000 0.0081
## 8 1.0342 nan 0.2000 0.0050
## 9 1.0201 nan 0.2000 0.0062
## 10 1.0068 nan 0.2000 0.0055
## 20 0.9299 nan 0.2000 0.0023
## 40 0.8673 nan 0.2000 -0.0004
## 60 0.8274 nan 0.2000 -0.0005
## 80 0.8119 nan 0.2000 -0.0004
## 100 0.7951 nan 0.2000 -0.0031
## 120 0.7816 nan 0.2000 -0.0019
## 140 0.7661 nan 0.2000 -0.0016
## 160 0.7540 nan 0.2000 -0.0019
## 180 0.7453 nan 0.2000 -0.0014
## 200 0.7361 nan 0.2000 -0.0010
## 220 0.7229 nan 0.2000 -0.0011
## 240 0.7153 nan 0.2000 -0.0021
## 260 0.7083 nan 0.2000 -0.0020
## 280 0.6984 nan 0.2000 -0.0023
## 300 0.6903 nan 0.2000 -0.0022
## 320 0.6868 nan 0.2000 -0.0033
## 340 0.6790 nan 0.2000 -0.0013
## 360 0.6753 nan 0.2000 -0.0028
## 380 0.6701 nan 0.2000 -0.0006
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## 440 0.6553 nan 0.2000 -0.0029
## 460 0.6502 nan 0.2000 -0.0028
## 480 0.6420 nan 0.2000 -0.0031
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2229 nan 0.2000 0.0353
## 2 1.1764 nan 0.2000 0.0193
## 3 1.1463 nan 0.2000 0.0132
## 4 1.1188 nan 0.2000 0.0112
## 5 1.0916 nan 0.2000 0.0126
## 6 1.0706 nan 0.2000 0.0079
## 7 1.0460 nan 0.2000 0.0085
## 8 1.0279 nan 0.2000 0.0062
## 9 1.0146 nan 0.2000 0.0058
## 10 1.0069 nan 0.2000 -0.0025
## 20 0.9254 nan 0.2000 0.0009
## 40 0.8613 nan 0.2000 -0.0015
## 60 0.8320 nan 0.2000 -0.0013
## 80 0.8030 nan 0.2000 -0.0019
## 100 0.7891 nan 0.2000 -0.0024
## 120 0.7767 nan 0.2000 -0.0019
## 140 0.7664 nan 0.2000 -0.0032
## 160 0.7525 nan 0.2000 -0.0023
## 180 0.7424 nan 0.2000 -0.0039
## 200 0.7329 nan 0.2000 -0.0007
## 220 0.7226 nan 0.2000 -0.0005
## 240 0.7149 nan 0.2000 -0.0010
## 260 0.7048 nan 0.2000 -0.0022
## 280 0.6961 nan 0.2000 -0.0022
## 300 0.6919 nan 0.2000 -0.0035
## 320 0.6839 nan 0.2000 -0.0023
## 340 0.6756 nan 0.2000 -0.0015
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## 380 0.6653 nan 0.2000 -0.0021
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## 440 0.6427 nan 0.2000 -0.0014
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2259 nan 0.2000 0.0300
## 2 1.1870 nan 0.2000 0.0224
## 3 1.1522 nan 0.2000 0.0141
## 4 1.1206 nan 0.2000 0.0103
## 5 1.0966 nan 0.2000 0.0090
## 6 1.0739 nan 0.2000 0.0108
## 7 1.0541 nan 0.2000 0.0068
## 8 1.0356 nan 0.2000 0.0069
## 9 1.0179 nan 0.2000 0.0045
## 10 1.0049 nan 0.2000 0.0023
## 20 0.9269 nan 0.2000 -0.0010
## 40 0.8656 nan 0.2000 -0.0006
## 60 0.8291 nan 0.2000 -0.0001
## 80 0.8093 nan 0.2000 -0.0026
## 100 0.7997 nan 0.2000 -0.0033
## 120 0.7842 nan 0.2000 -0.0025
## 140 0.7721 nan 0.2000 -0.0021
## 160 0.7585 nan 0.2000 -0.0020
## 180 0.7469 nan 0.2000 -0.0025
## 200 0.7374 nan 0.2000 -0.0013
## 220 0.7256 nan 0.2000 -0.0016
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## 280 0.7069 nan 0.2000 0.0001
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## 320 0.6951 nan 0.2000 -0.0011
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## 360 0.6816 nan 0.2000 -0.0014
## 380 0.6759 nan 0.2000 -0.0017
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## 460 0.6499 nan 0.2000 -0.0011
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2167 nan 0.2000 0.0344
## 2 1.1613 nan 0.2000 0.0294
## 3 1.1108 nan 0.2000 0.0201
## 4 1.0784 nan 0.2000 0.0114
## 5 1.0550 nan 0.2000 0.0090
## 6 1.0344 nan 0.2000 0.0011
## 7 1.0142 nan 0.2000 0.0085
## 8 0.9963 nan 0.2000 0.0029
## 9 0.9762 nan 0.2000 0.0061
## 10 0.9638 nan 0.2000 -0.0011
## 20 0.8660 nan 0.2000 0.0016
## 40 0.7917 nan 0.2000 -0.0000
## 60 0.7464 nan 0.2000 -0.0034
## 80 0.7008 nan 0.2000 -0.0026
## 100 0.6612 nan 0.2000 -0.0009
## 120 0.6295 nan 0.2000 -0.0039
## 140 0.5985 nan 0.2000 -0.0025
## 160 0.5657 nan 0.2000 -0.0041
## 180 0.5356 nan 0.2000 -0.0015
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## 260 0.4416 nan 0.2000 -0.0010
## 280 0.4221 nan 0.2000 -0.0014
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## 460 0.2911 nan 0.2000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2026 nan 0.2000 0.0391
## 2 1.1475 nan 0.2000 0.0253
## 3 1.0988 nan 0.2000 0.0189
## 4 1.0679 nan 0.2000 0.0123
## 5 1.0315 nan 0.2000 0.0150
## 6 1.0042 nan 0.2000 0.0104
## 7 0.9839 nan 0.2000 0.0054
## 8 0.9685 nan 0.2000 0.0026
## 9 0.9513 nan 0.2000 0.0028
## 10 0.9360 nan 0.2000 0.0043
## 20 0.8570 nan 0.2000 0.0015
## 40 0.7835 nan 0.2000 -0.0005
## 60 0.7464 nan 0.2000 -0.0031
## 80 0.7004 nan 0.2000 -0.0045
## 100 0.6631 nan 0.2000 -0.0017
## 120 0.6261 nan 0.2000 -0.0012
## 140 0.5904 nan 0.2000 -0.0031
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## 180 0.5489 nan 0.2000 -0.0017
## 200 0.5205 nan 0.2000 -0.0007
## 220 0.4933 nan 0.2000 -0.0011
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## 280 0.4229 nan 0.2000 -0.0019
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## 320 0.3847 nan 0.2000 -0.0014
## 340 0.3718 nan 0.2000 -0.0012
## 360 0.3591 nan 0.2000 -0.0021
## 380 0.3435 nan 0.2000 -0.0014
## 400 0.3311 nan 0.2000 -0.0005
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## 440 0.3097 nan 0.2000 -0.0008
## 460 0.2976 nan 0.2000 -0.0021
## 480 0.2895 nan 0.2000 -0.0013
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2080 nan 0.2000 0.0381
## 2 1.1467 nan 0.2000 0.0268
## 3 1.1022 nan 0.2000 0.0152
## 4 1.0598 nan 0.2000 0.0144
## 5 1.0321 nan 0.2000 0.0100
## 6 1.0038 nan 0.2000 0.0105
## 7 0.9852 nan 0.2000 0.0014
## 8 0.9677 nan 0.2000 0.0036
## 9 0.9529 nan 0.2000 0.0025
## 10 0.9379 nan 0.2000 0.0045
## 20 0.8624 nan 0.2000 0.0014
## 40 0.7884 nan 0.2000 -0.0028
## 60 0.7305 nan 0.2000 -0.0016
## 80 0.6906 nan 0.2000 -0.0012
## 100 0.6627 nan 0.2000 -0.0035
## 120 0.6256 nan 0.2000 -0.0041
## 140 0.5998 nan 0.2000 -0.0052
## 160 0.5572 nan 0.2000 -0.0023
## 180 0.5269 nan 0.2000 0.0003
## 200 0.5003 nan 0.2000 -0.0014
## 220 0.4825 nan 0.2000 -0.0016
## 240 0.4632 nan 0.2000 -0.0016
## 260 0.4410 nan 0.2000 -0.0034
## 280 0.4163 nan 0.2000 -0.0009
## 300 0.3999 nan 0.2000 -0.0009
## 320 0.3861 nan 0.2000 -0.0015
## 340 0.3671 nan 0.2000 -0.0007
## 360 0.3547 nan 0.2000 -0.0009
## 380 0.3428 nan 0.2000 -0.0016
## 400 0.3264 nan 0.2000 -0.0014
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## 440 0.3007 nan 0.2000 -0.0009
## 460 0.2903 nan 0.2000 -0.0006
## 480 0.2810 nan 0.2000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1891 nan 0.2000 0.0418
## 2 1.1308 nan 0.2000 0.0224
## 3 1.0811 nan 0.2000 0.0209
## 4 1.0398 nan 0.2000 0.0149
## 5 1.0074 nan 0.2000 0.0047
## 6 0.9826 nan 0.2000 0.0067
## 7 0.9592 nan 0.2000 0.0057
## 8 0.9373 nan 0.2000 0.0057
## 9 0.9202 nan 0.2000 0.0038
## 10 0.9063 nan 0.2000 0.0013
## 20 0.8114 nan 0.2000 -0.0019
## 40 0.7089 nan 0.2000 -0.0015
## 60 0.6327 nan 0.2000 -0.0029
## 80 0.5843 nan 0.2000 -0.0009
## 100 0.5323 nan 0.2000 -0.0025
## 120 0.4818 nan 0.2000 -0.0025
## 140 0.4398 nan 0.2000 -0.0014
## 160 0.4043 nan 0.2000 -0.0010
## 180 0.3761 nan 0.2000 -0.0018
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## 220 0.3190 nan 0.2000 -0.0017
## 240 0.2974 nan 0.2000 -0.0010
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## 280 0.2563 nan 0.2000 -0.0006
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## 320 0.2265 nan 0.2000 -0.0017
## 340 0.2124 nan 0.2000 -0.0009
## 360 0.1982 nan 0.2000 -0.0010
## 380 0.1842 nan 0.2000 0.0000
## 400 0.1749 nan 0.2000 -0.0013
## 420 0.1638 nan 0.2000 -0.0004
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## 460 0.1488 nan 0.2000 -0.0008
## 480 0.1397 nan 0.2000 -0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2050 nan 0.2000 0.0390
## 2 1.1255 nan 0.2000 0.0329
## 3 1.0773 nan 0.2000 0.0172
## 4 1.0318 nan 0.2000 0.0175
## 5 0.9958 nan 0.2000 0.0130
## 6 0.9693 nan 0.2000 0.0088
## 7 0.9441 nan 0.2000 0.0031
## 8 0.9285 nan 0.2000 0.0067
## 9 0.9137 nan 0.2000 0.0025
## 10 0.8978 nan 0.2000 0.0013
## 20 0.8067 nan 0.2000 -0.0012
## 40 0.7004 nan 0.2000 -0.0029
## 60 0.6217 nan 0.2000 -0.0026
## 80 0.5618 nan 0.2000 -0.0030
## 100 0.5248 nan 0.2000 -0.0042
## 120 0.4750 nan 0.2000 -0.0046
## 140 0.4403 nan 0.2000 -0.0009
## 160 0.4031 nan 0.2000 -0.0012
## 180 0.3682 nan 0.2000 -0.0007
## 200 0.3425 nan 0.2000 -0.0017
## 220 0.3184 nan 0.2000 -0.0014
## 240 0.2959 nan 0.2000 -0.0014
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## 280 0.2572 nan 0.2000 -0.0007
## 300 0.2411 nan 0.2000 -0.0028
## 320 0.2215 nan 0.2000 -0.0005
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## 360 0.1956 nan 0.2000 -0.0013
## 380 0.1829 nan 0.2000 -0.0012
## 400 0.1710 nan 0.2000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1818 nan 0.2000 0.0475
## 2 1.1079 nan 0.2000 0.0321
## 3 1.0626 nan 0.2000 0.0206
## 4 1.0321 nan 0.2000 0.0093
## 5 0.9942 nan 0.2000 0.0159
## 6 0.9664 nan 0.2000 0.0053
## 7 0.9471 nan 0.2000 0.0040
## 8 0.9371 nan 0.2000 -0.0020
## 9 0.9174 nan 0.2000 0.0034
## 10 0.9085 nan 0.2000 -0.0062
## 20 0.8075 nan 0.2000 -0.0036
## 40 0.7012 nan 0.2000 -0.0036
## 60 0.6445 nan 0.2000 -0.0060
## 80 0.5794 nan 0.2000 -0.0022
## 100 0.5320 nan 0.2000 -0.0014
## 120 0.4886 nan 0.2000 -0.0030
## 140 0.4435 nan 0.2000 -0.0008
## 160 0.4099 nan 0.2000 -0.0028
## 180 0.3755 nan 0.2000 -0.0023
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## 220 0.3187 nan 0.2000 -0.0023
## 240 0.2937 nan 0.2000 -0.0008
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## 280 0.2525 nan 0.2000 -0.0022
## 300 0.2345 nan 0.2000 -0.0015
## 320 0.2177 nan 0.2000 -0.0007
## 340 0.2049 nan 0.2000 -0.0006
## 360 0.1924 nan 0.2000 -0.0007
## 380 0.1812 nan 0.2000 -0.0007
## 400 0.1693 nan 0.2000 -0.0011
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## 460 0.1393 nan 0.2000 -0.0007
## 480 0.1296 nan 0.2000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2063 nan 0.3000 0.0450
## 2 1.1461 nan 0.3000 0.0245
## 3 1.1171 nan 0.3000 0.0101
## 4 1.0833 nan 0.3000 0.0142
## 5 1.0503 nan 0.3000 0.0134
## 6 1.0306 nan 0.3000 0.0053
## 7 1.0137 nan 0.3000 0.0063
## 8 0.9974 nan 0.3000 0.0046
## 9 0.9853 nan 0.3000 0.0010
## 10 0.9693 nan 0.3000 0.0027
## 20 0.9064 nan 0.3000 -0.0019
## 40 0.8477 nan 0.3000 -0.0030
## 60 0.8143 nan 0.3000 -0.0016
## 80 0.7931 nan 0.3000 -0.0041
## 100 0.7717 nan 0.3000 -0.0034
## 120 0.7584 nan 0.3000 -0.0024
## 140 0.7444 nan 0.3000 -0.0035
## 160 0.7269 nan 0.3000 -0.0006
## 180 0.7184 nan 0.3000 -0.0010
## 200 0.7037 nan 0.3000 -0.0019
## 220 0.6907 nan 0.3000 -0.0024
## 240 0.6826 nan 0.3000 -0.0023
## 260 0.6728 nan 0.3000 -0.0071
## 280 0.6572 nan 0.3000 -0.0001
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## 320 0.6398 nan 0.3000 -0.0035
## 340 0.6314 nan 0.3000 -0.0029
## 360 0.6251 nan 0.3000 -0.0036
## 380 0.6211 nan 0.3000 -0.0032
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## 460 0.5996 nan 0.3000 -0.0033
## 480 0.5965 nan 0.3000 -0.0054
## 500 0.5901 nan 0.3000 -0.0021
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2010 nan 0.3000 0.0381
## 2 1.1415 nan 0.3000 0.0187
## 3 1.1026 nan 0.3000 0.0135
## 4 1.0664 nan 0.3000 0.0144
## 5 1.0415 nan 0.3000 0.0123
## 6 1.0220 nan 0.3000 0.0014
## 7 0.9999 nan 0.3000 0.0072
## 8 0.9804 nan 0.3000 0.0035
## 9 0.9658 nan 0.3000 0.0044
## 10 0.9564 nan 0.3000 0.0002
## 20 0.8872 nan 0.3000 -0.0025
## 40 0.8412 nan 0.3000 -0.0006
## 60 0.8092 nan 0.3000 -0.0082
## 80 0.7864 nan 0.3000 -0.0033
## 100 0.7726 nan 0.3000 -0.0041
## 120 0.7559 nan 0.3000 -0.0023
## 140 0.7448 nan 0.3000 -0.0037
## 160 0.7316 nan 0.3000 -0.0041
## 180 0.7160 nan 0.3000 -0.0053
## 200 0.7028 nan 0.3000 -0.0037
## 220 0.6885 nan 0.3000 -0.0015
## 240 0.6826 nan 0.3000 -0.0025
## 260 0.6696 nan 0.3000 -0.0034
## 280 0.6592 nan 0.3000 -0.0013
## 300 0.6488 nan 0.3000 -0.0038
## 320 0.6397 nan 0.3000 -0.0018
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## 380 0.6172 nan 0.3000 -0.0041
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## 440 0.5930 nan 0.3000 -0.0039
## 460 0.5905 nan 0.3000 -0.0034
## 480 0.5880 nan 0.3000 -0.0041
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2031 nan 0.3000 0.0412
## 2 1.1475 nan 0.3000 0.0198
## 3 1.1100 nan 0.3000 0.0151
## 4 1.0760 nan 0.3000 0.0073
## 5 1.0401 nan 0.3000 0.0137
## 6 1.0218 nan 0.3000 0.0042
## 7 0.9974 nan 0.3000 0.0078
## 8 0.9826 nan 0.3000 0.0061
## 9 0.9694 nan 0.3000 0.0041
## 10 0.9607 nan 0.3000 0.0011
## 20 0.8827 nan 0.3000 -0.0008
## 40 0.8395 nan 0.3000 -0.0016
## 60 0.8009 nan 0.3000 -0.0010
## 80 0.7783 nan 0.3000 -0.0036
## 100 0.7566 nan 0.3000 -0.0025
## 120 0.7454 nan 0.3000 -0.0049
## 140 0.7319 nan 0.3000 -0.0023
## 160 0.7155 nan 0.3000 -0.0024
## 180 0.7030 nan 0.3000 -0.0027
## 200 0.6887 nan 0.3000 -0.0015
## 220 0.6756 nan 0.3000 -0.0022
## 240 0.6605 nan 0.3000 -0.0015
## 260 0.6478 nan 0.3000 -0.0006
## 280 0.6392 nan 0.3000 -0.0019
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## 320 0.6222 nan 0.3000 -0.0008
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## 360 0.6060 nan 0.3000 -0.0040
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## 480 0.5677 nan 0.3000 -0.0028
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1808 nan 0.3000 0.0589
## 2 1.1047 nan 0.3000 0.0312
## 3 1.0520 nan 0.3000 0.0183
## 4 1.0048 nan 0.3000 0.0204
## 5 0.9769 nan 0.3000 0.0070
## 6 0.9541 nan 0.3000 0.0036
## 7 0.9355 nan 0.3000 0.0052
## 8 0.9251 nan 0.3000 -0.0087
## 9 0.9080 nan 0.3000 0.0004
## 10 0.9052 nan 0.3000 -0.0087
## 20 0.8225 nan 0.3000 -0.0012
## 40 0.7202 nan 0.3000 -0.0062
## 60 0.6711 nan 0.3000 -0.0056
## 80 0.6206 nan 0.3000 -0.0069
## 100 0.5753 nan 0.3000 -0.0011
## 120 0.5347 nan 0.3000 -0.0003
## 140 0.4962 nan 0.3000 -0.0022
## 160 0.4569 nan 0.3000 -0.0020
## 180 0.4282 nan 0.3000 -0.0054
## 200 0.4033 nan 0.3000 -0.0022
## 220 0.3829 nan 0.3000 -0.0020
## 240 0.3684 nan 0.3000 -0.0021
## 260 0.3542 nan 0.3000 -0.0038
## 280 0.3307 nan 0.3000 -0.0023
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## 320 0.2989 nan 0.3000 -0.0091
## 340 0.2785 nan 0.3000 -0.0011
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## 380 0.2497 nan 0.3000 -0.0025
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## 460 0.1936 nan 0.3000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1639 nan 0.3000 0.0580
## 2 1.0921 nan 0.3000 0.0354
## 3 1.0474 nan 0.3000 0.0149
## 4 0.9972 nan 0.3000 0.0190
## 5 0.9733 nan 0.3000 -0.0008
## 6 0.9440 nan 0.3000 0.0070
## 7 0.9323 nan 0.3000 -0.0006
## 8 0.9135 nan 0.3000 0.0042
## 9 0.8980 nan 0.3000 0.0037
## 10 0.8845 nan 0.3000 0.0022
## 20 0.8140 nan 0.3000 -0.0076
## 40 0.7324 nan 0.3000 -0.0002
## 60 0.6726 nan 0.3000 -0.0035
## 80 0.6498 nan 0.3000 -0.0527
## 100 0.5510 nan 0.3000 -0.0041
## 120 0.5132 nan 0.3000 -0.0013
## 140 0.4798 nan 0.3000 -0.0023
## 160 0.4480 nan 0.3000 -0.0024
## 180 0.4174 nan 0.3000 -0.0018
## 200 0.3905 nan 0.3000 -0.0024
## 220 0.3591 nan 0.3000 -0.0032
## 240 0.3392 nan 0.3000 -0.0037
## 260 0.3241 nan 0.3000 -0.0006
## 280 0.3061 nan 0.3000 -0.0040
## 300 0.2922 nan 0.3000 -0.0017
## 320 0.2754 nan 0.3000 -0.0012
## 340 0.2574 nan 0.3000 -0.0028
## 360 0.2413 nan 0.3000 -0.0010
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## 460 0.1830 nan 0.3000 -0.0005
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1686 nan 0.3000 0.0514
## 2 1.1001 nan 0.3000 0.0317
## 3 1.0493 nan 0.3000 0.0212
## 4 1.0197 nan 0.3000 0.0053
## 5 0.9896 nan 0.3000 0.0062
## 6 0.9630 nan 0.3000 0.0097
## 7 0.9463 nan 0.3000 0.0048
## 8 0.9331 nan 0.3000 -0.0050
## 9 0.9192 nan 0.3000 -0.0003
## 10 0.9038 nan 0.3000 0.0020
## 20 0.8344 nan 0.3000 -0.0013
## 40 0.7323 nan 0.3000 -0.0065
## 60 0.6700 nan 0.3000 -0.0045
## 80 0.6233 nan 0.3000 -0.0038
## 100 0.5752 nan 0.3000 -0.0006
## 120 0.5197 nan 0.3000 -0.0014
## 140 0.4869 nan 0.3000 -0.0041
## 160 0.4556 nan 0.3000 -0.0046
## 180 0.4234 nan 0.3000 -0.0047
## 200 0.4022 nan 0.3000 -0.0029
## 220 0.3868 nan 0.3000 -0.0037
## 240 0.3597 nan 0.3000 -0.0042
## 260 0.3381 nan 0.3000 -0.0016
## 280 0.3132 nan 0.3000 -0.0021
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## 340 0.2651 nan 0.3000 -0.0032
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1731 nan 0.3000 0.0565
## 2 1.0974 nan 0.3000 0.0270
## 3 1.0169 nan 0.3000 0.0370
## 4 0.9890 nan 0.3000 0.0055
## 5 0.9581 nan 0.3000 0.0019
## 6 0.9266 nan 0.3000 0.0120
## 7 0.9087 nan 0.3000 -0.0028
## 8 0.8921 nan 0.3000 -0.0024
## 9 0.8778 nan 0.3000 0.0010
## 10 0.8683 nan 0.3000 -0.0069
## 20 0.7720 nan 0.3000 -0.0053
## 40 0.6442 nan 0.3000 -0.0022
## 60 0.5469 nan 0.3000 -0.0024
## 80 inf nan 0.3000 nan
## 100 inf nan 0.3000 nan
## 120 inf nan 0.3000 nan
## 140 inf nan 0.3000 nan
## 160 inf nan 0.3000 nan
## 180 inf nan 0.3000 nan
## 200 inf nan 0.3000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1664 nan 0.3000 0.0543
## 2 1.0712 nan 0.3000 0.0388
## 3 1.0175 nan 0.3000 0.0216
## 4 0.9846 nan 0.3000 0.0051
## 5 0.9583 nan 0.3000 0.0003
## 6 0.9312 nan 0.3000 0.0079
## 7 0.9129 nan 0.3000 -0.0007
## 8 0.8940 nan 0.3000 -0.0024
## 9 0.8792 nan 0.3000 0.0011
## 10 0.8651 nan 0.3000 -0.0044
## 20 0.7710 nan 0.3000 -0.0099
## 40 0.6613 nan 0.3000 -0.0085
## 60 0.5938 nan 0.3000 -0.0058
## 80 0.5135 nan 0.3000 -0.0036
## 100 0.4488 nan 0.3000 -0.0038
## 120 0.3954 nan 0.3000 -0.0046
## 140 0.3439 nan 0.3000 -0.0008
## 160 0.3064 nan 0.3000 -0.0036
## 180 0.2721 nan 0.3000 -0.0014
## 200 0.2480 nan 0.3000 -0.0013
## 220 0.2199 nan 0.3000 -0.0007
## 240 0.2000 nan 0.3000 -0.0009
## 260 0.1814 nan 0.3000 -0.0017
## 280 0.1664 nan 0.3000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1644 nan 0.3000 0.0599
## 2 1.0754 nan 0.3000 0.0393
## 3 1.0250 nan 0.3000 0.0158
## 4 0.9852 nan 0.3000 0.0094
## 5 0.9557 nan 0.3000 0.0063
## 6 0.9267 nan 0.3000 0.0023
## 7 0.9028 nan 0.3000 0.0044
## 8 0.8873 nan 0.3000 -0.0013
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## 10 0.8695 nan 0.3000 -0.0042
## 20 0.7786 nan 0.3000 -0.0058
## 40 0.6823 nan 0.3000 -0.0009
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## 180 0.2865 nan 0.3000 -0.0014
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## 280 0.1750 nan 0.3000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1693 nan 0.5000 0.0504
## 2 1.1053 nan 0.5000 0.0283
## 3 1.0604 nan 0.5000 0.0177
## 4 1.0144 nan 0.5000 0.0140
## 5 0.9877 nan 0.5000 -0.0027
## 6 0.9641 nan 0.5000 0.0050
## 7 0.9533 nan 0.5000 -0.0009
## 8 0.9379 nan 0.5000 0.0067
## 9 0.9355 nan 0.5000 -0.0055
## 10 0.9269 nan 0.5000 0.0021
## 20 0.8707 nan 0.5000 -0.0046
## 40 0.8156 nan 0.5000 -0.0155
## 60 0.7671 nan 0.5000 -0.0099
## 80 0.7424 nan 0.5000 -0.0012
## 100 0.7199 nan 0.5000 -0.0031
## 120 1.1378 nan 0.5000 -0.0026
## 140 1.1256 nan 0.5000 -0.0007
## 160 1.1151 nan 0.5000 0.0000
## 180 1.1090 nan 0.5000 -0.0001
## 200 1.1015 nan 0.5000 0.0001
## 220 1.0973 nan 0.5000 -0.0014
## 240 1.0882 nan 0.5000 -0.0042
## 260 1.0799 nan 0.5000 -0.0006
## 280 1.0744 nan 0.5000 -0.0001
## 300 1.0685 nan 0.5000 -0.0000
## 320 1.0657 nan 0.5000 -0.0000
## 340 1.0583 nan 0.5000 0.0014
## 360 1.0478 nan 0.5000 -0.0011
## 380 1.0425 nan 0.5000 -0.0047
## 400 1.0453 nan 0.5000 0.0001
## 420 1.0388 nan 0.5000 -0.0040
## 440 1.0294 nan 0.5000 0.0001
## 460 1.0203 nan 0.5000 -0.0005
## 480 1.0231 nan 0.5000 0.0000
## 500 1.0195 nan 0.5000 -0.0046
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1610 nan 0.5000 0.0722
## 2 1.1090 nan 0.5000 0.0228
## 3 1.0572 nan 0.5000 0.0192
## 4 1.0093 nan 0.5000 0.0124
## 5 0.9755 nan 0.5000 0.0110
## 6 0.9681 nan 0.5000 -0.0044
## 7 0.9548 nan 0.5000 0.0010
## 8 0.9418 nan 0.5000 0.0007
## 9 0.9343 nan 0.5000 -0.0045
## 10 0.9263 nan 0.5000 -0.0009
## 20 0.8777 nan 0.5000 -0.0015
## 40 0.8057 nan 0.5000 -0.0024
## 60 0.7792 nan 0.5000 -0.0092
## 80 0.7743 nan 0.5000 -0.0093
## 100 0.7471 nan 0.5000 -0.0042
## 120 0.7252 nan 0.5000 -0.0086
## 140 0.7077 nan 0.5000 -0.0061
## 160 0.6982 nan 0.5000 -0.0017
## 180 0.6729 nan 0.5000 -0.0003
## 200 0.6513 nan 0.5000 0.0004
## 220 0.6273 nan 0.5000 -0.0101
## 240 0.6103 nan 0.5000 -0.0053
## 260 0.6062 nan 0.5000 -0.0066
## 280 0.6035 nan 0.5000 -0.0128
## 300 0.5857 nan 0.5000 -0.0023
## 320 0.5774 nan 0.5000 -0.0067
## 340 0.5655 nan 0.5000 -0.0072
## 360 0.5630 nan 0.5000 -0.0061
## 380 0.5569 nan 0.5000 -0.0047
## 400 0.5604 nan 0.5000 -0.0062
## 420 0.5482 nan 0.5000 -0.0040
## 440 0.5278 nan 0.5000 -0.0026
## 460 0.5212 nan 0.5000 -0.0067
## 480 0.5158 nan 0.5000 -0.0012
## 500 0.5086 nan 0.5000 -0.0028
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1656 nan 0.5000 0.0703
## 2 1.1074 nan 0.5000 0.0222
## 3 1.0508 nan 0.5000 0.0190
## 4 1.0375 nan 0.5000 -0.0020
## 5 1.0005 nan 0.5000 0.0090
## 6 0.9642 nan 0.5000 0.0182
## 7 0.9418 nan 0.5000 0.0058
## 8 0.9327 nan 0.5000 -0.0007
## 9 0.9272 nan 0.5000 -0.0049
## 10 0.9158 nan 0.5000 0.0006
## 20 0.8471 nan 0.5000 0.0022
## 40 0.8009 nan 0.5000 0.0015
## 60 0.7717 nan 0.5000 -0.0059
## 80 0.7658 nan 0.5000 -0.0017
## 100 0.7369 nan 0.5000 0.0021
## 120 0.7331 nan 0.5000 -0.0099
## 140 0.6976 nan 0.5000 -0.0024
## 160 0.6880 nan 0.5000 -0.0085
## 180 0.6733 nan 0.5000 -0.0050
## 200 0.6671 nan 0.5000 -0.0072
## 220 0.6496 nan 0.5000 -0.0050
## 240 0.6362 nan 0.5000 -0.0027
## 260 0.6216 nan 0.5000 -0.0048
## 280 0.6206 nan 0.5000 -0.0119
## 300 0.6081 nan 0.5000 -0.0070
## 320 0.5933 nan 0.5000 -0.0049
## 340 0.5891 nan 0.5000 -0.0020
## 360 0.5776 nan 0.5000 -0.0043
## 380 0.5714 nan 0.5000 -0.0050
## 400 0.5742 nan 0.5000 -0.0064
## 420 0.5608 nan 0.5000 -0.0024
## 440 0.5584 nan 0.5000 -0.0087
## 460 0.5537 nan 0.5000 -0.0056
## 480 0.5450 nan 0.5000 -0.0017
## 500 0.5366 nan 0.5000 -0.0063
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1302 nan 0.5000 0.0795
## 2 1.0581 nan 0.5000 0.0186
## 3 0.9991 nan 0.5000 0.0173
## 4 0.9443 nan 0.5000 0.0132
## 5 0.9236 nan 0.5000 0.0007
## 6 0.9018 nan 0.5000 -0.0051
## 7 0.8757 nan 0.5000 0.0058
## 8 0.8607 nan 0.5000 -0.0008
## 9 0.8495 nan 0.5000 -0.0070
## 10 0.8456 nan 0.5000 -0.0122
## 20 0.7878 nan 0.5000 -0.0017
## 40 inf nan 0.5000 nan
## 60 inf nan 0.5000 nan
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1340 nan 0.5000 0.0667
## 2 1.0541 nan 0.5000 0.0338
## 3 0.9878 nan 0.5000 0.0267
## 4 0.9471 nan 0.5000 0.0068
## 5 0.9260 nan 0.5000 -0.0013
## 6 0.9086 nan 0.5000 -0.0040
## 7 0.8981 nan 0.5000 -0.0053
## 8 0.8732 nan 0.5000 0.0085
## 9 0.8515 nan 0.5000 0.0076
## 10 0.8366 nan 0.5000 -0.0017
## 20 0.7779 nan 0.5000 -0.0090
## 40 0.6720 nan 0.5000 -0.0011
## 60 0.5910 nan 0.5000 -0.0093
## 80 0.5403 nan 0.5000 -0.0092
## 100 0.4812 nan 0.5000 -0.0023
## 120 0.4276 nan 0.5000 -0.0101
## 140 0.3778 nan 0.5000 -0.0015
## 160 0.3481 nan 0.5000 -0.0022
## 180 0.3279 nan 0.5000 -0.0100
## 200 0.2960 nan 0.5000 -0.0057
## 220 0.2792 nan 0.5000 -0.0001
## 240 0.2488 nan 0.5000 -0.0028
## 260 0.2267 nan 0.5000 -0.0044
## 280 0.2123 nan 0.5000 -0.0043
## 300 0.1985 nan 0.5000 -0.0027
## 320 0.1808 nan 0.5000 -0.0054
## 340 0.1670 nan 0.5000 -0.0037
## 360 0.1509 nan 0.5000 -0.0023
## 380 0.1417 nan 0.5000 -0.0032
## 400 0.1305 nan 0.5000 -0.0009
## 420 0.1195 nan 0.5000 -0.0013
## 440 0.1095 nan 0.5000 -0.0025
## 460 0.1013 nan 0.5000 -0.0004
## 480 0.0938 nan 0.5000 -0.0005
## 500 0.0880 nan 0.5000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1148 nan 0.5000 0.0853
## 2 1.0245 nan 0.5000 0.0414
## 3 0.9778 nan 0.5000 0.0134
## 4 0.9404 nan 0.5000 0.0058
## 5 0.9185 nan 0.5000 -0.0005
## 6 0.8973 nan 0.5000 0.0051
## 7 0.8760 nan 0.5000 0.0046
## 8 0.8628 nan 0.5000 -0.0102
## 9 0.8457 nan 0.5000 -0.0044
## 10 0.8323 nan 0.5000 -0.0080
## 20 0.7757 nan 0.5000 -0.0020
## 40 0.6974 nan 0.5000 -0.0102
## 60 0.6214 nan 0.5000 0.0004
## 80 0.5477 nan 0.5000 -0.0102
## 100 0.4994 nan 0.5000 -0.0123
## 120 0.4375 nan 0.5000 -0.0042
## 140 0.3946 nan 0.5000 -0.0055
## 160 0.3677 nan 0.5000 -0.0038
## 180 0.3334 nan 0.5000 -0.0109
## 200 0.2993 nan 0.5000 -0.0075
## 220 0.2646 nan 0.5000 -0.0051
## 240 0.2366 nan 0.5000 -0.0049
## 260 0.2218 nan 0.5000 -0.0048
## 280 0.2011 nan 0.5000 -0.0012
## 300 0.1862 nan 0.5000 -0.0039
## 320 0.1717 nan 0.5000 -0.0033
## 340 0.1557 nan 0.5000 -0.0009
## 360 0.1456 nan 0.5000 -0.0019
## 380 0.1319 nan 0.5000 -0.0004
## 400 0.1240 nan 0.5000 -0.0022
## 420 0.1137 nan 0.5000 -0.0024
## 440 0.1038 nan 0.5000 0.0001
## 460 0.0956 nan 0.5000 -0.0012
## 480 0.0888 nan 0.5000 -0.0019
## 500 0.0833 nan 0.5000 -0.0017
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0994 nan 0.5000 0.0732
## 2 0.9947 nan 0.5000 0.0357
## 3 0.9356 nan 0.5000 0.0203
## 4 0.9074 nan 0.5000 0.0019
## 5 0.8821 nan 0.5000 -0.0022
## 6 0.8625 nan 0.5000 -0.0042
## 7 0.8429 nan 0.5000 -0.0004
## 8 0.8355 nan 0.5000 -0.0102
## 9 0.8220 nan 0.5000 -0.0113
## 10 0.8229 nan 0.5000 -0.0197
## 20 0.7154 nan 0.5000 -0.0051
## 40 0.5707 nan 0.5000 -0.0015
## 60 0.4747 nan 0.5000 -0.0093
## 80 0.3874 nan 0.5000 0.0012
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
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## 460 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1009 nan 0.5000 0.1006
## 2 1.0358 nan 0.5000 0.0189
## 3 0.9757 nan 0.5000 0.0077
## 4 0.9417 nan 0.5000 -0.0103
## 5 0.8968 nan 0.5000 0.0075
## 6 0.8765 nan 0.5000 -0.0014
## 7 0.8691 nan 0.5000 -0.0198
## 8 0.8427 nan 0.5000 -0.0065
## 9 0.8081 nan 0.5000 0.0098
## 10 0.7999 nan 0.5000 -0.0085
## 20 0.7236 nan 0.5000 -0.0057
## 40 0.5600 nan 0.5000 -0.0075
## 60 0.4944 nan 0.5000 -0.0086
## 80 0.4134 nan 0.5000 -0.0102
## 100 0.3301 nan 0.5000 -0.0155
## 120 1303502062914.8240 nan 0.5000 -0.0136
## 140 1303502062914.7742 nan 0.5000 -0.0026
## 160 1303502062914.5544 nan 0.5000 -0.0063
## 180 1303502062914.5261 nan 0.5000 -0.0016
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
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## 380 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0859 nan 0.5000 0.0899
## 2 0.9978 nan 0.5000 0.0309
## 3 0.9470 nan 0.5000 0.0063
## 4 0.9172 nan 0.5000 -0.0059
## 5 0.8926 nan 0.5000 -0.0085
## 6 0.8731 nan 0.5000 0.0003
## 7 0.8600 nan 0.5000 -0.0097
## 8 0.8515 nan 0.5000 -0.0144
## 9 0.8473 nan 0.5000 -0.0157
## 10 0.8403 nan 0.5000 -0.0208
## 20 0.7580 nan 0.5000 -0.0153
## 40 0.6131 nan 0.5000 -0.0089
## 60 0.4810 nan 0.5000 -0.0018
## 80 0.3981 nan 0.5000 -0.0041
## 100 0.3461 nan 0.5000 -0.0022
## 120 0.2865 nan 0.5000 -0.0054
## 140 0.2376 nan 0.5000 -0.0014
## 160 0.1952 nan 0.5000 -0.0032
## 180 0.1632 nan 0.5000 -0.0016
## 200 0.1403 nan 0.5000 -0.0022
## 220 0.1270 nan 0.5000 -0.0012
## 240 0.1080 nan 0.5000 -0.0019
## 260 0.0952 nan 0.5000 -0.0017
## 280 0.0842 nan 0.5000 -0.0010
## 300 0.0745 nan 0.5000 -0.0012
## 320 0.0656 nan 0.5000 -0.0016
## 340 0.0575 nan 0.5000 -0.0004
## 360 0.0509 nan 0.5000 -0.0010
## 380 0.0463 nan 0.5000 -0.0003
## 400 0.0386 nan 0.5000 -0.0003
## 420 0.0356 nan 0.5000 -0.0001
## 440 0.0311 nan 0.5000 -0.0002
## 460 0.0285 nan 0.5000 -0.0006
## 480 0.0249 nan 0.5000 -0.0002
## 500 0.0213 nan 0.5000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1309 nan 1.0000 0.0367
## 2 1.0473 nan 1.0000 0.0297
## 3 1.0225 nan 1.0000 -0.0124
## 4 0.9950 nan 1.0000 0.0097
## 5 0.9825 nan 1.0000 -0.0019
## 6 0.9880 nan 1.0000 -0.0178
## 7 0.9801 nan 1.0000 -0.0082
## 8 0.9637 nan 1.0000 -0.0115
## 9 0.9680 nan 1.0000 -0.0236
## 10 0.9985 nan 1.0000 -0.0519
## 20 0.9726 nan 1.0000 -0.0129
## 40 1.3207 nan 1.0000 -0.0199
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1181 nan 1.0000 0.0894
## 2 1.0595 nan 1.0000 0.0120
## 3 1.0036 nan 1.0000 0.0206
## 4 1.0045 nan 1.0000 -0.0137
## 5 1.0023 nan 1.0000 -0.0176
## 6 0.9766 nan 1.0000 0.0067
## 7 0.9625 nan 1.0000 -0.0052
## 8 0.9433 nan 1.0000 0.0059
## 9 0.9339 nan 1.0000 -0.0046
## 10 0.9200 nan 1.0000 -0.0082
## 20 0.9273 nan 1.0000 -0.0362
## 40 0.8884 nan 1.0000 -0.0052
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1348 nan 1.0000 0.0860
## 2 1.0704 nan 1.0000 0.0180
## 3 0.9984 nan 1.0000 0.0212
## 4 0.9552 nan 1.0000 -0.0004
## 5 0.9343 nan 1.0000 -0.0014
## 6 0.9225 nan 1.0000 0.0004
## 7 0.9222 nan 1.0000 -0.0181
## 8 0.9138 nan 1.0000 -0.0061
## 9 0.9645 nan 1.0000 -0.0515
## 10 0.9726 nan 1.0000 -0.0290
## 20 1.2712 nan 1.0000 -0.0049
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 3647384380668679330886068.0000 nan 1.0000 -0.0008
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0726 nan 1.0000 0.1000
## 2 1.0014 nan 1.0000 0.0113
## 3 1.0265 nan 1.0000 -0.0499
## 4 1.0201 nan 1.0000 -0.0221
## 5 1.0225 nan 1.0000 -0.0377
## 6 1.0010 nan 1.0000 -0.0129
## 7 0.9602 nan 1.0000 -0.0101
## 8 0.9543 nan 1.0000 -0.0263
## 9 0.9354 nan 1.0000 -0.0062
## 10 0.9039 nan 1.0000 -0.0007
## 20 0.8749 nan 1.0000 -0.0185
## 40 102937901.8866 nan 1.0000 -0.0032
## 60 102937901.7232 nan 1.0000 -0.0104
## 80 102937901.7241 nan 1.0000 -0.0529
## 100 inf nan 1.0000 -inf
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0714 nan 1.0000 0.0966
## 2 1.0074 nan 1.0000 0.0108
## 3 0.9714 nan 1.0000 -0.0007
## 4 0.9479 nan 1.0000 -0.0167
## 5 0.9406 nan 1.0000 -0.0186
## 6 0.9070 nan 1.0000 -0.0008
## 7 0.9141 nan 1.0000 -0.0453
## 8 0.9037 nan 1.0000 -0.0221
## 9 0.8802 nan 1.0000 -0.0176
## 10 0.8743 nan 1.0000 -0.0218
## 20 1.2974 nan 1.0000 -0.0476
## 40 inf nan 1.0000 nan
## 60 546431245524951957524.0000 nan 1.0000 -0.0000
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0954 nan 1.0000 0.0674
## 2 1.0049 nan 1.0000 0.0297
## 3 0.9707 nan 1.0000 0.0030
## 4 0.9409 nan 1.0000 -0.0084
## 5 0.9579 nan 1.0000 -0.0418
## 6 0.9570 nan 1.0000 -0.0288
## 7 0.9802 nan 1.0000 -0.0557
## 8 0.9522 nan 1.0000 -0.0004
## 9 0.9395 nan 1.0000 -0.0197
## 10 0.9388 nan 1.0000 -0.0340
## 20 0.9449 nan 1.0000 -0.0365
## 40 0.8449 nan 1.0000 -0.0105
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
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## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0161 nan 1.0000 0.1046
## 2 0.9527 nan 1.0000 0.0123
## 3 0.9179 nan 1.0000 -0.0047
## 4 0.9106 nan 1.0000 -0.0336
## 5 0.9129 nan 1.0000 -0.0379
## 6 0.9077 nan 1.0000 -0.0411
## 7 0.8429 nan 1.0000 0.0039
## 8 0.8338 nan 1.0000 -0.0276
## 9 0.8851 nan 1.0000 -0.0820
## 10 0.9325 nan 1.0000 -0.0942
## 20 4.4509 nan 1.0000 0.0354
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0749 nan 1.0000 0.0447
## 2 1.0581 nan 1.0000 -0.0444
## 3 0.9998 nan 1.0000 -0.0004
## 4 1.0610 nan 1.0000 -0.0847
## 5 1.0375 nan 1.0000 -0.0315
## 6 1.0217 nan 1.0000 -0.0298
## 7 1.0469 nan 1.0000 -0.0850
## 8 1.0059 nan 1.0000 -0.0199
## 9 1.0423 nan 1.0000 -0.0730
## 10 1.0650 nan 1.0000 -0.0734
## 20 73544255.9217 nan 1.0000 -36718988.0668
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
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## 420 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0404 nan 1.0000 0.0925
## 2 0.9940 nan 1.0000 -0.0034
## 3 0.9820 nan 1.0000 -0.0218
## 4 0.9741 nan 1.0000 -0.0371
## 5 0.9843 nan 1.0000 -0.0591
## 6 0.9850 nan 1.0000 -0.0444
## 7 1.0001 nan 1.0000 -0.0565
## 8 0.9667 nan 1.0000 -0.0109
## 9 1.0577 nan 1.0000 -0.1141
## 10 1.3607 nan 1.0000 -0.3619
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
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## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
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## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## 440 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
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## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
## 500 1.2934 nan 0.0000 0.0000
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
## 200 1.2934 nan 0.0000 0.0000
## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
## 300 1.2934 nan 0.0000 0.0000
## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
## 360 1.2934 nan 0.0000 0.0000
## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
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## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
## 260 1.2934 nan 0.0000 0.0000
## 280 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
## 480 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
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## 220 1.2934 nan 0.0000 0.0000
## 240 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
## 340 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
## 420 1.2934 nan 0.0000 0.0000
## 440 1.2934 nan 0.0000 0.0000
## 460 1.2934 nan 0.0000 0.0000
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2909 nan 0.0010 0.0001
## 8 1.2905 nan 0.0010 0.0002
## 9 1.2901 nan 0.0010 0.0002
## 10 1.2898 nan 0.0010 0.0002
## 20 1.2863 nan 0.0010 0.0002
## 40 1.2795 nan 0.0010 0.0001
## 60 1.2731 nan 0.0010 0.0001
## 80 1.2666 nan 0.0010 0.0002
## 100 1.2603 nan 0.0010 0.0001
## 120 1.2547 nan 0.0010 0.0001
## 140 1.2490 nan 0.0010 0.0001
## 160 1.2434 nan 0.0010 0.0001
## 180 1.2380 nan 0.0010 0.0001
## 200 1.2328 nan 0.0010 0.0001
## 220 1.2276 nan 0.0010 0.0001
## 240 1.2226 nan 0.0010 0.0001
## 260 1.2179 nan 0.0010 0.0001
## 280 1.2132 nan 0.0010 0.0001
## 300 1.2086 nan 0.0010 0.0001
## 320 1.2042 nan 0.0010 0.0001
## 340 1.1997 nan 0.0010 0.0001
## 360 1.1954 nan 0.0010 0.0001
## 380 1.1912 nan 0.0010 0.0001
## 400 1.1871 nan 0.0010 0.0001
## 420 1.1831 nan 0.0010 0.0001
## 440 1.1792 nan 0.0010 0.0001
## 460 1.1753 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
## 9 1.2900 nan 0.0010 0.0002
## 10 1.2896 nan 0.0010 0.0002
## 20 1.2862 nan 0.0010 0.0001
## 40 1.2795 nan 0.0010 0.0002
## 60 1.2730 nan 0.0010 0.0001
## 80 1.2665 nan 0.0010 0.0001
## 100 1.2603 nan 0.0010 0.0001
## 120 1.2543 nan 0.0010 0.0001
## 140 1.2490 nan 0.0010 0.0001
## 160 1.2434 nan 0.0010 0.0001
## 180 1.2381 nan 0.0010 0.0001
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## 220 1.2278 nan 0.0010 0.0001
## 240 1.2229 nan 0.0010 0.0001
## 260 1.2182 nan 0.0010 0.0001
## 280 1.2134 nan 0.0010 0.0001
## 300 1.2088 nan 0.0010 0.0001
## 320 1.2043 nan 0.0010 0.0001
## 340 1.2000 nan 0.0010 0.0001
## 360 1.1958 nan 0.0010 0.0001
## 380 1.1916 nan 0.0010 0.0001
## 400 1.1876 nan 0.0010 0.0001
## 420 1.1837 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0001
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0001
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
## 8 1.2905 nan 0.0010 0.0001
## 9 1.2902 nan 0.0010 0.0002
## 10 1.2898 nan 0.0010 0.0002
## 20 1.2863 nan 0.0010 0.0001
## 40 1.2796 nan 0.0010 0.0002
## 60 1.2729 nan 0.0010 0.0002
## 80 1.2665 nan 0.0010 0.0001
## 100 1.2604 nan 0.0010 0.0001
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## 140 1.2486 nan 0.0010 0.0001
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## 180 1.2376 nan 0.0010 0.0001
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## 320 1.2039 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
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## 10 1.2887 nan 0.0010 0.0002
## 20 1.2839 nan 0.0010 0.0002
## 40 1.2749 nan 0.0010 0.0002
## 60 1.2662 nan 0.0010 0.0002
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## 100 1.2497 nan 0.0010 0.0002
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## 180 1.2194 nan 0.0010 0.0001
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## 240 1.1986 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
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## 10 1.2888 nan 0.0010 0.0002
## 20 1.2842 nan 0.0010 0.0002
## 40 1.2754 nan 0.0010 0.0002
## 60 1.2665 nan 0.0010 0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
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## 10 1.2886 nan 0.0010 0.0002
## 20 1.2841 nan 0.0010 0.0002
## 40 1.2752 nan 0.0010 0.0002
## 60 1.2665 nan 0.0010 0.0002
## 80 1.2580 nan 0.0010 0.0002
## 100 1.2496 nan 0.0010 0.0002
## 120 1.2415 nan 0.0010 0.0002
## 140 1.2338 nan 0.0010 0.0002
## 160 1.2264 nan 0.0010 0.0002
## 180 1.2194 nan 0.0010 0.0001
## 200 1.2123 nan 0.0010 0.0001
## 220 1.2055 nan 0.0010 0.0001
## 240 1.1989 nan 0.0010 0.0001
## 260 1.1922 nan 0.0010 0.0001
## 280 1.1859 nan 0.0010 0.0001
## 300 1.1796 nan 0.0010 0.0001
## 320 1.1737 nan 0.0010 0.0001
## 340 1.1680 nan 0.0010 0.0001
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## 380 1.1568 nan 0.0010 0.0001
## 400 1.1513 nan 0.0010 0.0001
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## 480 1.1309 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0003
## 3 1.2917 nan 0.0010 0.0003
## 4 1.2911 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2900 nan 0.0010 0.0003
## 7 1.2894 nan 0.0010 0.0003
## 8 1.2889 nan 0.0010 0.0002
## 9 1.2884 nan 0.0010 0.0002
## 10 1.2878 nan 0.0010 0.0003
## 20 1.2825 nan 0.0010 0.0002
## 40 1.2722 nan 0.0010 0.0002
## 60 1.2622 nan 0.0010 0.0002
## 80 1.2523 nan 0.0010 0.0002
## 100 1.2426 nan 0.0010 0.0002
## 120 1.2332 nan 0.0010 0.0002
## 140 1.2243 nan 0.0010 0.0002
## 160 1.2156 nan 0.0010 0.0002
## 180 1.2069 nan 0.0010 0.0002
## 200 1.1987 nan 0.0010 0.0002
## 220 1.1909 nan 0.0010 0.0002
## 240 1.1833 nan 0.0010 0.0001
## 260 1.1756 nan 0.0010 0.0002
## 280 1.1685 nan 0.0010 0.0002
## 300 1.1615 nan 0.0010 0.0001
## 320 1.1545 nan 0.0010 0.0001
## 340 1.1477 nan 0.0010 0.0001
## 360 1.1413 nan 0.0010 0.0001
## 380 1.1351 nan 0.0010 0.0001
## 400 1.1288 nan 0.0010 0.0002
## 420 1.1225 nan 0.0010 0.0001
## 440 1.1165 nan 0.0010 0.0001
## 460 1.1109 nan 0.0010 0.0001
## 480 1.1053 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0003
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2907 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0002
## 8 1.2891 nan 0.0010 0.0002
## 9 1.2886 nan 0.0010 0.0002
## 10 1.2881 nan 0.0010 0.0002
## 20 1.2827 nan 0.0010 0.0002
## 40 1.2721 nan 0.0010 0.0002
## 60 1.2620 nan 0.0010 0.0002
## 80 1.2522 nan 0.0010 0.0002
## 100 1.2427 nan 0.0010 0.0002
## 120 1.2334 nan 0.0010 0.0002
## 140 1.2243 nan 0.0010 0.0001
## 160 1.2160 nan 0.0010 0.0002
## 180 1.2075 nan 0.0010 0.0002
## 200 1.1993 nan 0.0010 0.0001
## 220 1.1914 nan 0.0010 0.0002
## 240 1.1834 nan 0.0010 0.0002
## 260 1.1759 nan 0.0010 0.0002
## 280 1.1684 nan 0.0010 0.0002
## 300 1.1615 nan 0.0010 0.0002
## 320 1.1546 nan 0.0010 0.0001
## 340 1.1477 nan 0.0010 0.0001
## 360 1.1412 nan 0.0010 0.0001
## 380 1.1351 nan 0.0010 0.0001
## 400 1.1287 nan 0.0010 0.0001
## 420 1.1227 nan 0.0010 0.0001
## 440 1.1165 nan 0.0010 0.0001
## 460 1.1109 nan 0.0010 0.0001
## 480 1.1054 nan 0.0010 0.0001
## 500 1.0997 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0003
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2917 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0003
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0002
## 8 1.2891 nan 0.0010 0.0002
## 9 1.2885 nan 0.0010 0.0003
## 10 1.2880 nan 0.0010 0.0002
## 20 1.2825 nan 0.0010 0.0003
## 40 1.2717 nan 0.0010 0.0002
## 60 1.2614 nan 0.0010 0.0002
## 80 1.2515 nan 0.0010 0.0002
## 100 1.2423 nan 0.0010 0.0002
## 120 1.2330 nan 0.0010 0.0002
## 140 1.2239 nan 0.0010 0.0002
## 160 1.2153 nan 0.0010 0.0002
## 180 1.2068 nan 0.0010 0.0002
## 200 1.1987 nan 0.0010 0.0002
## 220 1.1907 nan 0.0010 0.0002
## 240 1.1831 nan 0.0010 0.0002
## 260 1.1756 nan 0.0010 0.0002
## 280 1.1683 nan 0.0010 0.0001
## 300 1.1615 nan 0.0010 0.0001
## 320 1.1547 nan 0.0010 0.0001
## 340 1.1480 nan 0.0010 0.0001
## 360 1.1416 nan 0.0010 0.0002
## 380 1.1353 nan 0.0010 0.0001
## 400 1.1290 nan 0.0010 0.0001
## 420 1.1231 nan 0.0010 0.0001
## 440 1.1172 nan 0.0010 0.0001
## 460 1.1117 nan 0.0010 0.0001
## 480 1.1057 nan 0.0010 0.0001
## 500 1.1005 nan 0.0010 0.0001
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2556 nan 0.1000 0.0153
## 2 1.2249 nan 0.1000 0.0134
## 3 1.2043 nan 0.1000 0.0079
## 4 1.1816 nan 0.1000 0.0084
## 5 1.1616 nan 0.1000 0.0083
## 6 1.1445 nan 0.1000 0.0073
## 7 1.1298 nan 0.1000 0.0062
## 8 1.1138 nan 0.1000 0.0076
## 9 1.1012 nan 0.1000 0.0033
## 10 1.0905 nan 0.1000 0.0046
## 20 1.0005 nan 0.1000 0.0015
## 40 0.9197 nan 0.1000 0.0001
## 60 0.8748 nan 0.1000 -0.0002
## 80 0.8491 nan 0.1000 0.0001
## 100 0.8303 nan 0.1000 -0.0005
## 120 0.8153 nan 0.1000 -0.0002
## 140 0.8057 nan 0.1000 -0.0008
## 160 0.7962 nan 0.1000 -0.0015
## 180 0.7904 nan 0.1000 -0.0007
## 200 0.7827 nan 0.1000 -0.0004
## 220 0.7766 nan 0.1000 -0.0016
## 240 0.7682 nan 0.1000 -0.0010
## 260 0.7614 nan 0.1000 -0.0024
## 280 0.7563 nan 0.1000 -0.0015
## 300 0.7505 nan 0.1000 -0.0009
## 320 0.7446 nan 0.1000 -0.0007
## 340 0.7392 nan 0.1000 -0.0011
## 360 0.7348 nan 0.1000 -0.0012
## 380 0.7311 nan 0.1000 -0.0013
## 400 0.7262 nan 0.1000 -0.0009
## 420 0.7220 nan 0.1000 -0.0003
## 440 0.7187 nan 0.1000 -0.0007
## 460 0.7138 nan 0.1000 -0.0013
## 480 0.7070 nan 0.1000 -0.0010
## 500 0.7042 nan 0.1000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2589 nan 0.1000 0.0155
## 2 1.2320 nan 0.1000 0.0110
## 3 1.2027 nan 0.1000 0.0113
## 4 1.1845 nan 0.1000 0.0073
## 5 1.1642 nan 0.1000 0.0084
## 6 1.1456 nan 0.1000 0.0070
## 7 1.1282 nan 0.1000 0.0078
## 8 1.1116 nan 0.1000 0.0056
## 9 1.0966 nan 0.1000 0.0057
## 10 1.0860 nan 0.1000 0.0045
## 20 0.9962 nan 0.1000 0.0023
## 40 0.9226 nan 0.1000 -0.0001
## 60 0.8796 nan 0.1000 0.0004
## 80 0.8551 nan 0.1000 -0.0013
## 100 0.8343 nan 0.1000 -0.0004
## 120 0.8216 nan 0.1000 -0.0011
## 140 0.8113 nan 0.1000 -0.0005
## 160 0.8036 nan 0.1000 -0.0007
## 180 0.7929 nan 0.1000 -0.0010
## 200 0.7859 nan 0.1000 -0.0010
## 220 0.7787 nan 0.1000 -0.0010
## 240 0.7706 nan 0.1000 -0.0008
## 260 0.7631 nan 0.1000 -0.0001
## 280 0.7553 nan 0.1000 -0.0008
## 300 0.7490 nan 0.1000 -0.0005
## 320 0.7438 nan 0.1000 -0.0007
## 340 0.7395 nan 0.1000 -0.0015
## 360 0.7320 nan 0.1000 -0.0010
## 380 0.7260 nan 0.1000 -0.0010
## 400 0.7201 nan 0.1000 -0.0010
## 420 0.7138 nan 0.1000 -0.0009
## 440 0.7094 nan 0.1000 -0.0008
## 460 0.7054 nan 0.1000 -0.0004
## 480 0.6999 nan 0.1000 -0.0012
## 500 0.6947 nan 0.1000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2569 nan 0.1000 0.0158
## 2 1.2327 nan 0.1000 0.0116
## 3 1.2075 nan 0.1000 0.0113
## 4 1.1886 nan 0.1000 0.0090
## 5 1.1689 nan 0.1000 0.0065
## 6 1.1525 nan 0.1000 0.0074
## 7 1.1380 nan 0.1000 0.0047
## 8 1.1231 nan 0.1000 0.0057
## 9 1.1091 nan 0.1000 0.0067
## 10 1.0939 nan 0.1000 0.0057
## 20 1.0028 nan 0.1000 0.0018
## 40 0.9197 nan 0.1000 0.0000
## 60 0.8805 nan 0.1000 -0.0003
## 80 0.8533 nan 0.1000 0.0000
## 100 0.8358 nan 0.1000 -0.0005
## 120 0.8193 nan 0.1000 -0.0009
## 140 0.8077 nan 0.1000 -0.0011
## 160 0.7983 nan 0.1000 -0.0014
## 180 0.7890 nan 0.1000 -0.0023
## 200 0.7804 nan 0.1000 -0.0017
## 220 0.7739 nan 0.1000 -0.0009
## 240 0.7666 nan 0.1000 -0.0007
## 260 0.7604 nan 0.1000 -0.0018
## 280 0.7547 nan 0.1000 -0.0009
## 300 0.7494 nan 0.1000 -0.0007
## 320 0.7449 nan 0.1000 -0.0010
## 340 0.7399 nan 0.1000 -0.0007
## 360 0.7345 nan 0.1000 -0.0010
## 380 0.7279 nan 0.1000 -0.0006
## 400 0.7244 nan 0.1000 -0.0007
## 420 0.7195 nan 0.1000 -0.0008
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## 460 0.7113 nan 0.1000 -0.0007
## 480 0.7074 nan 0.1000 -0.0016
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2472 nan 0.1000 0.0196
## 2 1.2117 nan 0.1000 0.0173
## 3 1.1779 nan 0.1000 0.0155
## 4 1.1490 nan 0.1000 0.0112
## 5 1.1263 nan 0.1000 0.0081
## 6 1.1028 nan 0.1000 0.0102
## 7 1.0822 nan 0.1000 0.0082
## 8 1.0665 nan 0.1000 0.0063
## 9 1.0520 nan 0.1000 0.0065
## 10 1.0335 nan 0.1000 0.0059
## 20 0.9409 nan 0.1000 0.0015
## 40 0.8544 nan 0.1000 -0.0007
## 60 0.8092 nan 0.1000 -0.0002
## 80 0.7754 nan 0.1000 -0.0005
## 100 0.7449 nan 0.1000 0.0002
## 120 0.7236 nan 0.1000 -0.0005
## 140 0.6999 nan 0.1000 -0.0010
## 160 0.6803 nan 0.1000 -0.0018
## 180 0.6569 nan 0.1000 -0.0007
## 200 0.6353 nan 0.1000 -0.0016
## 220 0.6180 nan 0.1000 -0.0017
## 240 0.6039 nan 0.1000 -0.0018
## 260 0.5883 nan 0.1000 -0.0010
## 280 0.5762 nan 0.1000 -0.0011
## 300 0.5622 nan 0.1000 -0.0011
## 320 0.5470 nan 0.1000 -0.0006
## 340 0.5354 nan 0.1000 -0.0010
## 360 0.5216 nan 0.1000 -0.0011
## 380 0.5087 nan 0.1000 -0.0011
## 400 0.4944 nan 0.1000 -0.0002
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## 460 0.4590 nan 0.1000 -0.0003
## 480 0.4487 nan 0.1000 -0.0002
## 500 0.4389 nan 0.1000 -0.0013
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2513 nan 0.1000 0.0196
## 2 1.2161 nan 0.1000 0.0157
## 3 1.1878 nan 0.1000 0.0133
## 4 1.1558 nan 0.1000 0.0100
## 5 1.1358 nan 0.1000 0.0072
## 6 1.1098 nan 0.1000 0.0116
## 7 1.0912 nan 0.1000 0.0062
## 8 1.0739 nan 0.1000 0.0084
## 9 1.0544 nan 0.1000 0.0067
## 10 1.0409 nan 0.1000 0.0029
## 20 0.9391 nan 0.1000 0.0013
## 40 0.8553 nan 0.1000 -0.0009
## 60 0.8121 nan 0.1000 -0.0005
## 80 0.7780 nan 0.1000 -0.0015
## 100 0.7510 nan 0.1000 -0.0004
## 120 0.7290 nan 0.1000 -0.0008
## 140 0.7111 nan 0.1000 -0.0020
## 160 0.6847 nan 0.1000 -0.0010
## 180 0.6682 nan 0.1000 -0.0023
## 200 0.6493 nan 0.1000 -0.0019
## 220 0.6330 nan 0.1000 -0.0022
## 240 0.6131 nan 0.1000 -0.0009
## 260 0.5993 nan 0.1000 -0.0017
## 280 0.5835 nan 0.1000 -0.0012
## 300 0.5693 nan 0.1000 -0.0009
## 320 0.5492 nan 0.1000 -0.0022
## 340 0.5373 nan 0.1000 -0.0008
## 360 0.5278 nan 0.1000 -0.0006
## 380 0.5136 nan 0.1000 -0.0009
## 400 0.5034 nan 0.1000 -0.0015
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## 440 0.4768 nan 0.1000 -0.0007
## 460 0.4657 nan 0.1000 -0.0010
## 480 0.4555 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2482 nan 0.1000 0.0189
## 2 1.2095 nan 0.1000 0.0168
## 3 1.1747 nan 0.1000 0.0137
## 4 1.1445 nan 0.1000 0.0126
## 5 1.1205 nan 0.1000 0.0114
## 6 1.0979 nan 0.1000 0.0108
## 7 1.0818 nan 0.1000 0.0052
## 8 1.0609 nan 0.1000 0.0074
## 9 1.0440 nan 0.1000 0.0063
## 10 1.0321 nan 0.1000 0.0045
## 20 0.9311 nan 0.1000 0.0003
## 40 0.8469 nan 0.1000 0.0001
## 60 0.7988 nan 0.1000 -0.0022
## 80 0.7634 nan 0.1000 -0.0011
## 100 0.7367 nan 0.1000 -0.0002
## 120 0.7122 nan 0.1000 -0.0006
## 140 0.6909 nan 0.1000 -0.0001
## 160 0.6733 nan 0.1000 -0.0013
## 180 0.6536 nan 0.1000 -0.0012
## 200 0.6354 nan 0.1000 -0.0008
## 220 0.6185 nan 0.1000 -0.0000
## 240 0.6049 nan 0.1000 -0.0007
## 260 0.5882 nan 0.1000 -0.0008
## 280 0.5747 nan 0.1000 -0.0005
## 300 0.5565 nan 0.1000 -0.0004
## 320 0.5420 nan 0.1000 -0.0008
## 340 0.5310 nan 0.1000 -0.0007
## 360 0.5197 nan 0.1000 -0.0018
## 380 0.5072 nan 0.1000 -0.0013
## 400 0.4935 nan 0.1000 -0.0008
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## 440 0.4729 nan 0.1000 -0.0010
## 460 0.4639 nan 0.1000 -0.0001
## 480 0.4524 nan 0.1000 -0.0016
## 500 0.4398 nan 0.1000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2355 nan 0.1000 0.0268
## 2 1.1914 nan 0.1000 0.0174
## 3 1.1525 nan 0.1000 0.0163
## 4 1.1181 nan 0.1000 0.0137
## 5 1.0899 nan 0.1000 0.0133
## 6 1.0679 nan 0.1000 0.0061
## 7 1.0467 nan 0.1000 0.0064
## 8 1.0258 nan 0.1000 0.0077
## 9 1.0090 nan 0.1000 0.0067
## 10 0.9932 nan 0.1000 0.0038
## 20 0.8897 nan 0.1000 -0.0002
## 40 0.7980 nan 0.1000 -0.0012
## 60 0.7421 nan 0.1000 -0.0008
## 80 0.6925 nan 0.1000 -0.0024
## 100 0.6543 nan 0.1000 -0.0014
## 120 0.6175 nan 0.1000 -0.0034
## 140 0.5821 nan 0.1000 -0.0008
## 160 0.5513 nan 0.1000 -0.0012
## 180 0.5272 nan 0.1000 -0.0011
## 200 0.5064 nan 0.1000 -0.0014
## 220 0.4851 nan 0.1000 -0.0012
## 240 0.4614 nan 0.1000 -0.0007
## 260 0.4399 nan 0.1000 -0.0019
## 280 0.4230 nan 0.1000 -0.0014
## 300 0.4056 nan 0.1000 -0.0003
## 320 0.3909 nan 0.1000 -0.0008
## 340 0.3759 nan 0.1000 -0.0005
## 360 0.3612 nan 0.1000 -0.0009
## 380 0.3495 nan 0.1000 -0.0008
## 400 0.3361 nan 0.1000 -0.0013
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## 460 0.2986 nan 0.1000 -0.0001
## 480 0.2875 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2395 nan 0.1000 0.0251
## 2 1.1995 nan 0.1000 0.0138
## 3 1.1638 nan 0.1000 0.0176
## 4 1.1349 nan 0.1000 0.0090
## 5 1.1028 nan 0.1000 0.0108
## 6 1.0804 nan 0.1000 0.0080
## 7 1.0538 nan 0.1000 0.0100
## 8 1.0367 nan 0.1000 0.0064
## 9 1.0171 nan 0.1000 0.0068
## 10 1.0012 nan 0.1000 0.0049
## 20 0.8970 nan 0.1000 -0.0003
## 40 0.8025 nan 0.1000 -0.0020
## 60 0.7465 nan 0.1000 0.0003
## 80 0.7031 nan 0.1000 -0.0025
## 100 0.6696 nan 0.1000 -0.0022
## 120 0.6359 nan 0.1000 -0.0016
## 140 0.6076 nan 0.1000 -0.0015
## 160 0.5819 nan 0.1000 0.0001
## 180 0.5563 nan 0.1000 -0.0012
## 200 0.5290 nan 0.1000 -0.0017
## 220 0.5070 nan 0.1000 -0.0009
## 240 0.4828 nan 0.1000 -0.0012
## 260 0.4630 nan 0.1000 -0.0017
## 280 0.4430 nan 0.1000 -0.0010
## 300 0.4217 nan 0.1000 -0.0016
## 320 0.4065 nan 0.1000 -0.0012
## 340 0.3907 nan 0.1000 -0.0008
## 360 0.3781 nan 0.1000 -0.0014
## 380 0.3641 nan 0.1000 -0.0004
## 400 0.3500 nan 0.1000 -0.0011
## 420 0.3361 nan 0.1000 -0.0009
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## 460 0.3124 nan 0.1000 -0.0005
## 480 0.3006 nan 0.1000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2484 nan 0.1000 0.0193
## 2 1.2075 nan 0.1000 0.0180
## 3 1.1678 nan 0.1000 0.0137
## 4 1.1352 nan 0.1000 0.0129
## 5 1.1087 nan 0.1000 0.0098
## 6 1.0774 nan 0.1000 0.0147
## 7 1.0578 nan 0.1000 0.0056
## 8 1.0372 nan 0.1000 0.0062
## 9 1.0173 nan 0.1000 0.0068
## 10 0.9979 nan 0.1000 0.0086
## 20 0.8925 nan 0.1000 0.0003
## 40 0.8019 nan 0.1000 0.0002
## 60 0.7512 nan 0.1000 -0.0030
## 80 0.7068 nan 0.1000 -0.0013
## 100 0.6711 nan 0.1000 -0.0024
## 120 0.6403 nan 0.1000 -0.0006
## 140 0.6155 nan 0.1000 -0.0017
## 160 0.5850 nan 0.1000 -0.0016
## 180 0.5548 nan 0.1000 -0.0012
## 200 0.5282 nan 0.1000 -0.0002
## 220 0.5038 nan 0.1000 -0.0007
## 240 0.4838 nan 0.1000 -0.0011
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## 280 0.4374 nan 0.1000 -0.0003
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## 320 0.4051 nan 0.1000 -0.0015
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## 380 0.3578 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2234 nan 0.2000 0.0286
## 2 1.1873 nan 0.2000 0.0146
## 3 1.1497 nan 0.2000 0.0144
## 4 1.1172 nan 0.2000 0.0161
## 5 1.0869 nan 0.2000 0.0131
## 6 1.0671 nan 0.2000 0.0059
## 7 1.0432 nan 0.2000 0.0099
## 8 1.0275 nan 0.2000 0.0041
## 9 1.0177 nan 0.2000 0.0024
## 10 1.0086 nan 0.2000 -0.0028
## 20 0.9204 nan 0.2000 0.0014
## 40 0.8572 nan 0.2000 -0.0010
## 60 0.8279 nan 0.2000 -0.0025
## 80 0.8049 nan 0.2000 -0.0019
## 100 0.7864 nan 0.2000 -0.0025
## 120 0.7743 nan 0.2000 -0.0020
## 140 0.7599 nan 0.2000 -0.0015
## 160 0.7519 nan 0.2000 -0.0034
## 180 0.7387 nan 0.2000 -0.0022
## 200 0.7297 nan 0.2000 -0.0011
## 220 0.7196 nan 0.2000 -0.0018
## 240 0.7086 nan 0.2000 -0.0016
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## 280 0.6944 nan 0.2000 -0.0015
## 300 0.6883 nan 0.2000 -0.0023
## 320 0.6781 nan 0.2000 -0.0018
## 340 0.6723 nan 0.2000 -0.0011
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## 380 0.6612 nan 0.2000 -0.0032
## 400 0.6530 nan 0.2000 -0.0003
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## 460 0.6372 nan 0.2000 -0.0021
## 480 0.6351 nan 0.2000 -0.0016
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2259 nan 0.2000 0.0348
## 2 1.1767 nan 0.2000 0.0204
## 3 1.1419 nan 0.2000 0.0149
## 4 1.1166 nan 0.2000 0.0101
## 5 1.0895 nan 0.2000 0.0128
## 6 1.0678 nan 0.2000 0.0079
## 7 1.0485 nan 0.2000 0.0071
## 8 1.0310 nan 0.2000 0.0067
## 9 1.0180 nan 0.2000 0.0043
## 10 1.0009 nan 0.2000 0.0066
## 20 0.9205 nan 0.2000 0.0004
## 40 0.8560 nan 0.2000 0.0003
## 60 0.8259 nan 0.2000 -0.0020
## 80 0.8003 nan 0.2000 -0.0012
## 100 0.7862 nan 0.2000 -0.0016
## 120 0.7729 nan 0.2000 -0.0018
## 140 0.7607 nan 0.2000 -0.0039
## 160 0.7497 nan 0.2000 -0.0018
## 180 0.7364 nan 0.2000 0.0001
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## 220 0.7185 nan 0.2000 -0.0029
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## 260 0.6992 nan 0.2000 -0.0028
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## 340 0.6679 nan 0.2000 -0.0019
## 360 0.6619 nan 0.2000 -0.0029
## 380 0.6524 nan 0.2000 -0.0024
## 400 0.6455 nan 0.2000 -0.0007
## 420 0.6393 nan 0.2000 -0.0022
## 440 0.6340 nan 0.2000 -0.0010
## 460 0.6277 nan 0.2000 -0.0010
## 480 0.6252 nan 0.2000 -0.0015
## 500 0.6203 nan 0.2000 -0.0011
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2296 nan 0.2000 0.0310
## 2 1.1923 nan 0.2000 0.0132
## 3 1.1507 nan 0.2000 0.0211
## 4 1.1277 nan 0.2000 0.0063
## 5 1.0944 nan 0.2000 0.0117
## 6 1.0705 nan 0.2000 0.0092
## 7 1.0445 nan 0.2000 0.0117
## 8 1.0274 nan 0.2000 0.0044
## 9 1.0153 nan 0.2000 0.0015
## 10 0.9996 nan 0.2000 0.0065
## 20 0.9160 nan 0.2000 0.0010
## 40 0.8463 nan 0.2000 -0.0003
## 60 0.8180 nan 0.2000 -0.0010
## 80 0.7983 nan 0.2000 -0.0009
## 100 0.7747 nan 0.2000 -0.0039
## 120 0.7600 nan 0.2000 -0.0014
## 140 0.7492 nan 0.2000 -0.0014
## 160 0.7385 nan 0.2000 -0.0037
## 180 0.7317 nan 0.2000 -0.0011
## 200 0.7245 nan 0.2000 -0.0026
## 220 0.7128 nan 0.2000 -0.0007
## 240 0.7054 nan 0.2000 -0.0011
## 260 0.6968 nan 0.2000 -0.0012
## 280 0.6897 nan 0.2000 -0.0024
## 300 0.6803 nan 0.2000 -0.0039
## 320 0.6717 nan 0.2000 -0.0038
## 340 0.6670 nan 0.2000 -0.0010
## 360 0.6611 nan 0.2000 -0.0013
## 380 0.6549 nan 0.2000 -0.0007
## 400 0.6497 nan 0.2000 -0.0027
## 420 0.6420 nan 0.2000 -0.0005
## 440 0.6382 nan 0.2000 -0.0003
## 460 0.6296 nan 0.2000 -0.0024
## 480 0.6249 nan 0.2000 -0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2043 nan 0.2000 0.0330
## 2 1.1431 nan 0.2000 0.0270
## 3 1.0975 nan 0.2000 0.0200
## 4 1.0626 nan 0.2000 0.0103
## 5 1.0239 nan 0.2000 0.0134
## 6 1.0047 nan 0.2000 0.0042
## 7 0.9805 nan 0.2000 0.0108
## 8 0.9649 nan 0.2000 0.0036
## 9 0.9440 nan 0.2000 0.0052
## 10 0.9321 nan 0.2000 0.0012
## 20 0.8482 nan 0.2000 0.0013
## 40 0.7808 nan 0.2000 -0.0037
## 60 0.7188 nan 0.2000 -0.0043
## 80 0.6811 nan 0.2000 -0.0008
## 100 0.6400 nan 0.2000 -0.0030
## 120 0.6071 nan 0.2000 -0.0020
## 140 0.5904 nan 0.2000 -0.0021
## 160 0.5591 nan 0.2000 -0.0037
## 180 0.5177 nan 0.2000 -0.0006
## 200 0.4895 nan 0.2000 -0.0029
## 220 0.4676 nan 0.2000 -0.0018
## 240 0.4474 nan 0.2000 -0.0008
## 260 0.4287 nan 0.2000 -0.0033
## 280 0.4047 nan 0.2000 -0.0011
## 300 0.3879 nan 0.2000 -0.0027
## 320 0.3719 nan 0.2000 -0.0003
## 340 0.3567 nan 0.2000 -0.0011
## 360 0.3372 nan 0.2000 -0.0012
## 380 0.3271 nan 0.2000 -0.0013
## 400 0.3118 nan 0.2000 -0.0019
## 420 0.2987 nan 0.2000 -0.0008
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## 460 0.2782 nan 0.2000 -0.0007
## 480 0.2696 nan 0.2000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1934 nan 0.2000 0.0330
## 2 1.1288 nan 0.2000 0.0253
## 3 1.0845 nan 0.2000 0.0201
## 4 1.0433 nan 0.2000 0.0189
## 5 1.0201 nan 0.2000 0.0070
## 6 0.9968 nan 0.2000 0.0055
## 7 0.9742 nan 0.2000 0.0078
## 8 0.9575 nan 0.2000 0.0039
## 9 0.9408 nan 0.2000 0.0025
## 10 0.9264 nan 0.2000 0.0039
## 20 0.8481 nan 0.2000 -0.0009
## 40 0.7838 nan 0.2000 -0.0019
## 60 0.7397 nan 0.2000 -0.0024
## 80 0.7002 nan 0.2000 -0.0027
## 100 0.6682 nan 0.2000 -0.0005
## 120 0.6286 nan 0.2000 -0.0042
## 140 0.5939 nan 0.2000 -0.0027
## 160 0.5594 nan 0.2000 -0.0025
## 180 0.5300 nan 0.2000 -0.0005
## 200 0.5014 nan 0.2000 -0.0013
## 220 0.4829 nan 0.2000 -0.0002
## 240 0.4586 nan 0.2000 -0.0020
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## 280 0.4154 nan 0.2000 -0.0009
## 300 0.3970 nan 0.2000 -0.0005
## 320 0.3804 nan 0.2000 -0.0013
## 340 0.3685 nan 0.2000 -0.0024
## 360 0.3543 nan 0.2000 -0.0025
## 380 0.3449 nan 0.2000 -0.0007
## 400 0.3298 nan 0.2000 -0.0011
## 420 0.3199 nan 0.2000 -0.0016
## 440 0.3081 nan 0.2000 -0.0020
## 460 0.2999 nan 0.2000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2087 nan 0.2000 0.0401
## 2 1.1453 nan 0.2000 0.0259
## 3 1.0956 nan 0.2000 0.0162
## 4 1.0582 nan 0.2000 0.0129
## 5 1.0277 nan 0.2000 0.0103
## 6 1.0057 nan 0.2000 0.0081
## 7 0.9859 nan 0.2000 0.0043
## 8 0.9678 nan 0.2000 0.0018
## 9 0.9513 nan 0.2000 0.0037
## 10 0.9326 nan 0.2000 0.0032
## 20 0.8491 nan 0.2000 0.0027
## 40 0.7712 nan 0.2000 -0.0022
## 60 0.7246 nan 0.2000 -0.0025
## 80 0.6914 nan 0.2000 -0.0031
## 100 0.6498 nan 0.2000 -0.0014
## 120 0.6145 nan 0.2000 -0.0025
## 140 0.5764 nan 0.2000 -0.0013
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## 180 0.5254 nan 0.2000 -0.0005
## 200 0.5001 nan 0.2000 -0.0015
## 220 0.4796 nan 0.2000 -0.0014
## 240 0.4682 nan 0.2000 -0.0034
## 260 0.4469 nan 0.2000 -0.0027
## 280 0.4257 nan 0.2000 -0.0005
## 300 0.4092 nan 0.2000 -0.0017
## 320 0.3933 nan 0.2000 -0.0019
## 340 0.3806 nan 0.2000 -0.0016
## 360 0.3682 nan 0.2000 -0.0017
## 380 0.3530 nan 0.2000 -0.0006
## 400 0.3393 nan 0.2000 -0.0015
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## 440 0.3129 nan 0.2000 -0.0006
## 460 0.3004 nan 0.2000 -0.0024
## 480 0.2931 nan 0.2000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1997 nan 0.2000 0.0473
## 2 1.1360 nan 0.2000 0.0238
## 3 1.0807 nan 0.2000 0.0241
## 4 1.0356 nan 0.2000 0.0164
## 5 1.0042 nan 0.2000 0.0085
## 6 0.9720 nan 0.2000 0.0104
## 7 0.9520 nan 0.2000 0.0049
## 8 0.9289 nan 0.2000 0.0057
## 9 0.9219 nan 0.2000 -0.0045
## 10 0.9044 nan 0.2000 0.0032
## 20 0.8108 nan 0.2000 -0.0024
## 40 0.7178 nan 0.2000 -0.0015
## 60 0.6429 nan 0.2000 -0.0027
## 80 0.5705 nan 0.2000 -0.0061
## 100 0.5230 nan 0.2000 -0.0018
## 120 0.4778 nan 0.2000 -0.0042
## 140 0.4356 nan 0.2000 -0.0023
## 160 0.4028 nan 0.2000 -0.0013
## 180 0.3745 nan 0.2000 -0.0018
## 200 0.3446 nan 0.2000 -0.0014
## 220 0.3171 nan 0.2000 -0.0006
## 240 0.2985 nan 0.2000 -0.0015
## 260 0.2780 nan 0.2000 -0.0021
## 280 0.2646 nan 0.2000 -0.0036
## 300 0.2524 nan 0.2000 -0.0016
## 320 0.2315 nan 0.2000 -0.0013
## 340 0.2177 nan 0.2000 -0.0011
## 360 0.2052 nan 0.2000 -0.0017
## 380 0.1927 nan 0.2000 -0.0008
## 400 0.1823 nan 0.2000 -0.0009
## 420 0.1727 nan 0.2000 -0.0008
## 440 0.1615 nan 0.2000 -0.0017
## 460 0.1524 nan 0.2000 -0.0010
## 480 0.1441 nan 0.2000 -0.0002
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1973 nan 0.2000 0.0374
## 2 1.1342 nan 0.2000 0.0216
## 3 1.0726 nan 0.2000 0.0266
## 4 1.0219 nan 0.2000 0.0176
## 5 0.9851 nan 0.2000 0.0133
## 6 0.9599 nan 0.2000 0.0059
## 7 0.9436 nan 0.2000 0.0015
## 8 0.9238 nan 0.2000 0.0059
## 9 0.9076 nan 0.2000 0.0039
## 10 0.8940 nan 0.2000 0.0002
## 20 0.8078 nan 0.2000 -0.0028
## 40 0.7087 nan 0.2000 -0.0050
## 60 0.6349 nan 0.2000 -0.0002
## 80 0.5840 nan 0.2000 -0.0024
## 100 0.5246 nan 0.2000 -0.0021
## 120 0.4741 nan 0.2000 0.0002
## 140 0.4265 nan 0.2000 -0.0002
## 160 0.3938 nan 0.2000 -0.0031
## 180 0.3655 nan 0.2000 -0.0017
## 200 0.3337 nan 0.2000 -0.0011
## 220 0.3120 nan 0.2000 -0.0028
## 240 0.2887 nan 0.2000 -0.0026
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## 280 0.2503 nan 0.2000 -0.0022
## 300 0.2319 nan 0.2000 -0.0014
## 320 0.2157 nan 0.2000 -0.0014
## 340 0.2033 nan 0.2000 -0.0011
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## 380 0.1792 nan 0.2000 -0.0001
## 400 0.1690 nan 0.2000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1980 nan 0.2000 0.0438
## 2 1.1159 nan 0.2000 0.0327
## 3 1.0701 nan 0.2000 0.0161
## 4 1.0349 nan 0.2000 0.0089
## 5 1.0032 nan 0.2000 0.0113
## 6 0.9724 nan 0.2000 0.0086
## 7 0.9471 nan 0.2000 0.0048
## 8 0.9276 nan 0.2000 0.0054
## 9 0.9098 nan 0.2000 -0.0021
## 10 0.8946 nan 0.2000 0.0001
## 20 0.8082 nan 0.2000 -0.0025
## 40 0.7109 nan 0.2000 -0.0004
## 60 0.6505 nan 0.2000 -0.0036
## 80 0.5905 nan 0.2000 -0.0039
## 100 0.5431 nan 0.2000 -0.0034
## 120 0.5073 nan 0.2000 -0.0042
## 140 0.4617 nan 0.2000 -0.0037
## 160 0.4223 nan 0.2000 -0.0018
## 180 0.3885 nan 0.2000 -0.0023
## 200 0.3555 nan 0.2000 -0.0010
## 220 0.3329 nan 0.2000 -0.0014
## 240 0.3055 nan 0.2000 -0.0015
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## 280 0.2634 nan 0.2000 -0.0009
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## 320 0.2263 nan 0.2000 -0.0013
## 340 0.2130 nan 0.2000 -0.0018
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## 380 0.1892 nan 0.2000 -0.0022
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2014 nan 0.3000 0.0427
## 2 1.1527 nan 0.3000 0.0196
## 3 1.1077 nan 0.3000 0.0213
## 4 1.0677 nan 0.3000 0.0185
## 5 1.0391 nan 0.3000 0.0122
## 6 1.0168 nan 0.3000 0.0066
## 7 0.9913 nan 0.3000 0.0101
## 8 0.9735 nan 0.3000 0.0068
## 9 0.9632 nan 0.3000 0.0003
## 10 0.9524 nan 0.3000 0.0001
## 20 0.8785 nan 0.3000 -0.0049
## 40 0.8227 nan 0.3000 -0.0021
## 60 0.7910 nan 0.3000 -0.0010
## 80 0.7696 nan 0.3000 -0.0007
## 100 0.7560 nan 0.3000 -0.0007
## 120 0.7431 nan 0.3000 -0.0012
## 140 0.7321 nan 0.3000 -0.0030
## 160 0.7209 nan 0.3000 -0.0005
## 180 0.7021 nan 0.3000 -0.0022
## 200 0.6910 nan 0.3000 -0.0012
## 220 0.6759 nan 0.3000 -0.0027
## 240 0.6627 nan 0.3000 -0.0004
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## 280 0.6396 nan 0.3000 -0.0033
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## 320 0.6234 nan 0.3000 -0.0020
## 340 0.6184 nan 0.3000 -0.0070
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## 380 0.6098 nan 0.3000 -0.0022
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## 440 0.5847 nan 0.3000 -0.0024
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1942 nan 0.3000 0.0384
## 2 1.1378 nan 0.3000 0.0221
## 3 1.0958 nan 0.3000 0.0189
## 4 1.0670 nan 0.3000 0.0098
## 5 1.0345 nan 0.3000 0.0129
## 6 1.0087 nan 0.3000 0.0081
## 7 0.9868 nan 0.3000 0.0061
## 8 0.9793 nan 0.3000 -0.0003
## 9 0.9580 nan 0.3000 0.0074
## 10 0.9477 nan 0.3000 0.0024
## 20 0.8764 nan 0.3000 -0.0017
## 40 0.8113 nan 0.3000 -0.0033
## 60 0.7853 nan 0.3000 0.0010
## 80 0.7684 nan 0.3000 -0.0043
## 100 0.7566 nan 0.3000 -0.0022
## 120 0.7462 nan 0.3000 -0.0037
## 140 0.7292 nan 0.3000 -0.0028
## 160 0.7196 nan 0.3000 -0.0045
## 180 0.7077 nan 0.3000 -0.0030
## 200 0.6941 nan 0.3000 -0.0001
## 220 0.6780 nan 0.3000 -0.0010
## 240 0.6697 nan 0.3000 -0.0005
## 260 0.6562 nan 0.3000 -0.0022
## 280 0.6496 nan 0.3000 -0.0024
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## 320 0.6353 nan 0.3000 -0.0037
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## 380 0.6118 nan 0.3000 -0.0005
## 400 0.6055 nan 0.3000 -0.0029
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## 460 0.5845 nan 0.3000 -0.0050
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2039 nan 0.3000 0.0466
## 2 1.1409 nan 0.3000 0.0273
## 3 1.1004 nan 0.3000 0.0182
## 4 1.0580 nan 0.3000 0.0200
## 5 1.0365 nan 0.3000 0.0089
## 6 1.0122 nan 0.3000 0.0093
## 7 0.9864 nan 0.3000 0.0081
## 8 0.9726 nan 0.3000 0.0032
## 9 0.9549 nan 0.3000 0.0083
## 10 0.9479 nan 0.3000 -0.0017
## 20 0.8834 nan 0.3000 0.0002
## 40 0.8213 nan 0.3000 -0.0004
## 60 0.7931 nan 0.3000 -0.0029
## 80 0.7794 nan 0.3000 -0.0059
## 100 0.7603 nan 0.3000 -0.0013
## 120 0.7458 nan 0.3000 -0.0036
## 140 0.7374 nan 0.3000 -0.0028
## 160 0.7200 nan 0.3000 -0.0035
## 180 0.7122 nan 0.3000 -0.0031
## 200 0.6913 nan 0.3000 -0.0042
## 220 0.6854 nan 0.3000 -0.0039
## 240 0.6721 nan 0.3000 -0.0038
## 260 0.6648 nan 0.3000 -0.0044
## 280 0.6560 nan 0.3000 -0.0018
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## 320 0.6349 nan 0.3000 -0.0024
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## 380 0.6085 nan 0.3000 -0.0048
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1735 nan 0.3000 0.0487
## 2 1.1025 nan 0.3000 0.0334
## 3 1.0471 nan 0.3000 0.0216
## 4 0.9992 nan 0.3000 0.0219
## 5 0.9824 nan 0.3000 -0.0011
## 6 0.9569 nan 0.3000 0.0027
## 7 0.9335 nan 0.3000 0.0082
## 8 0.9227 nan 0.3000 -0.0024
## 9 0.9058 nan 0.3000 0.0031
## 10 0.8973 nan 0.3000 -0.0012
## 20 0.8221 nan 0.3000 0.0023
## 40 0.7310 nan 0.3000 -0.0038
## 60 0.6742 nan 0.3000 -0.0064
## 80 0.6222 nan 0.3000 -0.0037
## 100 0.5735 nan 0.3000 -0.0006
## 120 0.5360 nan 0.3000 -0.0026
## 140 0.4928 nan 0.3000 -0.0036
## 160 0.4548 nan 0.3000 -0.0039
## 180 0.4236 nan 0.3000 -0.0007
## 200 0.3927 nan 0.3000 -0.0033
## 220 0.3649 nan 0.3000 -0.0019
## 240 0.3385 nan 0.3000 -0.0009
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## 280 0.2980 nan 0.3000 -0.0015
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1806 nan 0.3000 0.0540
## 2 1.0943 nan 0.3000 0.0415
## 3 1.0415 nan 0.3000 0.0183
## 4 1.0056 nan 0.3000 0.0034
## 5 0.9743 nan 0.3000 0.0119
## 6 0.9433 nan 0.3000 0.0104
## 7 0.9313 nan 0.3000 0.0004
## 8 0.9250 nan 0.3000 -0.0042
## 9 0.9116 nan 0.3000 -0.0006
## 10 0.8949 nan 0.3000 -0.0004
## 20 0.8240 nan 0.3000 -0.0055
## 40 0.7480 nan 0.3000 -0.0092
## 60 0.6894 nan 0.3000 -0.0029
## 80 0.6413 nan 0.3000 -0.0016
## 100 0.5886 nan 0.3000 -0.0027
## 120 0.5564 nan 0.3000 -0.0036
## 140 0.5301 nan 0.3000 -0.0016
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## 180 0.4679 nan 0.3000 -0.0056
## 200 0.4388 nan 0.3000 -0.0039
## 220 0.4181 nan 0.3000 -0.0023
## 240 0.3918 nan 0.3000 -0.0013
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## 280 0.3369 nan 0.3000 -0.0029
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1734 nan 0.3000 0.0553
## 2 1.1017 nan 0.3000 0.0350
## 3 1.0522 nan 0.3000 0.0091
## 4 1.0002 nan 0.3000 0.0149
## 5 0.9698 nan 0.3000 0.0093
## 6 0.9528 nan 0.3000 0.0049
## 7 0.9260 nan 0.3000 0.0091
## 8 0.9124 nan 0.3000 -0.0009
## 9 0.8956 nan 0.3000 0.0045
## 10 0.8851 nan 0.3000 -0.0000
## 20 0.8093 nan 0.3000 -0.0064
## 40 0.7341 nan 0.3000 -0.0065
## 60 0.6847 nan 0.3000 -0.0028
## 80 0.6497 nan 0.3000 -0.0067
## 100 0.6056 nan 0.3000 -0.0034
## 120 0.5614 nan 0.3000 -0.0035
## 140 0.5271 nan 0.3000 -0.0049
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## 220 0.4168 nan 0.3000 -0.0006
## 240 0.3937 nan 0.3000 -0.0030
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1534 nan 0.3000 0.0722
## 2 1.0635 nan 0.3000 0.0418
## 3 1.0078 nan 0.3000 0.0229
## 4 0.9575 nan 0.3000 0.0136
## 5 0.9286 nan 0.3000 0.0009
## 6 0.9129 nan 0.3000 -0.0032
## 7 0.8960 nan 0.3000 -0.0042
## 8 0.8708 nan 0.3000 0.0026
## 9 0.8571 nan 0.3000 -0.0003
## 10 0.8462 nan 0.3000 -0.0021
## 20 0.7633 nan 0.3000 -0.0061
## 40 0.6345 nan 0.3000 -0.0030
## 60 0.5607 nan 0.3000 -0.0026
## 80 0.4887 nan 0.3000 -0.0057
## 100 0.4339 nan 0.3000 -0.0031
## 120 0.3921 nan 0.3000 -0.0016
## 140 0.3573 nan 0.3000 -0.0019
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## 180 0.2806 nan 0.3000 -0.0039
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## 500 0.0720 nan 0.3000 -0.0005
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1470 nan 0.3000 0.0619
## 2 1.0599 nan 0.3000 0.0381
## 3 1.0159 nan 0.3000 0.0152
## 4 0.9688 nan 0.3000 0.0176
## 5 0.9412 nan 0.3000 0.0084
## 6 0.9272 nan 0.3000 -0.0047
## 7 0.9065 nan 0.3000 0.0043
## 8 0.8871 nan 0.3000 -0.0001
## 9 0.8724 nan 0.3000 -0.0048
## 10 0.8529 nan 0.3000 0.0004
## 20 0.7597 nan 0.3000 -0.0076
## 40 0.6567 nan 0.3000 -0.0035
## 60 0.5548 nan 0.3000 -0.0054
## 80 0.5311 nan 0.3000 -0.0057
## 100 0.4518 nan 0.3000 -0.0027
## 120 0.3985 nan 0.3000 -0.0058
## 140 0.3590 nan 0.3000 -0.0040
## 160 0.3174 nan 0.3000 -0.0022
## 180 0.2799 nan 0.3000 -0.0027
## 200 0.2535 nan 0.3000 -0.0024
## 220 0.2259 nan 0.3000 -0.0034
## 240 0.2074 nan 0.3000 -0.0014
## 260 0.1905 nan 0.3000 -0.0001
## 280 0.1753 nan 0.3000 -0.0023
## 300 0.1589 nan 0.3000 -0.0029
## 320 0.1449 nan 0.3000 -0.0010
## 340 0.1304 nan 0.3000 -0.0012
## 360 0.1201 nan 0.3000 -0.0005
## 380 0.1101 nan 0.3000 -0.0011
## 400 0.1014 nan 0.3000 -0.0015
## 420 0.0943 nan 0.3000 -0.0005
## 440 0.0854 nan 0.3000 -0.0006
## 460 0.0798 nan 0.3000 -0.0009
## 480 0.0737 nan 0.3000 -0.0006
## 500 0.0686 nan 0.3000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1532 nan 0.3000 0.0663
## 2 1.0668 nan 0.3000 0.0318
## 3 1.0164 nan 0.3000 0.0128
## 4 0.9844 nan 0.3000 0.0002
## 5 0.9469 nan 0.3000 0.0125
## 6 0.9205 nan 0.3000 0.0072
## 7 0.8955 nan 0.3000 0.0081
## 8 0.8802 nan 0.3000 -0.0007
## 9 0.8638 nan 0.3000 0.0008
## 10 0.8600 nan 0.3000 -0.0102
## 20 0.7777 nan 0.3000 -0.0053
## 40 0.6731 nan 0.3000 -0.0050
## 60 0.5818 nan 0.3000 -0.0018
## 80 0.5078 nan 0.3000 -0.0023
## 100 0.4466 nan 0.3000 -0.0059
## 120 0.3877 nan 0.3000 -0.0047
## 140 0.3484 nan 0.3000 -0.0037
## 160 0.3131 nan 0.3000 -0.0046
## 180 0.2761 nan 0.3000 -0.0035
## 200 0.2508 nan 0.3000 -0.0023
## 220 0.2246 nan 0.3000 -0.0011
## 240 0.2008 nan 0.3000 -0.0021
## 260 0.1836 nan 0.3000 -0.0026
## 280 0.1657 nan 0.3000 -0.0014
## 300 0.1537 nan 0.3000 -0.0018
## 320 0.1409 nan 0.3000 -0.0009
## 340 0.1275 nan 0.3000 -0.0017
## 360 0.1171 nan 0.3000 -0.0014
## 380 0.1058 nan 0.3000 -0.0002
## 400 0.0976 nan 0.3000 -0.0009
## 420 0.0898 nan 0.3000 -0.0004
## 440 0.0801 nan 0.3000 -0.0005
## 460 0.0747 nan 0.3000 -0.0006
## 480 0.0691 nan 0.3000 -0.0006
## 500 0.0642 nan 0.3000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1497 nan 0.5000 0.0571
## 2 1.0895 nan 0.5000 0.0193
## 3 1.0334 nan 0.5000 0.0318
## 4 0.9967 nan 0.5000 0.0170
## 5 0.9726 nan 0.5000 0.0044
## 6 0.9478 nan 0.5000 0.0060
## 7 0.9367 nan 0.5000 -0.0065
## 8 0.9273 nan 0.5000 -0.0004
## 9 0.9168 nan 0.5000 -0.0029
## 10 0.9092 nan 0.5000 -0.0005
## 20 0.8462 nan 0.5000 -0.0015
## 40 0.7823 nan 0.5000 -0.0033
## 60 0.7621 nan 0.5000 -0.0049
## 80 0.7272 nan 0.5000 -0.0013
## 100 0.7062 nan 0.5000 -0.0044
## 120 0.6907 nan 0.5000 -0.0043
## 140 0.6794 nan 0.5000 0.0003
## 160 0.6676 nan 0.5000 -0.0083
## 180 0.6617 nan 0.5000 -0.0125
## 200 0.6412 nan 0.5000 -0.0016
## 220 0.6262 nan 0.5000 -0.0025
## 240 0.6153 nan 0.5000 -0.0021
## 260 0.6061 nan 0.5000 -0.0052
## 280 0.5908 nan 0.5000 -0.0048
## 300 0.5844 nan 0.5000 -0.0023
## 320 0.5820 nan 0.5000 -0.0038
## 340 0.5778 nan 0.5000 -0.0055
## 360 0.5593 nan 0.5000 -0.0025
## 380 0.5577 nan 0.5000 -0.0021
## 400 0.5498 nan 0.5000 -0.0084
## 420 0.5421 nan 0.5000 -0.0053
## 440 0.5314 nan 0.5000 -0.0033
## 460 0.5296 nan 0.5000 -0.0093
## 480 0.5120 nan 0.5000 -0.0016
## 500 0.5021 nan 0.5000 -0.0016
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1659 nan 0.5000 0.0659
## 2 1.1016 nan 0.5000 0.0251
## 3 1.0403 nan 0.5000 0.0258
## 4 0.9887 nan 0.5000 0.0266
## 5 0.9740 nan 0.5000 -0.0011
## 6 0.9473 nan 0.5000 0.0066
## 7 0.9183 nan 0.5000 0.0046
## 8 0.9068 nan 0.5000 0.0039
## 9 0.8933 nan 0.5000 0.0023
## 10 0.8851 nan 0.5000 0.0018
## 20 0.8550 nan 0.5000 -0.0087
## 40 0.7986 nan 0.5000 -0.0128
## 60 0.7500 nan 0.5000 -0.0128
## 80 0.7297 nan 0.5000 -0.0017
## 100 0.7070 nan 0.5000 -0.0087
## 120 0.6803 nan 0.5000 -0.0062
## 140 0.6700 nan 0.5000 -0.0064
## 160 0.6540 nan 0.5000 -0.0057
## 180 0.6391 nan 0.5000 -0.0038
## 200 0.6268 nan 0.5000 -0.0029
## 220 0.6142 nan 0.5000 -0.0049
## 240 0.6039 nan 0.5000 0.0016
## 260 0.5881 nan 0.5000 -0.0027
## 280 0.5799 nan 0.5000 -0.0037
## 300 0.5709 nan 0.5000 -0.0054
## 320 0.5619 nan 0.5000 -0.0067
## 340 0.5520 nan 0.5000 -0.0016
## 360 0.5535 nan 0.5000 -0.0069
## 380 0.5412 nan 0.5000 -0.0050
## 400 0.5278 nan 0.5000 -0.0025
## 420 0.5198 nan 0.5000 -0.0030
## 440 0.5159 nan 0.5000 -0.0031
## 460 0.5075 nan 0.5000 -0.0021
## 480 0.5044 nan 0.5000 -0.0061
## 500 0.4995 nan 0.5000 -0.0038
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1771 nan 0.5000 0.0592
## 2 1.1010 nan 0.5000 0.0233
## 3 1.0271 nan 0.5000 0.0245
## 4 0.9846 nan 0.5000 0.0139
## 5 0.9739 nan 0.5000 -0.0036
## 6 0.9569 nan 0.5000 0.0051
## 7 0.9468 nan 0.5000 -0.0026
## 8 0.9469 nan 0.5000 -0.0105
## 9 0.9227 nan 0.5000 0.0058
## 10 0.9115 nan 0.5000 0.0023
## 20 0.8495 nan 0.5000 -0.0110
## 40 0.8056 nan 0.5000 -0.0004
## 60 0.7712 nan 0.5000 -0.0049
## 80 0.7529 nan 0.5000 -0.0020
## 100 0.7276 nan 0.5000 -0.0035
## 120 0.7132 nan 0.5000 -0.0035
## 140 0.7037 nan 0.5000 -0.0076
## 160 0.6783 nan 0.5000 -0.0029
## 180 0.6678 nan 0.5000 -0.0078
## 200 0.6601 nan 0.5000 -0.0044
## 220 0.6519 nan 0.5000 -0.0016
## 240 0.6439 nan 0.5000 -0.0015
## 260 0.6238 nan 0.5000 -0.0024
## 280 0.6013 nan 0.5000 -0.0050
## 300 0.5889 nan 0.5000 -0.0026
## 320 0.5803 nan 0.5000 -0.0060
## 340 0.5690 nan 0.5000 -0.0126
## 360 0.5521 nan 0.5000 -0.0049
## 380 0.5447 nan 0.5000 -0.0017
## 400 0.5413 nan 0.5000 -0.0075
## 420 0.5410 nan 0.5000 -0.0132
## 440 0.5287 nan 0.5000 -0.0058
## 460 0.5167 nan 0.5000 -0.0027
## 480 0.5080 nan 0.5000 -0.0005
## 500 0.5025 nan 0.5000 -0.0027
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1235 nan 0.5000 0.0776
## 2 1.0245 nan 0.5000 0.0444
## 3 0.9753 nan 0.5000 0.0142
## 4 0.9380 nan 0.5000 0.0146
## 5 0.9337 nan 0.5000 -0.0087
## 6 0.9141 nan 0.5000 -0.0006
## 7 0.8974 nan 0.5000 -0.0029
## 8 0.8792 nan 0.5000 -0.0105
## 9 0.8652 nan 0.5000 -0.0009
## 10 0.8566 nan 0.5000 -0.0024
## 20 0.7918 nan 0.5000 -0.0096
## 40 0.7138 nan 0.5000 -0.0084
## 60 0.6579 nan 0.5000 -0.0082
## 80 0.5877 nan 0.5000 -0.0084
## 100 0.5498 nan 0.5000 -0.0117
## 120 0.4692 nan 0.5000 -0.0029
## 140 0.4318 nan 0.5000 -0.0034
## 160 inf nan 0.5000 -0.0008
## 180 inf nan 0.5000 -0.0052
## 200 inf nan 0.5000 -0.0046
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 -0.0057
## 280 inf nan 0.5000 -0.0016
## 300 inf nan 0.5000 -0.0012
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 -0.0012
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 -0.0021
## 440 inf nan 0.5000 -0.0009
## 460 inf nan 0.5000 nan
## 480 inf nan 0.5000 nan
## 500 inf nan 0.5000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1238 nan 0.5000 0.0748
## 2 1.0589 nan 0.5000 0.0066
## 3 0.9995 nan 0.5000 0.0266
## 4 0.9521 nan 0.5000 0.0095
## 5 0.9168 nan 0.5000 0.0115
## 6 0.9001 nan 0.5000 -0.0010
## 7 0.8913 nan 0.5000 -0.0079
## 8 0.8801 nan 0.5000 -0.0012
## 9 0.8732 nan 0.5000 -0.0089
## 10 0.8631 nan 0.5000 -0.0077
## 20 0.8020 nan 0.5000 -0.0122
## 40 0.6974 nan 0.5000 -0.0071
## 60 0.6182 nan 0.5000 -0.0063
## 80 0.5483 nan 0.5000 -0.0021
## 100 0.4799 nan 0.5000 -0.0099
## 120 0.4298 nan 0.5000 -0.0026
## 140 0.4028 nan 0.5000 -0.0081
## 160 inf nan 0.5000 -0.0019
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1236 nan 0.5000 0.0824
## 2 1.0211 nan 0.5000 0.0478
## 3 0.9817 nan 0.5000 0.0120
## 4 0.9320 nan 0.5000 0.0153
## 5 0.9097 nan 0.5000 0.0033
## 6 0.8927 nan 0.5000 -0.0021
## 7 0.8859 nan 0.5000 -0.0091
## 8 0.8770 nan 0.5000 -0.0141
## 9 0.8653 nan 0.5000 -0.0004
## 10 0.8544 nan 0.5000 -0.0090
## 20 0.7789 nan 0.5000 -0.0109
## 40 0.6959 nan 0.5000 -0.0043
## 60 0.6279 nan 0.5000 -0.0117
## 80 0.5824 nan 0.5000 -0.0103
## 100 0.5372 nan 0.5000 -0.0089
## 120 0.4897 nan 0.5000 -0.0033
## 140 0.4295 nan 0.5000 -0.0002
## 160 0.3777 nan 0.5000 -0.0026
## 180 0.3501 nan 0.5000 -0.0075
## 200 0.3174 nan 0.5000 -0.0044
## 220 0.2909 nan 0.5000 -0.0049
## 240 1.9869 nan 0.5000 -0.0035
## 260 1.9932 nan 0.5000 -0.0035
## 280 1.9436 nan 0.5000 -0.0022
## 300 1.9344 nan 0.5000 -0.0011
## 320 1.8872 nan 0.5000 -0.0010
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0884 nan 0.5000 0.0660
## 2 1.0019 nan 0.5000 0.0262
## 3 0.9721 nan 0.5000 -0.0079
## 4 0.9212 nan 0.5000 0.0050
## 5 0.9031 nan 0.5000 -0.0069
## 6 0.8769 nan 0.5000 -0.0053
## 7 0.8533 nan 0.5000 -0.0010
## 8 0.8525 nan 0.5000 -0.0241
## 9 0.8336 nan 0.5000 -0.0070
## 10 0.8225 nan 0.5000 -0.0163
## 20 0.7443 nan 0.5000 -0.0043
## 40 0.5889 nan 0.5000 -0.0129
## 60 0.4409 nan 0.5000 -0.0051
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
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## 140 inf nan 0.5000 nan
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## 280 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0853 nan 0.5000 0.0958
## 2 0.9787 nan 0.5000 0.0530
## 3 0.9329 nan 0.5000 0.0085
## 4 0.9057 nan 0.5000 -0.0025
## 5 0.8761 nan 0.5000 0.0033
## 6 0.8642 nan 0.5000 -0.0130
## 7 0.8447 nan 0.5000 -0.0041
## 8 0.8332 nan 0.5000 -0.0121
## 9 0.8121 nan 0.5000 -0.0173
## 10 0.8020 nan 0.5000 -0.0111
## 20 0.6941 nan 0.5000 -0.0071
## 40 0.5532 nan 0.5000 -0.0116
## 60 0.4491 nan 0.5000 -0.0051
## 80 0.3712 nan 0.5000 -0.0089
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0800 nan 0.5000 0.0844
## 2 1.0056 nan 0.5000 0.0152
## 3 0.9650 nan 0.5000 0.0035
## 4 0.9345 nan 0.5000 -0.0041
## 5 0.9169 nan 0.5000 -0.0094
## 6 0.9172 nan 0.5000 -0.0288
## 7 0.8965 nan 0.5000 -0.0084
## 8 0.8691 nan 0.5000 0.0023
## 9 0.8599 nan 0.5000 -0.0162
## 10 0.8403 nan 0.5000 -0.0033
## 20 0.7463 nan 0.5000 -0.0200
## 40 0.6019 nan 0.5000 -0.0032
## 60 0.4912 nan 0.5000 -0.0049
## 80 0.3913 nan 0.5000 -0.0020
## 100 0.3315 nan 0.5000 -0.0027
## 120 0.2897 nan 0.5000 -0.0032
## 140 0.2438 nan 0.5000 -0.0035
## 160 0.1947 nan 0.5000 -0.0019
## 180 0.1599 nan 0.5000 -0.0017
## 200 0.1371 nan 0.5000 -0.0007
## 220 0.1165 nan 0.5000 -0.0010
## 240 0.0999 nan 0.5000 -0.0017
## 260 0.0865 nan 0.5000 -0.0009
## 280 0.0709 nan 0.5000 0.0008
## 300 0.0623 nan 0.5000 -0.0010
## 320 0.0564 nan 0.5000 -0.0008
## 340 0.0482 nan 0.5000 -0.0010
## 360 0.0424 nan 0.5000 -0.0010
## 380 0.0367 nan 0.5000 -0.0008
## 400 0.0324 nan 0.5000 -0.0006
## 420 0.0295 nan 0.5000 -0.0004
## 440 0.0260 nan 0.5000 -0.0009
## 460 0.0232 nan 0.5000 -0.0000
## 480 0.0212 nan 0.5000 -0.0004
## 500 0.0194 nan 0.5000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1354 nan 1.0000 0.0739
## 2 1.0544 nan 1.0000 0.0357
## 3 0.9959 nan 1.0000 0.0238
## 4 0.9879 nan 1.0000 -0.0094
## 5 0.9635 nan 1.0000 0.0101
## 6 0.9562 nan 1.0000 -0.0103
## 7 0.9331 nan 1.0000 0.0084
## 8 0.9204 nan 1.0000 -0.0021
## 9 0.9269 nan 1.0000 -0.0221
## 10 0.9360 nan 1.0000 -0.0338
## 20 0.9084 nan 1.0000 -0.0034
## 40 0.9427 nan 1.0000 -0.0077
## 60 2307022.2009 nan 1.0000 0.0009
## 80 2307022.1771 nan 1.0000 -0.0247
## 100 2307055.8115 nan 1.0000 0.0008
## 120 2307155.3230 nan 1.0000 -0.0017
## 140 2307155.2661 nan 1.0000 -0.0026
## 160 2307155.2807 nan 1.0000 -0.0041
## 180 2307155.2784 nan 1.0000 0.0000
## 200 2307155.2584 nan 1.0000 -0.0072
## 220 2307155.7183 nan 1.0000 0.0017
## 240 2307155.6843 nan 1.0000 0.0009
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1281 nan 1.0000 0.0779
## 2 1.0602 nan 1.0000 0.0182
## 3 1.0046 nan 1.0000 0.0142
## 4 0.9746 nan 1.0000 0.0066
## 5 0.9609 nan 1.0000 -0.0076
## 6 0.9620 nan 1.0000 -0.0159
## 7 0.9634 nan 1.0000 -0.0234
## 8 0.9311 nan 1.0000 0.0118
## 9 0.9164 nan 1.0000 0.0028
## 10 0.9127 nan 1.0000 -0.0098
## 20 0.9192 nan 1.0000 -0.0072
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
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## 140 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1282 nan 1.0000 0.0610
## 2 1.0372 nan 1.0000 0.0307
## 3 1.0225 nan 1.0000 -0.0056
## 4 0.9719 nan 1.0000 0.0260
## 5 0.9557 nan 1.0000 -0.0047
## 6 0.9476 nan 1.0000 -0.0060
## 7 0.9537 nan 1.0000 -0.0238
## 8 0.9562 nan 1.0000 -0.0258
## 9 0.9593 nan 1.0000 -0.0280
## 10 0.9229 nan 1.0000 0.0136
## 20 0.9107 nan 1.0000 -0.0148
## 40 0.8964 nan 1.0000 -0.0430
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0806 nan 1.0000 0.0628
## 2 0.9996 nan 1.0000 0.0217
## 3 0.9709 nan 1.0000 0.0006
## 4 0.9704 nan 1.0000 -0.0296
## 5 0.9993 nan 1.0000 -0.0696
## 6 0.9720 nan 1.0000 -0.0101
## 7 0.9780 nan 1.0000 -0.0331
## 8 0.9477 nan 1.0000 0.0013
## 9 0.9227 nan 1.0000 0.0015
## 10 0.9276 nan 1.0000 -0.0309
## 20 0.9003 nan 1.0000 -0.0325
## 40 0.8617 nan 1.0000 -0.0748
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 -0.0345
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 -0.0068
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 -0.0006
## 220 inf nan 1.0000 -0.0022
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 -0.0042
## 280 inf nan 1.0000 -0.0038
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 0.0015
## 360 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0774 nan 1.0000 0.0953
## 2 1.0034 nan 1.0000 0.0170
## 3 0.9184 nan 1.0000 0.0292
## 4 0.8923 nan 1.0000 -0.0126
## 5 0.8933 nan 1.0000 -0.0395
## 6 0.8888 nan 1.0000 -0.0206
## 7 0.8769 nan 1.0000 -0.0124
## 8 0.8616 nan 1.0000 -0.0039
## 9 0.8576 nan 1.0000 -0.0238
## 10 0.8707 nan 1.0000 -0.0435
## 20 0.7942 nan 1.0000 -0.0376
## 40 24016620229.5012 nan 1.0000 -0.0170
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
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## 400 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0751 nan 1.0000 0.1100
## 2 0.9957 nan 1.0000 -0.0013
## 3 0.9605 nan 1.0000 -0.0096
## 4 0.9749 nan 1.0000 -0.0398
## 5 0.9327 nan 1.0000 -0.0062
## 6 0.8872 nan 1.0000 0.0164
## 7 0.9750 nan 1.0000 -0.1076
## 8 1.0760 nan 1.0000 -0.1346
## 9 4.0625 nan 1.0000 -2.0685
## 10 4.0406 nan 1.0000 -0.0278
## 20 3.9680 nan 1.0000 -0.0084
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0529 nan 1.0000 0.0872
## 2 0.9814 nan 1.0000 -0.0033
## 3 0.9303 nan 1.0000 0.0042
## 4 0.9235 nan 1.0000 -0.0255
## 5 0.9294 nan 1.0000 -0.0417
## 6 0.8995 nan 1.0000 -0.0244
## 7 0.8918 nan 1.0000 -0.0339
## 8 0.8658 nan 1.0000 -0.0199
## 9 0.8923 nan 1.0000 -0.0706
## 10 0.9284 nan 1.0000 -0.0874
## 20 24.1232 nan 1.0000 -1.4419
## 40 24.7232 nan 1.0000 0.0000
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0633 nan 1.0000 0.0573
## 2 0.9747 nan 1.0000 0.0243
## 3 0.9786 nan 1.0000 -0.0437
## 4 0.9453 nan 1.0000 -0.0155
## 5 0.9071 nan 1.0000 -0.0120
## 6 0.9065 nan 1.0000 -0.0390
## 7 0.9082 nan 1.0000 -0.0459
## 8 0.9102 nan 1.0000 -0.0434
## 9 1.0771 nan 1.0000 -0.1037
## 10 1.0580 nan 1.0000 -0.0083
## 20 89690508917.0235 nan 1.0000 -0.0270
## 40 89690508919.7059 nan 1.0000 -0.0335
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0575 nan 1.0000 0.0947
## 2 0.9709 nan 1.0000 -0.0034
## 3 0.9863 nan 1.0000 -0.0413
## 4 0.9464 nan 1.0000 0.0106
## 5 0.9325 nan 1.0000 -0.0124
## 6 0.9326 nan 1.0000 -0.0557
## 7 0.9017 nan 1.0000 0.0006
## 8 0.9761 nan 1.0000 -0.1186
## 9 0.9445 nan 1.0000 -0.0308
## 10 0.9521 nan 1.0000 -0.0562
## 20 2.5666 nan 1.0000 -0.0209
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
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## 3 1.2934 nan 0.0000 0.0000
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## 40 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
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## 120 1.2934 nan 0.0000 0.0000
## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 320 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
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## 180 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
## 60 1.2934 nan 0.0000 0.0000
## 80 1.2934 nan 0.0000 0.0000
## 100 1.2934 nan 0.0000 0.0000
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## 140 1.2934 nan 0.0000 0.0000
## 160 1.2934 nan 0.0000 0.0000
## 180 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
## 400 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
## 4 1.2934 nan 0.0000 0.0000
## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
## 8 1.2934 nan 0.0000 0.0000
## 9 1.2934 nan 0.0000 0.0000
## 10 1.2934 nan 0.0000 0.0000
## 20 1.2934 nan 0.0000 0.0000
## 40 1.2934 nan 0.0000 0.0000
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## 380 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2934 nan 0.0000 0.0000
## 2 1.2934 nan 0.0000 0.0000
## 3 1.2934 nan 0.0000 0.0000
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## 5 1.2934 nan 0.0000 0.0000
## 6 1.2934 nan 0.0000 0.0000
## 7 1.2934 nan 0.0000 0.0000
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2923 nan 0.0010 0.0001
## 4 1.2920 nan 0.0010 0.0002
## 5 1.2916 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2909 nan 0.0010 0.0002
## 8 1.2905 nan 0.0010 0.0002
## 9 1.2902 nan 0.0010 0.0002
## 10 1.2898 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2792 nan 0.0010 0.0002
## 60 1.2725 nan 0.0010 0.0002
## 80 1.2659 nan 0.0010 0.0001
## 100 1.2595 nan 0.0010 0.0001
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## 140 1.2473 nan 0.0010 0.0001
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## 180 1.2359 nan 0.0010 0.0001
## 200 1.2304 nan 0.0010 0.0001
## 220 1.2251 nan 0.0010 0.0001
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## 320 1.2012 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2919 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2911 nan 0.0010 0.0002
## 7 1.2907 nan 0.0010 0.0002
## 8 1.2904 nan 0.0010 0.0002
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## 10 1.2896 nan 0.0010 0.0002
## 20 1.2860 nan 0.0010 0.0001
## 40 1.2790 nan 0.0010 0.0002
## 60 1.2724 nan 0.0010 0.0001
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## 100 1.2595 nan 0.0010 0.0001
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## 140 1.2475 nan 0.0010 0.0001
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## 180 1.2364 nan 0.0010 0.0001
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## 220 1.2257 nan 0.0010 0.0001
## 240 1.2206 nan 0.0010 0.0001
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## 280 1.2109 nan 0.0010 0.0001
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## 320 1.2018 nan 0.0010 0.0001
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2930 nan 0.0010 0.0002
## 2 1.2926 nan 0.0010 0.0002
## 3 1.2922 nan 0.0010 0.0002
## 4 1.2918 nan 0.0010 0.0002
## 5 1.2915 nan 0.0010 0.0002
## 6 1.2912 nan 0.0010 0.0002
## 7 1.2908 nan 0.0010 0.0002
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## 10 1.2897 nan 0.0010 0.0002
## 20 1.2861 nan 0.0010 0.0002
## 40 1.2790 nan 0.0010 0.0002
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## 140 1.2475 nan 0.0010 0.0001
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## 300 1.2060 nan 0.0010 0.0001
## 320 1.2016 nan 0.0010 0.0001
## 340 1.1972 nan 0.0010 0.0001
## 360 1.1929 nan 0.0010 0.0001
## 380 1.1887 nan 0.0010 0.0001
## 400 1.1846 nan 0.0010 0.0001
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## 440 1.1768 nan 0.0010 0.0001
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## 480 1.1691 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2901 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
## 9 1.2891 nan 0.0010 0.0002
## 10 1.2886 nan 0.0010 0.0002
## 20 1.2838 nan 0.0010 0.0002
## 40 1.2747 nan 0.0010 0.0002
## 60 1.2660 nan 0.0010 0.0002
## 80 1.2575 nan 0.0010 0.0002
## 100 1.2492 nan 0.0010 0.0002
## 120 1.2411 nan 0.0010 0.0002
## 140 1.2334 nan 0.0010 0.0002
## 160 1.2260 nan 0.0010 0.0001
## 180 1.2186 nan 0.0010 0.0002
## 200 1.2113 nan 0.0010 0.0002
## 220 1.2043 nan 0.0010 0.0002
## 240 1.1977 nan 0.0010 0.0002
## 260 1.1915 nan 0.0010 0.0001
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## 320 1.1728 nan 0.0010 0.0001
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## 380 1.1557 nan 0.0010 0.0001
## 400 1.1503 nan 0.0010 0.0001
## 420 1.1451 nan 0.0010 0.0001
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## 460 1.1349 nan 0.0010 0.0001
## 480 1.1301 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2924 nan 0.0010 0.0002
## 3 1.2919 nan 0.0010 0.0002
## 4 1.2914 nan 0.0010 0.0002
## 5 1.2909 nan 0.0010 0.0002
## 6 1.2904 nan 0.0010 0.0002
## 7 1.2899 nan 0.0010 0.0002
## 8 1.2894 nan 0.0010 0.0002
## 9 1.2889 nan 0.0010 0.0002
## 10 1.2885 nan 0.0010 0.0002
## 20 1.2839 nan 0.0010 0.0002
## 40 1.2747 nan 0.0010 0.0002
## 60 1.2660 nan 0.0010 0.0002
## 80 1.2576 nan 0.0010 0.0002
## 100 1.2492 nan 0.0010 0.0002
## 120 1.2413 nan 0.0010 0.0002
## 140 1.2337 nan 0.0010 0.0001
## 160 1.2265 nan 0.0010 0.0002
## 180 1.2192 nan 0.0010 0.0002
## 200 1.2121 nan 0.0010 0.0002
## 220 1.2052 nan 0.0010 0.0001
## 240 1.1987 nan 0.0010 0.0001
## 260 1.1921 nan 0.0010 0.0001
## 280 1.1857 nan 0.0010 0.0001
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## 380 1.1566 nan 0.0010 0.0001
## 400 1.1511 nan 0.0010 0.0001
## 420 1.1459 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2929 nan 0.0010 0.0002
## 2 1.2925 nan 0.0010 0.0002
## 3 1.2920 nan 0.0010 0.0002
## 4 1.2915 nan 0.0010 0.0002
## 5 1.2910 nan 0.0010 0.0002
## 6 1.2905 nan 0.0010 0.0002
## 7 1.2900 nan 0.0010 0.0002
## 8 1.2896 nan 0.0010 0.0002
## 9 1.2891 nan 0.0010 0.0002
## 10 1.2886 nan 0.0010 0.0002
## 20 1.2840 nan 0.0010 0.0002
## 40 1.2748 nan 0.0010 0.0002
## 60 1.2658 nan 0.0010 0.0002
## 80 1.2573 nan 0.0010 0.0002
## 100 1.2493 nan 0.0010 0.0002
## 120 1.2415 nan 0.0010 0.0002
## 140 1.2339 nan 0.0010 0.0002
## 160 1.2265 nan 0.0010 0.0002
## 180 1.2191 nan 0.0010 0.0001
## 200 1.2119 nan 0.0010 0.0002
## 220 1.2049 nan 0.0010 0.0002
## 240 1.1982 nan 0.0010 0.0001
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## 280 1.1852 nan 0.0010 0.0001
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## 320 1.1730 nan 0.0010 0.0001
## 340 1.1671 nan 0.0010 0.0001
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## 380 1.1559 nan 0.0010 0.0001
## 400 1.1504 nan 0.0010 0.0001
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## 460 1.1350 nan 0.0010 0.0001
## 480 1.1301 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2913 nan 0.0010 0.0003
## 5 1.2907 nan 0.0010 0.0003
## 6 1.2902 nan 0.0010 0.0002
## 7 1.2897 nan 0.0010 0.0002
## 8 1.2892 nan 0.0010 0.0003
## 9 1.2887 nan 0.0010 0.0002
## 10 1.2882 nan 0.0010 0.0002
## 20 1.2830 nan 0.0010 0.0002
## 40 1.2723 nan 0.0010 0.0002
## 60 1.2623 nan 0.0010 0.0002
## 80 1.2523 nan 0.0010 0.0002
## 100 1.2427 nan 0.0010 0.0002
## 120 1.2335 nan 0.0010 0.0002
## 140 1.2245 nan 0.0010 0.0002
## 160 1.2160 nan 0.0010 0.0002
## 180 1.2077 nan 0.0010 0.0002
## 200 1.1995 nan 0.0010 0.0002
## 220 1.1913 nan 0.0010 0.0002
## 240 1.1837 nan 0.0010 0.0002
## 260 1.1762 nan 0.0010 0.0001
## 280 1.1690 nan 0.0010 0.0002
## 300 1.1619 nan 0.0010 0.0001
## 320 1.1549 nan 0.0010 0.0002
## 340 1.1483 nan 0.0010 0.0001
## 360 1.1420 nan 0.0010 0.0001
## 380 1.1356 nan 0.0010 0.0001
## 400 1.1295 nan 0.0010 0.0001
## 420 1.1234 nan 0.0010 0.0001
## 440 1.1175 nan 0.0010 0.0001
## 460 1.1118 nan 0.0010 0.0001
## 480 1.1062 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2922 nan 0.0010 0.0002
## 3 1.2916 nan 0.0010 0.0002
## 4 1.2911 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2900 nan 0.0010 0.0003
## 7 1.2895 nan 0.0010 0.0002
## 8 1.2889 nan 0.0010 0.0003
## 9 1.2884 nan 0.0010 0.0003
## 10 1.2879 nan 0.0010 0.0002
## 20 1.2827 nan 0.0010 0.0003
## 40 1.2723 nan 0.0010 0.0002
## 60 1.2620 nan 0.0010 0.0002
## 80 1.2521 nan 0.0010 0.0002
## 100 1.2427 nan 0.0010 0.0002
## 120 1.2331 nan 0.0010 0.0002
## 140 1.2242 nan 0.0010 0.0002
## 160 1.2156 nan 0.0010 0.0002
## 180 1.2074 nan 0.0010 0.0002
## 200 1.1994 nan 0.0010 0.0002
## 220 1.1915 nan 0.0010 0.0002
## 240 1.1837 nan 0.0010 0.0002
## 260 1.1763 nan 0.0010 0.0002
## 280 1.1689 nan 0.0010 0.0001
## 300 1.1617 nan 0.0010 0.0002
## 320 1.1550 nan 0.0010 0.0001
## 340 1.1483 nan 0.0010 0.0001
## 360 1.1419 nan 0.0010 0.0001
## 380 1.1357 nan 0.0010 0.0002
## 400 1.1295 nan 0.0010 0.0001
## 420 1.1233 nan 0.0010 0.0002
## 440 1.1174 nan 0.0010 0.0001
## 460 1.1117 nan 0.0010 0.0001
## 480 1.1063 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2928 nan 0.0010 0.0002
## 2 1.2923 nan 0.0010 0.0002
## 3 1.2918 nan 0.0010 0.0002
## 4 1.2912 nan 0.0010 0.0002
## 5 1.2906 nan 0.0010 0.0002
## 6 1.2901 nan 0.0010 0.0002
## 7 1.2896 nan 0.0010 0.0002
## 8 1.2891 nan 0.0010 0.0003
## 9 1.2885 nan 0.0010 0.0003
## 10 1.2879 nan 0.0010 0.0002
## 20 1.2826 nan 0.0010 0.0002
## 40 1.2721 nan 0.0010 0.0002
## 60 1.2621 nan 0.0010 0.0002
## 80 1.2521 nan 0.0010 0.0002
## 100 1.2429 nan 0.0010 0.0002
## 120 1.2337 nan 0.0010 0.0002
## 140 1.2247 nan 0.0010 0.0002
## 160 1.2162 nan 0.0010 0.0002
## 180 1.2079 nan 0.0010 0.0002
## 200 1.2000 nan 0.0010 0.0002
## 220 1.1922 nan 0.0010 0.0002
## 240 1.1847 nan 0.0010 0.0001
## 260 1.1771 nan 0.0010 0.0002
## 280 1.1699 nan 0.0010 0.0001
## 300 1.1626 nan 0.0010 0.0001
## 320 1.1556 nan 0.0010 0.0001
## 340 1.1489 nan 0.0010 0.0002
## 360 1.1421 nan 0.0010 0.0002
## 380 1.1359 nan 0.0010 0.0001
## 400 1.1296 nan 0.0010 0.0001
## 420 1.1234 nan 0.0010 0.0001
## 440 1.1177 nan 0.0010 0.0001
## 460 1.1119 nan 0.0010 0.0001
## 480 1.1064 nan 0.0010 0.0001
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2587 nan 0.1000 0.0140
## 2 1.2296 nan 0.1000 0.0143
## 3 1.2064 nan 0.1000 0.0111
## 4 1.1820 nan 0.1000 0.0104
## 5 1.1639 nan 0.1000 0.0076
## 6 1.1468 nan 0.1000 0.0062
## 7 1.1302 nan 0.1000 0.0076
## 8 1.1160 nan 0.1000 0.0061
## 9 1.1017 nan 0.1000 0.0052
## 10 1.0871 nan 0.1000 0.0065
## 20 1.0044 nan 0.1000 0.0018
## 40 0.9155 nan 0.1000 -0.0001
## 60 0.8719 nan 0.1000 0.0003
## 80 0.8425 nan 0.1000 -0.0016
## 100 0.8239 nan 0.1000 -0.0000
## 120 0.8083 nan 0.1000 -0.0003
## 140 0.7958 nan 0.1000 -0.0008
## 160 0.7858 nan 0.1000 -0.0019
## 180 0.7772 nan 0.1000 -0.0001
## 200 0.7685 nan 0.1000 -0.0008
## 220 0.7594 nan 0.1000 -0.0010
## 240 0.7497 nan 0.1000 -0.0011
## 260 0.7438 nan 0.1000 -0.0005
## 280 0.7362 nan 0.1000 -0.0009
## 300 0.7296 nan 0.1000 -0.0007
## 320 0.7237 nan 0.1000 -0.0004
## 340 0.7185 nan 0.1000 -0.0010
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## 380 0.7075 nan 0.1000 -0.0007
## 400 0.7032 nan 0.1000 -0.0009
## 420 0.6984 nan 0.1000 -0.0005
## 440 0.6936 nan 0.1000 -0.0004
## 460 0.6895 nan 0.1000 -0.0001
## 480 0.6846 nan 0.1000 -0.0011
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2567 nan 0.1000 0.0161
## 2 1.2299 nan 0.1000 0.0144
## 3 1.2047 nan 0.1000 0.0108
## 4 1.1847 nan 0.1000 0.0100
## 5 1.1645 nan 0.1000 0.0084
## 6 1.1459 nan 0.1000 0.0075
## 7 1.1302 nan 0.1000 0.0052
## 8 1.1169 nan 0.1000 0.0054
## 9 1.1042 nan 0.1000 0.0043
## 10 1.0910 nan 0.1000 0.0057
## 20 1.0034 nan 0.1000 0.0021
## 40 0.9158 nan 0.1000 0.0001
## 60 0.8726 nan 0.1000 -0.0004
## 80 0.8446 nan 0.1000 -0.0013
## 100 0.8277 nan 0.1000 -0.0015
## 120 0.8149 nan 0.1000 -0.0005
## 140 0.8009 nan 0.1000 -0.0014
## 160 0.7913 nan 0.1000 -0.0007
## 180 0.7832 nan 0.1000 -0.0011
## 200 0.7754 nan 0.1000 -0.0007
## 220 0.7679 nan 0.1000 -0.0010
## 240 0.7625 nan 0.1000 -0.0006
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## 280 0.7419 nan 0.1000 -0.0008
## 300 0.7343 nan 0.1000 -0.0005
## 320 0.7286 nan 0.1000 -0.0005
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## 360 0.7205 nan 0.1000 -0.0007
## 380 0.7155 nan 0.1000 -0.0003
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## 460 0.6974 nan 0.1000 -0.0020
## 480 0.6927 nan 0.1000 -0.0008
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2580 nan 0.1000 0.0153
## 2 1.2295 nan 0.1000 0.0130
## 3 1.2030 nan 0.1000 0.0117
## 4 1.1828 nan 0.1000 0.0088
## 5 1.1610 nan 0.1000 0.0085
## 6 1.1432 nan 0.1000 0.0050
## 7 1.1268 nan 0.1000 0.0065
## 8 1.1149 nan 0.1000 0.0050
## 9 1.0991 nan 0.1000 0.0055
## 10 1.0866 nan 0.1000 0.0058
## 20 1.0016 nan 0.1000 0.0022
## 40 0.9177 nan 0.1000 -0.0004
## 60 0.8749 nan 0.1000 -0.0003
## 80 0.8437 nan 0.1000 0.0004
## 100 0.8248 nan 0.1000 0.0000
## 120 0.8090 nan 0.1000 -0.0001
## 140 0.7953 nan 0.1000 -0.0008
## 160 0.7845 nan 0.1000 -0.0010
## 180 0.7750 nan 0.1000 -0.0014
## 200 0.7663 nan 0.1000 -0.0013
## 220 0.7580 nan 0.1000 -0.0005
## 240 0.7509 nan 0.1000 -0.0005
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## 280 0.7391 nan 0.1000 -0.0004
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## 320 0.7276 nan 0.1000 -0.0011
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## 380 0.7106 nan 0.1000 -0.0011
## 400 0.7065 nan 0.1000 -0.0014
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## 460 0.6932 nan 0.1000 -0.0011
## 480 0.6873 nan 0.1000 -0.0008
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2439 nan 0.1000 0.0204
## 2 1.2045 nan 0.1000 0.0167
## 3 1.1711 nan 0.1000 0.0140
## 4 1.1435 nan 0.1000 0.0098
## 5 1.1168 nan 0.1000 0.0113
## 6 1.0968 nan 0.1000 0.0077
## 7 1.0778 nan 0.1000 0.0071
## 8 1.0653 nan 0.1000 0.0031
## 9 1.0484 nan 0.1000 0.0058
## 10 1.0352 nan 0.1000 0.0045
## 20 0.9320 nan 0.1000 0.0014
## 40 0.8420 nan 0.1000 0.0003
## 60 0.7936 nan 0.1000 -0.0002
## 80 0.7567 nan 0.1000 -0.0016
## 100 0.7290 nan 0.1000 -0.0016
## 120 0.7029 nan 0.1000 -0.0009
## 140 0.6767 nan 0.1000 -0.0007
## 160 0.6534 nan 0.1000 -0.0014
## 180 0.6347 nan 0.1000 -0.0016
## 200 0.6173 nan 0.1000 -0.0006
## 220 0.5993 nan 0.1000 -0.0005
## 240 0.5813 nan 0.1000 -0.0009
## 260 0.5659 nan 0.1000 0.0000
## 280 0.5538 nan 0.1000 -0.0003
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## 320 0.5224 nan 0.1000 -0.0005
## 340 0.5111 nan 0.1000 -0.0011
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## 380 0.4849 nan 0.1000 -0.0013
## 400 0.4745 nan 0.1000 -0.0019
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## 460 0.4407 nan 0.1000 -0.0005
## 480 0.4316 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2470 nan 0.1000 0.0162
## 2 1.2135 nan 0.1000 0.0144
## 3 1.1796 nan 0.1000 0.0130
## 4 1.1534 nan 0.1000 0.0127
## 5 1.1259 nan 0.1000 0.0107
## 6 1.1003 nan 0.1000 0.0122
## 7 1.0779 nan 0.1000 0.0089
## 8 1.0565 nan 0.1000 0.0076
## 9 1.0390 nan 0.1000 0.0083
## 10 1.0249 nan 0.1000 0.0046
## 20 0.9352 nan 0.1000 0.0019
## 40 0.8366 nan 0.1000 -0.0016
## 60 0.7923 nan 0.1000 -0.0009
## 80 0.7563 nan 0.1000 -0.0013
## 100 0.7291 nan 0.1000 -0.0007
## 120 0.7056 nan 0.1000 -0.0014
## 140 0.6851 nan 0.1000 -0.0013
## 160 0.6576 nan 0.1000 -0.0007
## 180 0.6414 nan 0.1000 -0.0013
## 200 0.6198 nan 0.1000 -0.0007
## 220 0.6030 nan 0.1000 -0.0007
## 240 0.5911 nan 0.1000 -0.0013
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## 280 0.5559 nan 0.1000 -0.0019
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## 320 0.5252 nan 0.1000 -0.0012
## 340 0.5135 nan 0.1000 -0.0008
## 360 0.5015 nan 0.1000 -0.0013
## 380 0.4903 nan 0.1000 -0.0011
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## 460 0.4433 nan 0.1000 -0.0008
## 480 0.4320 nan 0.1000 -0.0006
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2501 nan 0.1000 0.0137
## 2 1.2077 nan 0.1000 0.0186
## 3 1.1729 nan 0.1000 0.0159
## 4 1.1394 nan 0.1000 0.0127
## 5 1.1133 nan 0.1000 0.0119
## 6 1.0930 nan 0.1000 0.0084
## 7 1.0744 nan 0.1000 0.0088
## 8 1.0563 nan 0.1000 0.0073
## 9 1.0403 nan 0.1000 0.0051
## 10 1.0244 nan 0.1000 0.0059
## 20 0.9248 nan 0.1000 0.0021
## 40 0.8360 nan 0.1000 0.0007
## 60 0.7891 nan 0.1000 -0.0004
## 80 0.7508 nan 0.1000 0.0001
## 100 0.7294 nan 0.1000 -0.0009
## 120 0.7037 nan 0.1000 -0.0005
## 140 0.6828 nan 0.1000 -0.0015
## 160 0.6587 nan 0.1000 -0.0008
## 180 0.6376 nan 0.1000 -0.0003
## 200 0.6213 nan 0.1000 -0.0012
## 220 0.6053 nan 0.1000 -0.0021
## 240 0.5845 nan 0.1000 -0.0014
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## 280 0.5610 nan 0.1000 -0.0008
## 300 0.5481 nan 0.1000 -0.0003
## 320 0.5329 nan 0.1000 -0.0014
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## 380 0.4921 nan 0.1000 -0.0005
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## 460 0.4468 nan 0.1000 -0.0009
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2453 nan 0.1000 0.0202
## 2 1.2029 nan 0.1000 0.0185
## 3 1.1627 nan 0.1000 0.0169
## 4 1.1318 nan 0.1000 0.0123
## 5 1.1020 nan 0.1000 0.0133
## 6 1.0792 nan 0.1000 0.0075
## 7 1.0537 nan 0.1000 0.0101
## 8 1.0335 nan 0.1000 0.0075
## 9 1.0154 nan 0.1000 0.0074
## 10 0.9975 nan 0.1000 0.0066
## 20 0.8888 nan 0.1000 0.0001
## 40 0.7930 nan 0.1000 -0.0005
## 60 0.7375 nan 0.1000 -0.0015
## 80 0.6929 nan 0.1000 -0.0028
## 100 0.6567 nan 0.1000 -0.0021
## 120 0.6188 nan 0.1000 -0.0009
## 140 0.5859 nan 0.1000 -0.0010
## 160 0.5568 nan 0.1000 -0.0014
## 180 0.5348 nan 0.1000 -0.0017
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2343 nan 0.1000 0.0249
## 2 1.1899 nan 0.1000 0.0199
## 3 1.1527 nan 0.1000 0.0168
## 4 1.1228 nan 0.1000 0.0115
## 5 1.0949 nan 0.1000 0.0108
## 6 1.0709 nan 0.1000 0.0072
## 7 1.0472 nan 0.1000 0.0073
## 8 1.0312 nan 0.1000 0.0051
## 9 1.0138 nan 0.1000 0.0031
## 10 0.9964 nan 0.1000 0.0054
## 20 0.8973 nan 0.1000 0.0011
## 40 0.7976 nan 0.1000 -0.0029
## 60 0.7395 nan 0.1000 -0.0007
## 80 0.6956 nan 0.1000 -0.0014
## 100 0.6505 nan 0.1000 -0.0009
## 120 0.6175 nan 0.1000 -0.0017
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## 180 0.5348 nan 0.1000 -0.0019
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## 220 0.4910 nan 0.1000 -0.0003
## 240 0.4673 nan 0.1000 -0.0013
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## 280 0.4258 nan 0.1000 -0.0007
## 300 0.4053 nan 0.1000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2437 nan 0.1000 0.0240
## 2 1.2021 nan 0.1000 0.0186
## 3 1.1681 nan 0.1000 0.0117
## 4 1.1332 nan 0.1000 0.0111
## 5 1.1030 nan 0.1000 0.0117
## 6 1.0818 nan 0.1000 0.0077
## 7 1.0552 nan 0.1000 0.0112
## 8 1.0337 nan 0.1000 0.0079
## 9 1.0156 nan 0.1000 0.0047
## 10 1.0020 nan 0.1000 0.0026
## 20 0.8962 nan 0.1000 -0.0006
## 40 0.8014 nan 0.1000 0.0006
## 60 0.7438 nan 0.1000 -0.0012
## 80 0.6961 nan 0.1000 -0.0005
## 100 0.6560 nan 0.1000 -0.0006
## 120 0.6156 nan 0.1000 -0.0017
## 140 0.5785 nan 0.1000 -0.0006
## 160 0.5523 nan 0.1000 -0.0020
## 180 0.5266 nan 0.1000 -0.0008
## 200 0.5007 nan 0.1000 -0.0015
## 220 0.4756 nan 0.1000 -0.0011
## 240 0.4577 nan 0.1000 -0.0007
## 260 0.4353 nan 0.1000 -0.0002
## 280 0.4181 nan 0.1000 -0.0013
## 300 0.4031 nan 0.1000 -0.0014
## 320 0.3863 nan 0.1000 -0.0004
## 340 0.3685 nan 0.1000 -0.0007
## 360 0.3552 nan 0.1000 -0.0015
## 380 0.3386 nan 0.1000 -0.0001
## 400 0.3258 nan 0.1000 -0.0003
## 420 0.3135 nan 0.1000 -0.0007
## 440 0.3019 nan 0.1000 -0.0006
## 460 0.2879 nan 0.1000 -0.0005
## 480 0.2771 nan 0.1000 -0.0003
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2291 nan 0.2000 0.0307
## 2 1.1803 nan 0.2000 0.0242
## 3 1.1429 nan 0.2000 0.0170
## 4 1.1213 nan 0.2000 0.0085
## 5 1.0924 nan 0.2000 0.0110
## 6 1.0693 nan 0.2000 0.0064
## 7 1.0527 nan 0.2000 0.0039
## 8 1.0381 nan 0.2000 0.0042
## 9 1.0158 nan 0.2000 0.0105
## 10 1.0027 nan 0.2000 0.0049
## 20 0.9147 nan 0.2000 0.0030
## 40 0.8450 nan 0.2000 0.0003
## 60 0.8126 nan 0.2000 -0.0027
## 80 0.7917 nan 0.2000 -0.0018
## 100 0.7725 nan 0.2000 -0.0021
## 120 0.7583 nan 0.2000 -0.0009
## 140 0.7467 nan 0.2000 -0.0051
## 160 0.7325 nan 0.2000 -0.0012
## 180 0.7239 nan 0.2000 -0.0020
## 200 0.7160 nan 0.2000 -0.0025
## 220 0.7054 nan 0.2000 -0.0017
## 240 0.6973 nan 0.2000 -0.0020
## 260 0.6855 nan 0.2000 -0.0026
## 280 0.6734 nan 0.2000 -0.0006
## 300 0.6642 nan 0.2000 -0.0013
## 320 0.6578 nan 0.2000 -0.0005
## 340 0.6505 nan 0.2000 0.0002
## 360 0.6416 nan 0.2000 -0.0012
## 380 0.6353 nan 0.2000 -0.0006
## 400 0.6283 nan 0.2000 -0.0023
## 420 0.6233 nan 0.2000 -0.0027
## 440 0.6159 nan 0.2000 -0.0017
## 460 0.6108 nan 0.2000 -0.0011
## 480 0.6054 nan 0.2000 -0.0030
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2269 nan 0.2000 0.0281
## 2 1.1770 nan 0.2000 0.0178
## 3 1.1455 nan 0.2000 0.0087
## 4 1.1140 nan 0.2000 0.0121
## 5 1.0880 nan 0.2000 0.0120
## 6 1.0649 nan 0.2000 0.0105
## 7 1.0447 nan 0.2000 0.0086
## 8 1.0282 nan 0.2000 0.0087
## 9 1.0181 nan 0.2000 0.0006
## 10 1.0047 nan 0.2000 0.0049
## 20 0.9257 nan 0.2000 -0.0001
## 40 0.8506 nan 0.2000 -0.0026
## 60 0.8100 nan 0.2000 -0.0016
## 80 0.7851 nan 0.2000 -0.0015
## 100 0.7669 nan 0.2000 -0.0010
## 120 0.7520 nan 0.2000 -0.0004
## 140 0.7403 nan 0.2000 -0.0019
## 160 0.7238 nan 0.2000 -0.0012
## 180 0.7145 nan 0.2000 -0.0017
## 200 0.7076 nan 0.2000 -0.0018
## 220 0.6949 nan 0.2000 -0.0012
## 240 0.6835 nan 0.2000 -0.0015
## 260 0.6745 nan 0.2000 -0.0017
## 280 0.6652 nan 0.2000 -0.0023
## 300 0.6576 nan 0.2000 -0.0012
## 320 0.6525 nan 0.2000 -0.0033
## 340 0.6459 nan 0.2000 -0.0019
## 360 0.6399 nan 0.2000 -0.0013
## 380 0.6329 nan 0.2000 -0.0021
## 400 0.6255 nan 0.2000 -0.0016
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## 480 0.6045 nan 0.2000 -0.0020
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2282 nan 0.2000 0.0283
## 2 1.1790 nan 0.2000 0.0215
## 3 1.1374 nan 0.2000 0.0120
## 4 1.1090 nan 0.2000 0.0117
## 5 1.0852 nan 0.2000 0.0106
## 6 1.0605 nan 0.2000 0.0106
## 7 1.0432 nan 0.2000 0.0066
## 8 1.0369 nan 0.2000 0.0001
## 9 1.0243 nan 0.2000 0.0041
## 10 1.0096 nan 0.2000 0.0037
## 20 0.9222 nan 0.2000 -0.0018
## 40 0.8416 nan 0.2000 0.0003
## 60 0.8109 nan 0.2000 -0.0009
## 80 0.7915 nan 0.2000 -0.0032
## 100 0.7684 nan 0.2000 -0.0019
## 120 0.7542 nan 0.2000 -0.0037
## 140 0.7440 nan 0.2000 -0.0027
## 160 0.7293 nan 0.2000 -0.0022
## 180 0.7186 nan 0.2000 -0.0031
## 200 0.7080 nan 0.2000 -0.0018
## 220 0.6978 nan 0.2000 -0.0037
## 240 0.6916 nan 0.2000 -0.0014
## 260 0.6813 nan 0.2000 -0.0011
## 280 0.6725 nan 0.2000 -0.0005
## 300 0.6647 nan 0.2000 -0.0012
## 320 0.6581 nan 0.2000 -0.0021
## 340 0.6510 nan 0.2000 -0.0022
## 360 0.6445 nan 0.2000 -0.0012
## 380 0.6373 nan 0.2000 -0.0015
## 400 0.6337 nan 0.2000 -0.0014
## 420 0.6293 nan 0.2000 -0.0015
## 440 0.6224 nan 0.2000 -0.0029
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## 480 0.6133 nan 0.2000 -0.0012
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2144 nan 0.2000 0.0356
## 2 1.1519 nan 0.2000 0.0326
## 3 1.1108 nan 0.2000 0.0177
## 4 1.0759 nan 0.2000 0.0125
## 5 1.0438 nan 0.2000 0.0127
## 6 1.0163 nan 0.2000 0.0079
## 7 0.9921 nan 0.2000 0.0105
## 8 0.9698 nan 0.2000 0.0086
## 9 0.9517 nan 0.2000 0.0053
## 10 0.9373 nan 0.2000 0.0040
## 20 0.8506 nan 0.2000 -0.0016
## 40 0.7576 nan 0.2000 -0.0009
## 60 0.7038 nan 0.2000 -0.0026
## 80 0.6661 nan 0.2000 -0.0024
## 100 0.6384 nan 0.2000 -0.0025
## 120 inf nan 0.2000 nan
## 140 inf nan 0.2000 nan
## 160 inf nan 0.2000 nan
## 180 inf nan 0.2000 nan
## 200 inf nan 0.2000 nan
## 220 inf nan 0.2000 nan
## 240 inf nan 0.2000 nan
## 260 inf nan 0.2000 nan
## 280 inf nan 0.2000 nan
## 300 inf nan 0.2000 nan
## 320 inf nan 0.2000 nan
## 340 inf nan 0.2000 nan
## 360 inf nan 0.2000 nan
## 380 inf nan 0.2000 nan
## 400 inf nan 0.2000 nan
## 420 inf nan 0.2000 nan
## 440 inf nan 0.2000 nan
## 460 inf nan 0.2000 -0.0005
## 480 inf nan 0.2000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2105 nan 0.2000 0.0337
## 2 1.1650 nan 0.2000 0.0146
## 3 1.1212 nan 0.2000 0.0149
## 4 1.0782 nan 0.2000 0.0184
## 5 1.0362 nan 0.2000 0.0165
## 6 1.0035 nan 0.2000 0.0085
## 7 0.9821 nan 0.2000 0.0069
## 8 0.9658 nan 0.2000 0.0037
## 9 0.9465 nan 0.2000 0.0078
## 10 0.9337 nan 0.2000 0.0017
## 20 0.8491 nan 0.2000 -0.0004
## 40 0.7588 nan 0.2000 -0.0042
## 60 0.7005 nan 0.2000 -0.0010
## 80 0.6614 nan 0.2000 -0.0033
## 100 0.6220 nan 0.2000 -0.0034
## 120 0.5943 nan 0.2000 -0.0045
## 140 0.5673 nan 0.2000 -0.0022
## 160 0.5398 nan 0.2000 -0.0023
## 180 0.5184 nan 0.2000 -0.0015
## 200 0.4941 nan 0.2000 -0.0023
## 220 0.4734 nan 0.2000 -0.0017
## 240 0.4563 nan 0.2000 -0.0034
## 260 0.4376 nan 0.2000 -0.0005
## 280 0.4189 nan 0.2000 -0.0014
## 300 0.4011 nan 0.2000 -0.0018
## 320 0.3836 nan 0.2000 -0.0015
## 340 0.3680 nan 0.2000 -0.0009
## 360 0.3501 nan 0.2000 -0.0009
## 380 0.3361 nan 0.2000 -0.0015
## 400 0.3258 nan 0.2000 -0.0010
## 420 0.3146 nan 0.2000 -0.0015
## 440 0.3014 nan 0.2000 -0.0008
## 460 0.2824 nan 0.2000 -0.0018
## 480 0.2719 nan 0.2000 -0.0014
## 500 0.2629 nan 0.2000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2105 nan 0.2000 0.0339
## 2 1.1512 nan 0.2000 0.0228
## 3 1.1013 nan 0.2000 0.0260
## 4 1.0662 nan 0.2000 0.0123
## 5 1.0373 nan 0.2000 0.0106
## 6 1.0147 nan 0.2000 0.0088
## 7 0.9949 nan 0.2000 0.0037
## 8 0.9734 nan 0.2000 0.0028
## 9 0.9633 nan 0.2000 0.0007
## 10 0.9420 nan 0.2000 0.0085
## 20 0.8577 nan 0.2000 -0.0002
## 40 0.7715 nan 0.2000 -0.0033
## 60 0.7165 nan 0.2000 -0.0015
## 80 0.6665 nan 0.2000 -0.0021
## 100 0.6296 nan 0.2000 -0.0018
## 120 0.6005 nan 0.2000 -0.0016
## 140 0.5703 nan 0.2000 -0.0030
## 160 0.5465 nan 0.2000 -0.0031
## 180 0.5197 nan 0.2000 -0.0007
## 200 0.5004 nan 0.2000 -0.0013
## 220 0.4718 nan 0.2000 -0.0040
## 240 0.4540 nan 0.2000 -0.0013
## 260 0.4331 nan 0.2000 -0.0022
## 280 0.4122 nan 0.2000 -0.0007
## 300 0.3943 nan 0.2000 -0.0023
## 320 0.3788 nan 0.2000 -0.0025
## 340 0.3614 nan 0.2000 -0.0006
## 360 0.3445 nan 0.2000 -0.0035
## 380 0.3300 nan 0.2000 -0.0028
## 400 0.3169 nan 0.2000 -0.0010
## 420 0.3006 nan 0.2000 -0.0025
## 440 0.2895 nan 0.2000 -0.0021
## 460 0.2784 nan 0.2000 -0.0027
## 480 0.2663 nan 0.2000 -0.0004
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1905 nan 0.2000 0.0381
## 2 1.1121 nan 0.2000 0.0309
## 3 1.0615 nan 0.2000 0.0200
## 4 1.0282 nan 0.2000 0.0121
## 5 0.9988 nan 0.2000 0.0124
## 6 0.9723 nan 0.2000 0.0099
## 7 0.9450 nan 0.2000 0.0080
## 8 0.9196 nan 0.2000 0.0071
## 9 0.9033 nan 0.2000 0.0013
## 10 0.8889 nan 0.2000 0.0017
## 20 0.8008 nan 0.2000 -0.0008
## 40 0.7010 nan 0.2000 -0.0030
## 60 0.6082 nan 0.2000 -0.0033
## 80 0.5405 nan 0.2000 -0.0042
## 100 0.4939 nan 0.2000 -0.0026
## 120 0.4445 nan 0.2000 -0.0003
## 140 0.4113 nan 0.2000 -0.0015
## 160 0.3762 nan 0.2000 0.0000
## 180 0.3454 nan 0.2000 -0.0018
## 200 0.3169 nan 0.2000 -0.0021
## 220 0.2964 nan 0.2000 -0.0008
## 240 0.2747 nan 0.2000 -0.0002
## 260 0.2524 nan 0.2000 -0.0014
## 280 0.2354 nan 0.2000 -0.0009
## 300 0.2218 nan 0.2000 -0.0012
## 320 0.2043 nan 0.2000 -0.0013
## 340 0.1908 nan 0.2000 -0.0013
## 360 0.1798 nan 0.2000 -0.0014
## 380 0.1683 nan 0.2000 -0.0004
## 400 0.1582 nan 0.2000 -0.0005
## 420 0.1468 nan 0.2000 -0.0010
## 440 0.1380 nan 0.2000 -0.0013
## 460 0.1289 nan 0.2000 -0.0012
## 480 0.1211 nan 0.2000 -0.0004
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1951 nan 0.2000 0.0486
## 2 1.1185 nan 0.2000 0.0344
## 3 1.0683 nan 0.2000 0.0191
## 4 1.0322 nan 0.2000 0.0095
## 5 0.9970 nan 0.2000 0.0086
## 6 0.9681 nan 0.2000 0.0116
## 7 0.9470 nan 0.2000 0.0034
## 8 0.9252 nan 0.2000 0.0041
## 9 0.9112 nan 0.2000 -0.0000
## 10 0.9033 nan 0.2000 -0.0044
## 20 0.7925 nan 0.2000 0.0010
## 40 0.6873 nan 0.2000 -0.0045
## 60 0.6172 nan 0.2000 -0.0055
## 80 0.5596 nan 0.2000 -0.0012
## 100 0.5048 nan 0.2000 -0.0036
## 120 0.4620 nan 0.2000 -0.0013
## 140 0.4264 nan 0.2000 -0.0037
## 160 0.3964 nan 0.2000 -0.0018
## 180 0.3723 nan 0.2000 -0.0017
## 200 0.3427 nan 0.2000 -0.0010
## 220 0.3161 nan 0.2000 -0.0020
## 240 0.2911 nan 0.2000 -0.0020
## 260 0.2714 nan 0.2000 -0.0009
## 280 0.2506 nan 0.2000 -0.0026
## 300 0.2326 nan 0.2000 -0.0020
## 320 0.2157 nan 0.2000 -0.0012
## 340 0.2027 nan 0.2000 -0.0009
## 360 0.1899 nan 0.2000 -0.0018
## 380 0.1750 nan 0.2000 -0.0014
## 400 0.1633 nan 0.2000 -0.0004
## 420 0.1540 nan 0.2000 -0.0004
## 440 0.1430 nan 0.2000 -0.0003
## 460 0.1319 nan 0.2000 -0.0007
## 480 0.1245 nan 0.2000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1948 nan 0.2000 0.0448
## 2 1.1231 nan 0.2000 0.0307
## 3 1.0650 nan 0.2000 0.0231
## 4 1.0257 nan 0.2000 0.0122
## 5 0.9942 nan 0.2000 0.0126
## 6 0.9723 nan 0.2000 0.0053
## 7 0.9529 nan 0.2000 0.0077
## 8 0.9384 nan 0.2000 0.0017
## 9 0.9201 nan 0.2000 0.0049
## 10 0.9047 nan 0.2000 0.0019
## 20 0.8068 nan 0.2000 -0.0023
## 40 0.6881 nan 0.2000 0.0002
## 60 0.6281 nan 0.2000 -0.0007
## 80 0.5729 nan 0.2000 -0.0018
## 100 0.5196 nan 0.2000 -0.0023
## 120 0.4729 nan 0.2000 -0.0013
## 140 0.4342 nan 0.2000 -0.0008
## 160 0.4068 nan 0.2000 -0.0025
## 180 0.3750 nan 0.2000 -0.0021
## 200 0.3488 nan 0.2000 -0.0007
## 220 0.3213 nan 0.2000 -0.0026
## 240 0.2968 nan 0.2000 -0.0032
## 260 0.2711 nan 0.2000 -0.0006
## 280 0.2513 nan 0.2000 -0.0006
## 300 0.2334 nan 0.2000 -0.0003
## 320 0.2155 nan 0.2000 -0.0006
## 340 0.1983 nan 0.2000 -0.0006
## 360 0.1871 nan 0.2000 -0.0007
## 380 0.1772 nan 0.2000 -0.0008
## 400 0.1642 nan 0.2000 -0.0004
## 420 0.1561 nan 0.2000 -0.0009
## 440 0.1444 nan 0.2000 -0.0003
## 460 0.1371 nan 0.2000 -0.0007
## 480 0.1285 nan 0.2000 -0.0002
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2037 nan 0.3000 0.0323
## 2 1.1443 nan 0.3000 0.0269
## 3 1.1033 nan 0.3000 0.0160
## 4 1.0688 nan 0.3000 0.0125
## 5 1.0412 nan 0.3000 0.0083
## 6 1.0085 nan 0.3000 0.0137
## 7 0.9895 nan 0.3000 0.0081
## 8 0.9811 nan 0.3000 0.0020
## 9 0.9695 nan 0.3000 0.0013
## 10 0.9554 nan 0.3000 -0.0005
## 20 0.8809 nan 0.3000 -0.0039
## 40 0.8179 nan 0.3000 -0.0030
## 60 0.7808 nan 0.3000 -0.0016
## 80 0.7626 nan 0.3000 -0.0029
## 100 0.7418 nan 0.3000 -0.0049
## 120 0.7300 nan 0.3000 -0.0018
## 140 0.7113 nan 0.3000 -0.0035
## 160 0.7008 nan 0.3000 -0.0028
## 180 0.6816 nan 0.3000 -0.0017
## 200 0.6689 nan 0.3000 -0.0024
## 220 0.6660 nan 0.3000 -0.0037
## 240 0.6554 nan 0.3000 -0.0033
## 260 0.6485 nan 0.3000 -0.0062
## 280 0.6407 nan 0.3000 -0.0012
## 300 0.6311 nan 0.3000 -0.0055
## 320 0.6247 nan 0.3000 -0.0042
## 340 0.6181 nan 0.3000 -0.0046
## 360 0.6068 nan 0.3000 -0.0041
## 380 0.5982 nan 0.3000 -0.0057
## 400 0.5952 nan 0.3000 -0.0037
## 420 0.5853 nan 0.3000 -0.0026
## 440 0.5758 nan 0.3000 -0.0019
## 460 0.5698 nan 0.3000 -0.0010
## 480 0.5639 nan 0.3000 -0.0030
## 500 0.5581 nan 0.3000 -0.0020
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1961 nan 0.3000 0.0434
## 2 1.1558 nan 0.3000 0.0142
## 3 1.1101 nan 0.3000 0.0234
## 4 1.0753 nan 0.3000 0.0141
## 5 1.0486 nan 0.3000 0.0046
## 6 1.0204 nan 0.3000 0.0151
## 7 1.0093 nan 0.3000 -0.0006
## 8 0.9944 nan 0.3000 0.0057
## 9 0.9827 nan 0.3000 0.0020
## 10 0.9694 nan 0.3000 0.0039
## 20 0.8806 nan 0.3000 -0.0000
## 40 0.8202 nan 0.3000 -0.0000
## 60 0.7938 nan 0.3000 -0.0022
## 80 0.7751 nan 0.3000 -0.0046
## 100 0.7529 nan 0.3000 -0.0055
## 120 0.7317 nan 0.3000 -0.0025
## 140 0.7103 nan 0.3000 -0.0040
## 160 0.7004 nan 0.3000 -0.0023
## 180 0.6894 nan 0.3000 -0.0043
## 200 0.6789 nan 0.3000 -0.0011
## 220 0.6675 nan 0.3000 -0.0017
## 240 0.6557 nan 0.3000 -0.0033
## 260 0.6443 nan 0.3000 -0.0037
## 280 0.6321 nan 0.3000 -0.0023
## 300 0.6258 nan 0.3000 -0.0004
## 320 0.6141 nan 0.3000 -0.0016
## 340 0.6101 nan 0.3000 -0.0023
## 360 0.5968 nan 0.3000 -0.0024
## 380 0.5871 nan 0.3000 -0.0010
## 400 0.5800 nan 0.3000 -0.0026
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## 460 0.5612 nan 0.3000 -0.0016
## 480 0.5531 nan 0.3000 -0.0021
## 500 0.5451 nan 0.3000 -0.0034
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1997 nan 0.3000 0.0445
## 2 1.1381 nan 0.3000 0.0300
## 3 1.1002 nan 0.3000 0.0145
## 4 1.0661 nan 0.3000 0.0122
## 5 1.0402 nan 0.3000 0.0091
## 6 1.0088 nan 0.3000 0.0147
## 7 0.9897 nan 0.3000 0.0063
## 8 0.9747 nan 0.3000 0.0068
## 9 0.9628 nan 0.3000 -0.0008
## 10 0.9498 nan 0.3000 -0.0013
## 20 0.8786 nan 0.3000 -0.0008
## 40 0.8267 nan 0.3000 -0.0010
## 60 0.7979 nan 0.3000 -0.0026
## 80 0.7741 nan 0.3000 -0.0037
## 100 0.7568 nan 0.3000 -0.0023
## 120 0.7332 nan 0.3000 -0.0015
## 140 0.7134 nan 0.3000 -0.0058
## 160 0.6985 nan 0.3000 -0.0029
## 180 0.6884 nan 0.3000 -0.0042
## 200 0.6766 nan 0.3000 -0.0017
## 220 0.6652 nan 0.3000 -0.0028
## 240 0.6544 nan 0.3000 -0.0020
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## 280 0.6337 nan 0.3000 -0.0039
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## 340 0.6104 nan 0.3000 -0.0020
## 360 0.6048 nan 0.3000 -0.0032
## 380 0.5975 nan 0.3000 -0.0003
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1925 nan 0.3000 0.0519
## 2 1.1076 nan 0.3000 0.0335
## 3 1.0397 nan 0.3000 0.0257
## 4 1.0068 nan 0.3000 0.0055
## 5 0.9863 nan 0.3000 0.0061
## 6 0.9615 nan 0.3000 0.0078
## 7 0.9354 nan 0.3000 0.0099
## 8 0.9145 nan 0.3000 0.0045
## 9 0.9020 nan 0.3000 -0.0007
## 10 0.8886 nan 0.3000 -0.0029
## 20 0.8161 nan 0.3000 -0.0042
## 40 0.7235 nan 0.3000 0.0003
## 60 0.6612 nan 0.3000 -0.0046
## 80 0.6120 nan 0.3000 -0.0060
## 100 0.5730 nan 0.3000 -0.0030
## 120 0.5181 nan 0.3000 -0.0049
## 140 0.4842 nan 0.3000 -0.0034
## 160 0.4582 nan 0.3000 -0.0082
## 180 0.4243 nan 0.3000 -0.0031
## 200 0.3906 nan 0.3000 -0.0029
## 220 0.3652 nan 0.3000 -0.0030
## 240 0.3411 nan 0.3000 -0.0009
## 260 0.3138 nan 0.3000 -0.0010
## 280 0.2995 nan 0.3000 -0.0015
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## 320 0.2645 nan 0.3000 -0.0014
## 340 0.2543 nan 0.3000 -0.0024
## 360 0.2341 nan 0.3000 -0.0007
## 380 0.2212 nan 0.3000 -0.0014
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## 460 0.1804 nan 0.3000 -0.0019
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1784 nan 0.3000 0.0560
## 2 1.1007 nan 0.3000 0.0311
## 3 1.0620 nan 0.3000 0.0145
## 4 1.0232 nan 0.3000 0.0123
## 5 0.9859 nan 0.3000 0.0133
## 6 0.9686 nan 0.3000 -0.0019
## 7 0.9421 nan 0.3000 0.0041
## 8 0.9249 nan 0.3000 0.0043
## 9 0.9099 nan 0.3000 -0.0033
## 10 0.8981 nan 0.3000 -0.0003
## 20 0.8229 nan 0.3000 -0.0031
## 40 0.7314 nan 0.3000 -0.0007
## 60 0.6636 nan 0.3000 -0.0024
## 80 0.6142 nan 0.3000 -0.0034
## 100 0.5691 nan 0.3000 -0.0045
## 120 0.5304 nan 0.3000 -0.0035
## 140 0.4956 nan 0.3000 -0.0014
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## 180 0.4403 nan 0.3000 -0.0057
## 200 0.4093 nan 0.3000 -0.0064
## 220 0.3885 nan 0.3000 -0.0045
## 240 0.3586 nan 0.3000 -0.0013
## 260 0.3330 nan 0.3000 -0.0009
## 280 0.3137 nan 0.3000 -0.0037
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## 320 0.2778 nan 0.3000 -0.0025
## 340 0.2638 nan 0.3000 -0.0014
## 360 0.2457 nan 0.3000 -0.0015
## 380 0.2294 nan 0.3000 -0.0011
## 400 0.2176 nan 0.3000 -0.0012
## 420 0.2086 nan 0.3000 -0.0013
## 440 0.1934 nan 0.3000 -0.0013
## 460 0.1806 nan 0.3000 -0.0017
## 480 0.1736 nan 0.3000 -0.0012
## 500 0.1630 nan 0.3000 -0.0020
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1756 nan 0.3000 0.0472
## 2 1.0841 nan 0.3000 0.0474
## 3 1.0349 nan 0.3000 0.0157
## 4 1.0011 nan 0.3000 0.0105
## 5 0.9628 nan 0.3000 0.0094
## 6 0.9441 nan 0.3000 -0.0015
## 7 0.9243 nan 0.3000 0.0061
## 8 0.9100 nan 0.3000 -0.0016
## 9 0.8942 nan 0.3000 0.0046
## 10 0.8795 nan 0.3000 0.0030
## 20 0.8022 nan 0.3000 -0.0002
## 40 0.7296 nan 0.3000 -0.0048
## 60 0.6693 nan 0.3000 -0.0040
## 80 0.6075 nan 0.3000 -0.0027
## 100 0.5622 nan 0.3000 -0.0026
## 120 0.5185 nan 0.3000 -0.0029
## 140 0.4888 nan 0.3000 -0.0037
## 160 0.4561 nan 0.3000 -0.0061
## 180 0.4184 nan 0.3000 -0.0016
## 200 0.3929 nan 0.3000 -0.0032
## 220 0.3659 nan 0.3000 -0.0038
## 240 0.3389 nan 0.3000 -0.0027
## 260 0.3176 nan 0.3000 -0.0028
## 280 0.2968 nan 0.3000 -0.0007
## 300 0.2811 nan 0.3000 -0.0032
## 320 0.2684 nan 0.3000 -0.0010
## 340 0.2414 nan 0.3000 -0.0022
## 360 0.2253 nan 0.3000 -0.0012
## 380 0.2154 nan 0.3000 -0.0014
## 400 0.2009 nan 0.3000 -0.0022
## 420 0.1898 nan 0.3000 -0.0017
## 440 0.1775 nan 0.3000 -0.0007
## 460 0.1661 nan 0.3000 -0.0014
## 480 0.1559 nan 0.3000 -0.0009
## 500 0.1485 nan 0.3000 -0.0014
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1489 nan 0.3000 0.0575
## 2 1.0810 nan 0.3000 0.0150
## 3 1.0215 nan 0.3000 0.0221
## 4 0.9880 nan 0.3000 0.0085
## 5 0.9505 nan 0.3000 0.0080
## 6 0.9192 nan 0.3000 0.0047
## 7 0.8989 nan 0.3000 0.0020
## 8 0.8734 nan 0.3000 0.0072
## 9 0.8593 nan 0.3000 0.0020
## 10 0.8492 nan 0.3000 -0.0029
## 20 0.7518 nan 0.3000 -0.0026
## 40 0.6283 nan 0.3000 -0.0027
## 60 0.5543 nan 0.3000 -0.0044
## 80 0.4885 nan 0.3000 -0.0026
## 100 0.4353 nan 0.3000 -0.0027
## 120 0.4278 nan 0.3000 -0.0530
## 140 inf nan 0.3000 nan
## 160 inf nan 0.3000 nan
## 180 inf nan 0.3000 nan
## 200 inf nan 0.3000 nan
## 220 inf nan 0.3000 nan
## 240 inf nan 0.3000 nan
## 260 inf nan 0.3000 nan
## 280 inf nan 0.3000 nan
## 300 inf nan 0.3000 nan
## 320 inf nan 0.3000 nan
## 340 inf nan 0.3000 nan
## 360 inf nan 0.3000 nan
## 380 inf nan 0.3000 nan
## 400 inf nan 0.3000 nan
## 420 inf nan 0.3000 nan
## 440 inf nan 0.3000 nan
## 460 inf nan 0.3000 nan
## 480 inf nan 0.3000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1480 nan 0.3000 0.0449
## 2 1.0752 nan 0.3000 0.0341
## 3 1.0123 nan 0.3000 0.0295
## 4 0.9646 nan 0.3000 0.0154
## 5 0.9343 nan 0.3000 0.0064
## 6 0.9154 nan 0.3000 0.0012
## 7 0.8873 nan 0.3000 0.0031
## 8 0.8673 nan 0.3000 -0.0017
## 9 0.8517 nan 0.3000 0.0018
## 10 0.8324 nan 0.3000 0.0015
## 20 0.7484 nan 0.3000 -0.0057
## 40 0.6361 nan 0.3000 -0.0021
## 60 0.5412 nan 0.3000 -0.0047
## 80 0.4589 nan 0.3000 -0.0036
## 100 0.3912 nan 0.3000 -0.0015
## 120 0.3591 nan 0.3000 -0.0003
## 140 0.3153 nan 0.3000 -0.0010
## 160 0.2847 nan 0.3000 -0.0024
## 180 0.2522 nan 0.3000 -0.0030
## 200 0.2239 nan 0.3000 -0.0036
## 220 0.2033 nan 0.3000 -0.0014
## 240 0.1802 nan 0.3000 -0.0020
## 260 0.1620 nan 0.3000 -0.0028
## 280 0.1452 nan 0.3000 -0.0013
## 300 0.1339 nan 0.3000 -0.0017
## 320 0.1206 nan 0.3000 -0.0007
## 340 0.1084 nan 0.3000 -0.0008
## 360 0.0997 nan 0.3000 -0.0009
## 380 0.0920 nan 0.3000 -0.0007
## 400 0.0842 nan 0.3000 -0.0010
## 420 0.0756 nan 0.3000 -0.0003
## 440 0.0695 nan 0.3000 -0.0006
## 460 0.0626 nan 0.3000 -0.0006
## 480 0.0566 nan 0.3000 -0.0002
## 500 0.0521 nan 0.3000 -0.0007
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1639 nan 0.3000 0.0597
## 2 1.0858 nan 0.3000 0.0312
## 3 1.0292 nan 0.3000 0.0208
## 4 0.9839 nan 0.3000 0.0132
## 5 0.9534 nan 0.3000 0.0047
## 6 0.9225 nan 0.3000 0.0072
## 7 0.8959 nan 0.3000 0.0079
## 8 0.8830 nan 0.3000 -0.0067
## 9 0.8630 nan 0.3000 -0.0006
## 10 0.8449 nan 0.3000 0.0008
## 20 0.7561 nan 0.3000 -0.0088
## 40 0.6544 nan 0.3000 -0.0013
## 60 0.5660 nan 0.3000 -0.0031
## 80 0.4940 nan 0.3000 -0.0027
## 100 0.4311 nan 0.3000 -0.0019
## 120 0.3782 nan 0.3000 -0.0008
## 140 0.3432 nan 0.3000 -0.0035
## 160 0.3074 nan 0.3000 -0.0033
## 180 0.2691 nan 0.3000 0.0005
## 200 0.2425 nan 0.3000 -0.0016
## 220 0.2128 nan 0.3000 -0.0014
## 240 0.1894 nan 0.3000 -0.0013
## 260 0.1706 nan 0.3000 -0.0014
## 280 0.1544 nan 0.3000 -0.0018
## 300 0.1410 nan 0.3000 -0.0002
## 320 0.1275 nan 0.3000 -0.0017
## 340 0.1133 nan 0.3000 -0.0007
## 360 0.1022 nan 0.3000 -0.0010
## 380 0.0952 nan 0.3000 -0.0003
## 400 0.0851 nan 0.3000 -0.0010
## 420 0.0781 nan 0.3000 -0.0003
## 440 0.0717 nan 0.3000 -0.0005
## 460 0.0664 nan 0.3000 -0.0005
## 480 0.0618 nan 0.3000 -0.0004
## 500 0.0568 nan 0.3000 -0.0003
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1621 nan 0.5000 0.0526
## 2 1.0872 nan 0.5000 0.0250
## 3 1.0406 nan 0.5000 0.0138
## 4 1.0009 nan 0.5000 0.0161
## 5 0.9907 nan 0.5000 -0.0048
## 6 0.9686 nan 0.5000 0.0084
## 7 0.9571 nan 0.5000 0.0023
## 8 0.9432 nan 0.5000 0.0049
## 9 0.9251 nan 0.5000 0.0064
## 10 0.9151 nan 0.5000 -0.0014
## 20 0.8568 nan 0.5000 -0.0007
## 40 0.7981 nan 0.5000 -0.0000
## 60 0.7566 nan 0.5000 -0.0074
## 80 0.7211 nan 0.5000 -0.0033
## 100 0.7023 nan 0.5000 -0.0105
## 120 0.6826 nan 0.5000 -0.0068
## 140 0.6627 nan 0.5000 -0.0018
## 160 0.6358 nan 0.5000 -0.0005
## 180 0.6163 nan 0.5000 0.0014
## 200 0.6066 nan 0.5000 -0.0045
## 220 0.5973 nan 0.5000 -0.0039
## 240 0.5887 nan 0.5000 -0.0062
## 260 0.5762 nan 0.5000 -0.0065
## 280 0.5610 nan 0.5000 -0.0051
## 300 0.5380 nan 0.5000 -0.0040
## 320 0.5270 nan 0.5000 -0.0018
## 340 0.5166 nan 0.5000 -0.0039
## 360 0.5123 nan 0.5000 -0.0012
## 380 0.5128 nan 0.5000 -0.0061
## 400 0.4949 nan 0.5000 -0.0028
## 420 0.4869 nan 0.5000 -0.0027
## 440 0.4840 nan 0.5000 -0.0072
## 460 0.4714 nan 0.5000 -0.0017
## 480 0.4727 nan 0.5000 -0.0077
## 500 0.4588 nan 0.5000 -0.0028
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1616 nan 0.5000 0.0617
## 2 1.1096 nan 0.5000 0.0198
## 3 1.0570 nan 0.5000 0.0232
## 4 1.0099 nan 0.5000 0.0197
## 5 0.9853 nan 0.5000 0.0073
## 6 0.9590 nan 0.5000 0.0041
## 7 0.9422 nan 0.5000 0.0014
## 8 0.9308 nan 0.5000 -0.0061
## 9 0.9204 nan 0.5000 -0.0049
## 10 0.9079 nan 0.5000 -0.0007
## 20 0.8445 nan 0.5000 -0.0001
## 40 0.7878 nan 0.5000 -0.0052
## 60 0.7579 nan 0.5000 -0.0064
## 80 0.7323 nan 0.5000 -0.0114
## 100 0.7047 nan 0.5000 -0.0037
## 120 0.6922 nan 0.5000 -0.0059
## 140 0.6768 nan 0.5000 -0.0049
## 160 0.6687 nan 0.5000 -0.0100
## 180 0.6421 nan 0.5000 -0.0051
## 200 0.6374 nan 0.5000 -0.0108
## 220 0.6152 nan 0.5000 -0.0097
## 240 0.5998 nan 0.5000 -0.0012
## 260 0.5839 nan 0.5000 -0.0030
## 280 0.5871 nan 0.5000 -0.0092
## 300 0.5650 nan 0.5000 -0.0061
## 320 0.5595 nan 0.5000 -0.0043
## 340 0.5522 nan 0.5000 -0.0098
## 360 0.5388 nan 0.5000 -0.0041
## 380 0.5279 nan 0.5000 -0.0015
## 400 0.5244 nan 0.5000 -0.0036
## 420 0.5140 nan 0.5000 -0.0047
## 440 0.5125 nan 0.5000 -0.0027
## 460 0.5028 nan 0.5000 -0.0066
## 480 0.4993 nan 0.5000 -0.0070
## 500 0.4811 nan 0.5000 -0.0009
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1491 nan 0.5000 0.0674
## 2 1.0855 nan 0.5000 0.0276
## 3 1.0345 nan 0.5000 0.0131
## 4 0.9923 nan 0.5000 0.0189
## 5 0.9727 nan 0.5000 -0.0011
## 6 0.9558 nan 0.5000 -0.0027
## 7 0.9397 nan 0.5000 0.0036
## 8 0.9265 nan 0.5000 -0.0002
## 9 0.9153 nan 0.5000 0.0017
## 10 0.9081 nan 0.5000 -0.0042
## 20 0.8416 nan 0.5000 -0.0062
## 40 0.7958 nan 0.5000 -0.0057
## 60 0.7734 nan 0.5000 -0.0047
## 80 0.7368 nan 0.5000 -0.0014
## 100 0.7091 nan 0.5000 -0.0083
## 120 0.6922 nan 0.5000 -0.0092
## 140 0.6800 nan 0.5000 -0.0074
## 160 0.6556 nan 0.5000 -0.0039
## 180 0.6438 nan 0.5000 -0.0033
## 200 0.6192 nan 0.5000 -0.0048
## 220 0.6020 nan 0.5000 -0.0044
## 240 0.5837 nan 0.5000 -0.0015
## 260 0.5667 nan 0.5000 -0.0087
## 280 0.5639 nan 0.5000 -0.0053
## 300 0.5547 nan 0.5000 -0.0076
## 320 0.5412 nan 0.5000 -0.0014
## 340 0.5339 nan 0.5000 -0.0051
## 360 0.5188 nan 0.5000 -0.0022
## 380 0.5123 nan 0.5000 -0.0053
## 400 0.5062 nan 0.5000 -0.0057
## 420 0.4992 nan 0.5000 -0.0016
## 440 0.4960 nan 0.5000 -0.0033
## 460 0.4879 nan 0.5000 -0.0040
## 480 0.4822 nan 0.5000 -0.0080
## 500 0.4745 nan 0.5000 -0.0009
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1221 nan 0.5000 0.0846
## 2 1.0428 nan 0.5000 0.0314
## 3 0.9910 nan 0.5000 0.0195
## 4 0.9434 nan 0.5000 0.0151
## 5 0.9178 nan 0.5000 0.0067
## 6 0.9030 nan 0.5000 -0.0016
## 7 0.8784 nan 0.5000 -0.0025
## 8 0.8639 nan 0.5000 -0.0023
## 9 0.8535 nan 0.5000 -0.0052
## 10 0.8464 nan 0.5000 -0.0068
## 20 0.7804 nan 0.5000 -0.0043
## 40 0.7210 nan 0.5000 -0.0092
## 60 0.6204 nan 0.5000 -0.0157
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 nan
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 nan
## 280 inf nan 0.5000 nan
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
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## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1377 nan 0.5000 0.0864
## 2 1.0525 nan 0.5000 0.0347
## 3 0.9951 nan 0.5000 0.0167
## 4 0.9563 nan 0.5000 0.0003
## 5 0.9181 nan 0.5000 0.0090
## 6 0.8996 nan 0.5000 -0.0018
## 7 0.8749 nan 0.5000 0.0015
## 8 0.8616 nan 0.5000 -0.0022
## 9 0.8531 nan 0.5000 -0.0054
## 10 0.8339 nan 0.5000 0.0048
## 20 0.7750 nan 0.5000 0.0035
## 40 0.6588 nan 0.5000 -0.0092
## 60 0.5606 nan 0.5000 -0.0032
## 80 0.5075 nan 0.5000 -0.0099
## 100 0.4678 nan 0.5000 -0.0051
## 120 0.4165 nan 0.5000 -0.0051
## 140 0.3823 nan 0.5000 -0.0061
## 160 0.3345 nan 0.5000 -0.0066
## 180 0.3057 nan 0.5000 -0.0052
## 200 0.2787 nan 0.5000 -0.0030
## 220 0.2563 nan 0.5000 -0.0027
## 240 0.2221 nan 0.5000 -0.0045
## 260 0.1988 nan 0.5000 -0.0026
## 280 0.1825 nan 0.5000 -0.0016
## 300 0.1622 nan 0.5000 -0.0028
## 320 0.1489 nan 0.5000 -0.0016
## 340 0.1387 nan 0.5000 -0.0009
## 360 0.1273 nan 0.5000 -0.0035
## 380 0.1144 nan 0.5000 -0.0003
## 400 0.1072 nan 0.5000 -0.0022
## 420 0.0965 nan 0.5000 -0.0012
## 440 0.0903 nan 0.5000 -0.0024
## 460 0.0853 nan 0.5000 -0.0023
## 480 0.0791 nan 0.5000 -0.0020
## 500 0.0720 nan 0.5000 -0.0006
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1348 nan 0.5000 0.0780
## 2 1.0455 nan 0.5000 0.0160
## 3 0.9734 nan 0.5000 0.0275
## 4 0.9419 nan 0.5000 -0.0042
## 5 0.9274 nan 0.5000 -0.0024
## 6 0.9188 nan 0.5000 -0.0077
## 7 0.9051 nan 0.5000 -0.0066
## 8 0.8895 nan 0.5000 -0.0072
## 9 0.8791 nan 0.5000 -0.0085
## 10 0.8651 nan 0.5000 -0.0034
## 20 0.8122 nan 0.5000 -0.0113
## 40 0.7186 nan 0.5000 -0.0083
## 60 0.6103 nan 0.5000 -0.0097
## 80 0.4924 nan 0.5000 -0.0026
## 100 0.4337 nan 0.5000 -0.0003
## 120 0.3949 nan 0.5000 -0.0068
## 140 0.3549 nan 0.5000 -0.0022
## 160 0.3242 nan 0.5000 -0.0019
## 180 0.3026 nan 0.5000 -0.0063
## 200 0.2727 nan 0.5000 -0.0029
## 220 0.2470 nan 0.5000 0.0003
## 240 0.2219 nan 0.5000 -0.0012
## 260 0.1986 nan 0.5000 -0.0020
## 280 0.1876 nan 0.5000 -0.0056
## 300 0.1672 nan 0.5000 -0.0027
## 320 0.1573 nan 0.5000 -0.0025
## 340 0.1418 nan 0.5000 -0.0002
## 360 0.1330 nan 0.5000 -0.0019
## 380 0.1250 nan 0.5000 -0.0023
## 400 0.1152 nan 0.5000 -0.0015
## 420 0.1057 nan 0.5000 -0.0017
## 440 0.1009 nan 0.5000 -0.0005
## 460 0.0962 nan 0.5000 -0.0011
## 480 0.0847 nan 0.5000 -0.0010
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0929 nan 0.5000 0.1011
## 2 0.9986 nan 0.5000 0.0369
## 3 0.9478 nan 0.5000 0.0165
## 4 0.9031 nan 0.5000 0.0165
## 5 0.8731 nan 0.5000 0.0028
## 6 0.8623 nan 0.5000 -0.0130
## 7 0.8497 nan 0.5000 0.0004
## 8 0.8490 nan 0.5000 -0.0217
## 9 0.8344 nan 0.5000 -0.0066
## 10 0.8277 nan 0.5000 -0.0217
## 20 2.0521 nan 0.5000 -0.0061
## 40 1.9703 nan 0.5000 -0.0084
## 60 1.9966 nan 0.5000 -0.0035
## 80 inf nan 0.5000 nan
## 100 inf nan 0.5000 nan
## 120 inf nan 0.5000 nan
## 140 inf nan 0.5000 -0.0003
## 160 inf nan 0.5000 nan
## 180 inf nan 0.5000 nan
## 200 inf nan 0.5000 nan
## 220 inf nan 0.5000 nan
## 240 inf nan 0.5000 nan
## 260 inf nan 0.5000 -0.0018
## 280 inf nan 0.5000 -0.0021
## 300 inf nan 0.5000 nan
## 320 inf nan 0.5000 nan
## 340 inf nan 0.5000 nan
## 360 inf nan 0.5000 nan
## 380 inf nan 0.5000 nan
## 400 inf nan 0.5000 nan
## 420 inf nan 0.5000 nan
## 440 inf nan 0.5000 nan
## 460 inf nan 0.5000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0900 nan 0.5000 0.0780
## 2 0.9992 nan 0.5000 0.0272
## 3 0.9566 nan 0.5000 -0.0037
## 4 0.9180 nan 0.5000 -0.0033
## 5 0.8913 nan 0.5000 -0.0120
## 6 0.8562 nan 0.5000 0.0030
## 7 0.8299 nan 0.5000 0.0056
## 8 0.8093 nan 0.5000 -0.0042
## 9 0.8086 nan 0.5000 -0.0201
## 10 0.7909 nan 0.5000 -0.0148
## 20 0.6908 nan 0.5000 -0.0152
## 40 0.5607 nan 0.5000 -0.0031
## 60 0.4558 nan 0.5000 -0.0098
## 80 0.3762 nan 0.5000 -0.0005
## 100 0.2808 nan 0.5000 -0.0052
## 120 0.2458 nan 0.5000 -0.0036
## 140 0.2021 nan 0.5000 -0.0053
## 160 0.1688 nan 0.5000 -0.0017
## 180 0.1388 nan 0.5000 -0.0041
## 200 0.1198 nan 0.5000 -0.0023
## 220 0.1080 nan 0.5000 -0.0016
## 240 0.0955 nan 0.5000 -0.0017
## 260 0.0848 nan 0.5000 -0.0014
## 280 0.0722 nan 0.5000 -0.0009
## 300 0.0625 nan 0.5000 -0.0006
## 320 0.0534 nan 0.5000 -0.0006
## 340 0.0463 nan 0.5000 -0.0010
## 360 0.0426 nan 0.5000 -0.0010
## 380 0.0384 nan 0.5000 -0.0011
## 400 0.0342 nan 0.5000 -0.0005
## 420 0.0297 nan 0.5000 -0.0005
## 440 0.0262 nan 0.5000 -0.0005
## 460 0.0237 nan 0.5000 -0.0002
## 480 0.0205 nan 0.5000 -0.0001
## 500 0.0181 nan 0.5000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0956 nan 0.5000 0.0917
## 2 1.0129 nan 0.5000 0.0306
## 3 0.9560 nan 0.5000 -0.0006
## 4 0.9266 nan 0.5000 -0.0025
## 5 0.8937 nan 0.5000 0.0052
## 6 0.8676 nan 0.5000 0.0036
## 7 0.8592 nan 0.5000 -0.0140
## 8 0.8471 nan 0.5000 -0.0153
## 9 0.8305 nan 0.5000 0.0028
## 10 0.8211 nan 0.5000 -0.0107
## 20 0.7027 nan 0.5000 -0.0106
## 40 0.5544 nan 0.5000 -0.0136
## 60 0.4612 nan 0.5000 -0.0154
## 80 0.3854 nan 0.5000 -0.0065
## 100 0.3151 nan 0.5000 -0.0082
## 120 0.2629 nan 0.5000 -0.0042
## 140 0.2168 nan 0.5000 -0.0034
## 160 0.1790 nan 0.5000 -0.0026
## 180 0.1593 nan 0.5000 -0.0086
## 200 0.1214 nan 0.5000 -0.0007
## 220 0.1071 nan 0.5000 -0.0004
## 240 0.0919 nan 0.5000 -0.0017
## 260 0.0801 nan 0.5000 -0.0011
## 280 0.0704 nan 0.5000 -0.0014
## 300 0.0617 nan 0.5000 -0.0014
## 320 0.0541 nan 0.5000 -0.0011
## 340 0.0464 nan 0.5000 -0.0004
## 360 0.0407 nan 0.5000 -0.0013
## 380 0.0355 nan 0.5000 -0.0008
## 400 0.0325 nan 0.5000 -0.0007
## 420 0.0278 nan 0.5000 -0.0002
## 440 0.0242 nan 0.5000 -0.0001
## 460 0.0218 nan 0.5000 -0.0003
## 480 0.0189 nan 0.5000 -0.0001
## 500 0.0165 nan 0.5000 -0.0002
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1274 nan 1.0000 0.0668
## 2 1.0444 nan 1.0000 0.0243
## 3 0.9864 nan 1.0000 0.0314
## 4 0.9906 nan 1.0000 -0.0257
## 5 1.0041 nan 1.0000 -0.0395
## 6 0.9816 nan 1.0000 -0.0009
## 7 0.9592 nan 1.0000 -0.0018
## 8 0.9448 nan 1.0000 -0.0004
## 9 0.9162 nan 1.0000 -0.0065
## 10 0.9088 nan 1.0000 -0.0030
## 20 0.8341 nan 1.0000 -0.0115
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
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##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1225 nan 1.0000 0.0700
## 2 1.0639 nan 1.0000 0.0229
## 3 1.0269 nan 1.0000 0.0026
## 4 0.9816 nan 1.0000 0.0175
## 5 0.9747 nan 1.0000 -0.0151
## 6 0.9827 nan 1.0000 -0.0326
## 7 0.9457 nan 1.0000 0.0177
## 8 0.9485 nan 1.0000 -0.0239
## 9 0.9390 nan 1.0000 -0.0054
## 10 0.9301 nan 1.0000 -0.0039
## 20 0.9235 nan 1.0000 -0.0056
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 3522651.0612 nan 1.0000 -0.0300
## 140 3522651.0713 nan 1.0000 -0.0061
## 160 3522651.0424 nan 1.0000 -0.0043
## 180 3522651.0536 nan 1.0000 -0.0514
## 200 3522651.1591 nan 1.0000 0.0018
## 220 3522652.6901 nan 1.0000 0.0010
## 240 3522652.7094 nan 1.0000 -0.0003
## 260 3522674.2908 nan 1.0000 -0.0074
## 280 3522674.2368 nan 1.0000 -0.0145
## 300 3522674.2328 nan 1.0000 -0.0209
## 320 3522674.2388 nan 1.0000 -0.0187
## 340 3522674.2313 nan 1.0000 -0.0020
## 360 inf nan 1.0000 -inf
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.1284 nan 1.0000 0.0728
## 2 1.0595 nan 1.0000 0.0334
## 3 1.0313 nan 1.0000 -0.0043
## 4 0.9954 nan 1.0000 0.0063
## 5 0.9947 nan 1.0000 -0.0326
## 6 0.9895 nan 1.0000 -0.0098
## 7 0.9634 nan 1.0000 -0.0007
## 8 0.9561 nan 1.0000 -0.0199
## 9 0.9311 nan 1.0000 -0.0128
## 10 0.9263 nan 1.0000 -0.0143
## 20 0.8819 nan 1.0000 -0.0076
## 40 0.9553 nan 1.0000 -0.0296
## 60 24.4869 nan 1.0000 0.0017
## 80 24.4567 nan 1.0000 -0.0179
## 100 24.4407 nan 1.0000 -0.0007
## 120 24.4387 nan 1.0000 -0.0011
## 140 24.3075 nan 1.0000 0.0022
## 160 24.2928 nan 1.0000 -0.0046
## 180 24.2947 nan 1.0000 -0.0000
## 200 24.2603 nan 1.0000 0.0001
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0918 nan 1.0000 0.0904
## 2 0.9769 nan 1.0000 0.0383
## 3 0.9534 nan 1.0000 -0.0172
## 4 0.9738 nan 1.0000 -0.0464
## 5 0.9840 nan 1.0000 -0.0400
## 6 0.9531 nan 1.0000 -0.0196
## 7 0.9638 nan 1.0000 -0.0274
## 8 0.9717 nan 1.0000 -0.0551
## 9 0.9424 nan 1.0000 -0.0063
## 10 0.9516 nan 1.0000 -0.0389
## 20 0.9084 nan 1.0000 -0.0360
## 40 1.2785 nan 1.0000 -0.0399
## 60 1.4842 nan 1.0000 -0.0362
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0604 nan 1.0000 0.0983
## 2 0.9714 nan 1.0000 0.0148
## 3 0.9722 nan 1.0000 -0.0366
## 4 0.9438 nan 1.0000 -0.0062
## 5 0.9718 nan 1.0000 -0.0620
## 6 0.9398 nan 1.0000 0.0081
## 7 0.9712 nan 1.0000 -0.0351
## 8 inf nan 1.0000 -inf
## 9 inf nan 1.0000 nan
## 10 inf nan 1.0000 nan
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0810 nan 1.0000 0.0672
## 2 1.0184 nan 1.0000 -0.0102
## 3 0.9766 nan 1.0000 -0.0008
## 4 0.9792 nan 1.0000 -0.0383
## 5 0.9711 nan 1.0000 -0.0246
## 6 0.9772 nan 1.0000 -0.0400
## 7 0.9821 nan 1.0000 -0.0452
## 8 0.9332 nan 1.0000 0.0075
## 9 0.9356 nan 1.0000 -0.0366
## 10 0.9420 nan 1.0000 -0.0347
## 20 1.3683 nan 1.0000 -0.0343
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 -inf
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 nan nan 1.0000 nan
## 160 nan nan 1.0000 nan
## 180 nan nan 1.0000 nan
## 200 nan nan 1.0000 nan
## 220 nan nan 1.0000 nan
## 240 nan nan 1.0000 nan
## 260 nan nan 1.0000 nan
## 280 nan nan 1.0000 nan
## 300 nan nan 1.0000 nan
## 320 nan nan 1.0000 nan
## 340 nan nan 1.0000 nan
## 360 nan nan 1.0000 nan
## 380 nan nan 1.0000 nan
## 400 nan nan 1.0000 nan
## 420 nan nan 1.0000 nan
## 440 nan nan 1.0000 nan
## 460 nan nan 1.0000 nan
## 480 nan nan 1.0000 nan
## 500 nan nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0321 nan 1.0000 0.1085
## 2 0.9560 nan 1.0000 0.0085
## 3 0.9344 nan 1.0000 -0.0382
## 4 0.9312 nan 1.0000 -0.0421
## 5 0.9199 nan 1.0000 -0.0307
## 6 0.9506 nan 1.0000 -0.0686
## 7 0.9248 nan 1.0000 -0.0100
## 8 0.8766 nan 1.0000 0.0134
## 9 0.8583 nan 1.0000 -0.0122
## 10 0.8461 nan 1.0000 -0.0315
## 20 530.1265 nan 1.0000 -0.0271
## 40 844.6416 nan 1.0000 -0.0488
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0721 nan 1.0000 0.0754
## 2 0.9964 nan 1.0000 -0.0152
## 3 0.9344 nan 1.0000 0.0141
## 4 0.9175 nan 1.0000 -0.0228
## 5 0.9431 nan 1.0000 -0.0613
## 6 0.9363 nan 1.0000 -0.0245
## 7 0.8731 nan 1.0000 0.0139
## 8 0.8417 nan 1.0000 0.0025
## 9 0.8310 nan 1.0000 -0.0265
## 10 0.8105 nan 1.0000 -0.0161
## 20 0.7402 nan 1.0000 -0.0042
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.0655 nan 1.0000 0.0576
## 2 1.0120 nan 1.0000 -0.0149
## 3 0.9668 nan 1.0000 -0.0104
## 4 0.9670 nan 1.0000 -0.0357
## 5 0.9118 nan 1.0000 -0.0219
## 6 0.8692 nan 1.0000 0.0016
## 7 1.1822 nan 1.0000 -0.3038
## 8 inf nan 1.0000 -inf
## 9 inf nan 1.0000 nan
## 10 inf nan 1.0000 nan
## 20 inf nan 1.0000 nan
## 40 inf nan 1.0000 nan
## 60 inf nan 1.0000 nan
## 80 inf nan 1.0000 nan
## 100 inf nan 1.0000 nan
## 120 inf nan 1.0000 nan
## 140 inf nan 1.0000 nan
## 160 inf nan 1.0000 nan
## 180 inf nan 1.0000 nan
## 200 inf nan 1.0000 nan
## 220 inf nan 1.0000 nan
## 240 inf nan 1.0000 nan
## 260 inf nan 1.0000 nan
## 280 inf nan 1.0000 nan
## 300 inf nan 1.0000 nan
## 320 inf nan 1.0000 nan
## 340 inf nan 1.0000 nan
## 360 inf nan 1.0000 nan
## 380 inf nan 1.0000 nan
## 400 inf nan 1.0000 nan
## 420 inf nan 1.0000 nan
## 440 inf nan 1.0000 nan
## 460 inf nan 1.0000 nan
## 480 inf nan 1.0000 nan
## 500 inf nan 1.0000 nan
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 1.2568 nan 0.1000 0.0167
## 2 1.2269 nan 0.1000 0.0135
## 3 1.2008 nan 0.1000 0.0092
## 4 1.1803 nan 0.1000 0.0097
## 5 1.1633 nan 0.1000 0.0069
## 6 1.1467 nan 0.1000 0.0074
## 7 1.1288 nan 0.1000 0.0064
## 8 1.1172 nan 0.1000 0.0038
## 9 1.1032 nan 0.1000 0.0062
## 10 1.0917 nan 0.1000 0.0055
## 20 1.0039 nan 0.1000 0.0014
## 40 0.9210 nan 0.1000 0.0007
## 60 0.8850 nan 0.1000 -0.0016
## 80 0.8592 nan 0.1000 -0.0002
## 100 0.8412 nan 0.1000 -0.0003plot(fit.final3)
print(fit.final3)## Stochastic Gradient Boosting
##
## 768 samples
## 8 predictor
## 2 classes: 'neg', 'pos'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 692, 691, 691, 691, 691, 692, ...
## Resampling results across tuning parameters:
##
## shrinkage interaction.depth n.minobsinnode n.trees Accuracy
## 0.000 1 1 100 0.6510595
## 0.000 1 1 250 0.6510595
## 0.000 1 1 500 0.6510595
## 0.000 1 2 100 0.6510595
## 0.000 1 2 250 0.6510595
## 0.000 1 2 500 0.6510595
## 0.000 1 3 100 0.6510595
## 0.000 1 3 250 0.6510595
## 0.000 1 3 500 0.6510595
## 0.000 2 1 100 0.6510595
## 0.000 2 1 250 0.6510595
## 0.000 2 1 500 0.6510595
## 0.000 2 2 100 0.6510595
## 0.000 2 2 250 0.6510595
## 0.000 2 2 500 0.6510595
## 0.000 2 3 100 0.6510595
## 0.000 2 3 250 0.6510595
## 0.000 2 3 500 0.6510595
## 0.000 3 1 100 0.6510595
## 0.000 3 1 250 0.6510595
## 0.000 3 1 500 0.6510595
## 0.000 3 2 100 0.6510595
## 0.000 3 2 250 0.6510595
## 0.000 3 2 500 0.6510595
## 0.000 3 3 100 0.6510595
## 0.000 3 3 250 0.6510595
## 0.000 3 3 500 0.6510595
## 0.001 1 1 100 0.6510595
## 0.001 1 1 250 0.6510595
## 0.001 1 1 500 0.6519253
## 0.001 1 2 100 0.6510595
## 0.001 1 2 250 0.6510595
## 0.001 1 2 500 0.6519253
## 0.001 1 3 100 0.6510595
## 0.001 1 3 250 0.6510595
## 0.001 1 3 500 0.6514924
## 0.001 2 1 100 0.6510595
## 0.001 2 1 250 0.6510595
## 0.001 2 1 500 0.7174356
## 0.001 2 2 100 0.6510595
## 0.001 2 2 250 0.6510595
## 0.001 2 2 500 0.7196115
## 0.001 2 3 100 0.6510595
## 0.001 2 3 250 0.6510595
## 0.001 2 3 500 0.7178913
## 0.001 3 1 100 0.6510595
## 0.001 3 1 250 0.6510595
## 0.001 3 1 500 0.7317783
## 0.001 3 2 100 0.6510595
## 0.001 3 2 250 0.6510595
## 0.001 3 2 500 0.7330884
## 0.001 3 3 100 0.6510595
## 0.001 3 3 250 0.6510595
## 0.001 3 3 500 0.7317897
## 0.100 1 1 100 0.7682388
## 0.100 1 1 250 0.7613124
## 0.100 1 1 500 0.7587378
## 0.100 1 2 100 0.7630554
## 0.100 1 2 250 0.7565334
## 0.100 1 2 500 0.7600478
## 0.100 1 3 100 0.7643541
## 0.100 1 3 250 0.7652313
## 0.100 1 3 500 0.7591650
## 0.100 2 1 100 0.7638984
## 0.100 2 1 250 0.7600080
## 0.100 2 1 500 0.7526601
## 0.100 2 2 100 0.7652313
## 0.100 2 2 250 0.7674072
## 0.100 2 2 500 0.7595865
## 0.100 2 3 100 0.7587207
## 0.100 2 3 250 0.7539417
## 0.100 2 3 500 0.7526487
## 0.100 3 1 100 0.7513500
## 0.100 3 1 250 0.7474653
## 0.100 3 1 500 0.7539645
## 0.100 3 2 100 0.7652654
## 0.100 3 2 250 0.7492196
## 0.100 3 2 500 0.7461552
## 0.100 3 3 100 0.7621839
## 0.100 3 3 250 0.7543803
## 0.100 3 3 500 0.7439508
## 0.200 1 1 100 0.7617738
## 0.200 1 1 250 0.7583049
## 0.200 1 1 500 0.7570175
## 0.200 1 2 100 0.7582764
## 0.200 1 2 250 0.7626396
## 0.200 1 2 500 0.7535373
## 0.200 1 3 100 0.7586865
## 0.200 1 3 250 0.7600194
## 0.200 1 3 500 0.7491798
## 0.200 2 1 100 0.7578663
## 0.200 2 1 250 0.7435236
## 0.200 2 1 500 0.7370187
## 0.200 2 2 100 0.7526373
## 0.200 2 2 250 0.7518057
## 0.200 2 2 500 0.7431533
## 0.200 2 3 100 0.7574220
## 0.200 2 3 250 0.7531157
## 0.200 2 3 500 0.7396389
## 0.200 3 1 100 0.7574391
## 0.200 3 1 250 0.7435578
## 0.200 3 1 500 0.7461552
## 0.200 3 2 100 0.7535429
## 0.200 3 2 250 0.7426920
## 0.200 3 2 500 0.7404933
## 0.200 3 3 100 0.7530531
## 0.200 3 3 250 0.7535543
## 0.200 3 3 500 0.7474766
## 0.300 1 1 100 0.7552461
## 0.300 1 1 250 0.7561688
## 0.300 1 1 500 0.7413762
## 0.300 1 2 100 0.7608681
## 0.300 1 2 250 0.7505069
## 0.300 1 2 500 0.7392458
## 0.300 1 3 100 0.7538961
## 0.300 1 3 250 0.7478811
## 0.300 1 3 500 0.7409376
## 0.300 2 1 100 0.7496070
## 0.300 2 1 250 0.7335669
## 0.300 2 1 500 0.7374687
## 0.300 2 2 100 0.7465710
## 0.300 2 2 250 0.7431476
## 0.300 2 2 500 0.7275120
## 0.300 2 3 100 0.7491171
## 0.300 2 3 250 0.7434666
## 0.300 2 3 500 0.7257006
## 0.300 3 1 100 0.7426293
## 0.300 3 1 250 0.7270506
## 0.300 3 1 500 0.7227102
## 0.300 3 2 100 0.7464969
## 0.300 3 2 250 0.7352586
## 0.300 3 2 500 0.7300752
## 0.300 3 3 100 0.7465539
## 0.300 3 3 250 0.7283778
## 0.300 3 3 500 0.7357883
## 0.500 1 1 100 0.7495842
## 0.500 1 1 250 0.7439565
## 0.500 1 1 500 0.7266120
## 0.500 1 2 100 0.7470153
## 0.500 1 2 250 0.7392458
## 0.500 1 2 500 0.7313568
## 0.500 1 3 100 0.7491684
## 0.500 1 3 250 0.7366200
## 0.500 1 3 500 0.7231146
## 0.500 2 1 100 0.7366029
## 0.500 2 1 250 0.7222488
## 0.500 2 1 500 0.7253304
## 0.500 2 2 100 0.7318410
## 0.500 2 2 250 0.7122921
## 0.500 2 2 500 0.7083846
## 0.500 2 3 100 0.7348713
## 0.500 2 3 250 0.7309410
## 0.500 2 3 500 0.7183641
## 0.500 3 1 100 0.7274778
## 0.500 3 1 250 0.7253133
## 0.500 3 1 500 0.7205058
## 0.500 3 2 100 0.7248291
## 0.500 3 2 250 0.7322739
## 0.500 3 2 500 0.7235931
## 0.500 3 3 100 0.7235589
## 0.500 3 3 250 0.7353042
## 0.500 3 3 500 0.7365915
## 1.000 1 1 100 0.7283607
## 1.000 1 1 250 0.7252563
## 1.000 1 1 500 0.7048644
## 1.000 1 2 100 0.7210014
## 1.000 1 2 250 0.6988950
## 1.000 1 2 500 0.7122750
## 1.000 1 3 100 0.7348143
## 1.000 1 3 250 0.7278879
## 1.000 1 3 500 0.7205400
## 1.000 2 1 100 0.7009171
## 1.000 2 1 250 0.6814308
## 1.000 2 1 500 0.6810207
## 1.000 2 2 100 0.7061859
## 1.000 2 2 250 0.6866655
## 1.000 2 2 500 0.6650319
## 1.000 2 3 100 0.6931590
## 1.000 2 3 250 0.6713545
## 1.000 2 3 500 0.6401059
## 1.000 3 1 100 0.6870529
## 1.000 3 1 250 0.6302119
## 1.000 3 1 500 0.5989633
## 1.000 3 2 100 0.6797505
## 1.000 3 2 250 0.6223684
## 1.000 3 2 500 0.5586238
## 1.000 3 3 100 0.6953976
## 1.000 3 3 250 0.6454545
## 1.000 3 3 500 0.6144851
## Kappa
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.000000000
## 0.004390317
## 0.000000000
## 0.000000000
## 0.004978642
## 0.000000000
## 0.000000000
## 0.002917046
## 0.000000000
## 0.000000000
## 0.252321850
## 0.000000000
## 0.000000000
## 0.259578129
## 0.000000000
## 0.000000000
## 0.253945259
## 0.000000000
## 0.000000000
## 0.298769389
## 0.000000000
## 0.000000000
## 0.303930320
## 0.000000000
## 0.000000000
## 0.299331950
## 0.464360321
## 0.454745945
## 0.452141053
## 0.450092366
## 0.441678101
## 0.453812047
## 0.454786591
## 0.462739389
## 0.450789646
## 0.457166002
## 0.456687378
## 0.443288814
## 0.462619174
## 0.473775037
## 0.460954847
## 0.447015450
## 0.442777807
## 0.443257863
## 0.433250519
## 0.429626995
## 0.451450779
## 0.463344822
## 0.437025142
## 0.431338943
## 0.455951346
## 0.445797906
## 0.427159253
## 0.452072994
## 0.448267786
## 0.447277667
## 0.444753899
## 0.459462146
## 0.442820365
## 0.447341473
## 0.453977096
## 0.429263440
## 0.449160984
## 0.420734882
## 0.411697929
## 0.436905272
## 0.443001321
## 0.426431832
## 0.444679268
## 0.447809295
## 0.419444694
## 0.453687513
## 0.428878951
## 0.434882942
## 0.441584422
## 0.427116043
## 0.422852320
## 0.446400014
## 0.446262518
## 0.434894552
## 0.442555631
## 0.447901777
## 0.414274682
## 0.454343398
## 0.435488084
## 0.415785006
## 0.439227340
## 0.427344073
## 0.414843577
## 0.435570092
## 0.405110975
## 0.416055791
## 0.427139739
## 0.423569663
## 0.395293831
## 0.433302599
## 0.422428343
## 0.390076392
## 0.422988507
## 0.391125182
## 0.381730704
## 0.428721177
## 0.409240018
## 0.397521940
## 0.433454187
## 0.399000541
## 0.413708441
## 0.431597841
## 0.421064257
## 0.387124460
## 0.421206989
## 0.410982003
## 0.399048009
## 0.430993272
## 0.404826108
## 0.379864330
## 0.408068705
## 0.381646949
## 0.389810503
## 0.399556764
## 0.352537007
## 0.342796110
## 0.407429731
## 0.401992243
## 0.376600816
## 0.389859809
## 0.390010824
## 0.383709225
## 0.386357169
## 0.400844865
## 0.384741090
## 0.387685764
## 0.410572575
## 0.413865225
## 0.389312332
## 0.380412123
## 0.338448580
## 0.372647201
## 0.327124724
## 0.355411420
## 0.400172364
## 0.385065375
## 0.372467612
## 0.335446860
## 0.299474290
## 0.295327902
## 0.350465617
## 0.301533773
## 0.278394261
## 0.320298557
## 0.284257487
## 0.222276054
## 0.305742036
## 0.235065021
## 0.196306555
## 0.292246106
## 0.190793734
## 0.107683185
## 0.327450994
## 0.234090613
## 0.185768299
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were n.trees = 100,
## interaction.depth = 1, shrinkage = 0.1 and n.minobsinnode = 1.5.d) Compare Algorithms After Tuning
results <- resamples(list(LogReg=fit.final1, LDA=fit.final2, GBM=fit.final3))
summary(results)##
## Call:
## summary.resamples(object = results)
##
## Models: LogReg, LDA, GBM
## Number of resamples: 30
##
## Accuracy
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## LogReg 0.6883117 0.7532468 0.7843472 0.7773354 0.8000256 0.8441558 0
## LDA 0.6883117 0.7662338 0.7792208 0.7760367 0.8000256 0.8311688 0
## GBM 0.6883117 0.7508117 0.7712748 0.7682388 0.7922078 0.8441558 0
##
## Kappa
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## LogReg 0.2655008 0.4401342 0.4943168 0.4843861 0.5346588 0.6393443 0
## LDA 0.2786885 0.4425230 0.4897297 0.4802587 0.5346588 0.6057503 0
## GBM 0.2376238 0.4143031 0.4685583 0.4643603 0.5344289 0.6393443 0dotplot(results)
6. Finalize Model and Present Results
Once we have narrow down to a model that we believe can make accurate predictions on unseen data, we are ready to finalize it. Finalizing a model may involve sub-tasks such as:
- Using an optimal model tuned to make predictions on unseen data.
- Creating a standalone model using the tuned parameters
- Saving an optimal model to file for later use.
For this particular dataset, we will settle on using the SVM algorithm with a sigma value of 0.01.
6.a) Predictions on validation dataset
# Not applicable for this iteration of the project.6.b) Create standalone model on entire training dataset
# Not applicable for this iteration of the project.6.c) Save model for later use
# Not applicable for this iteration of the project.